Abadi M, Chu A, Goodfellow I, et al. (2016) Deep learning with differential privacy. ArXiv 1607, e00133.
Abadi S, Yan WX, Amar D, Mayrose I (2017) A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action. PLoS Comput Biol 13, e1005807.
Abeel T, Helleputte T, de Peer YV, et al. (2010) Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26(3), 392-398.
Aboutalib SS, Mohamed AA, Berg WA, et al. (2018) Deep learning to distinguish recalled but benign mammography images in breast cancer screening. Clin Cancer Res 24, 5902-599.
Agarwal V, Bell GW, Nam JW (2015) Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. 4, e05005.
Aggarwal N, Singh D (2021) Technology Assisted Farming: Implications of IoT and AI. IOP Conf Ser Mater Sci Eng 1022, e012080.
Agris PF (1996) The importance of being modified: roles of modified nucleosides and Mg2+ in RNA structure and function. Prog Nucleic Acid Res Mol Biol 53, 79-129.
Ahsani MR, Mohammadabadi MR, Shamsaddini MB (2010) Clostridium perfringens isolate typing by multiplex PCR. J Venom Anim Toxins Includ Trop Dis 16 (4), 573-578.
Akhtar N, Rasheed Z, Ramamurthy S, et al. (2010) MicroRNA-27b regulates the expression of matrix metalloproteinase 13 in human osteoarthritis chondrocytes. Arthritis Rheum 62(5), 1361-1371.
Alakwaa FM, Chaudhary K, Garmire LX (2018) Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data. J Proteome Res 17, 337-347
Albattah W, Javed A, Nawaz M, et al. (2022) Artificial Intelligence-Based Drone System for Multiclass Plant Disease Detection Using an Improved Efficient Convolutional Neural Network. Front Plant Sci 13, e808380.
Ali Q, Ahmar S, Sohail MA, et al. (2021) Research Advances and Applications of Biosensing Technology for the Diagnosis of Pathogens in Sustainable Agriculture. Environ Sci Pollut Res Int 28, 9002-9019.
Alipanahi B, Delong A, Weirauch MT, Frey BJ (2015) Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nature Biotechnol 33, 831–838.
Amin N, McGrath A, Chen YPP (2019) Evaluation of deep learning in non-coding RNA classification. Nat Mach Intell 1, 246-256.
Amiri Roudbar M, Abdollahi-Arpanahi R, Ayatollahi Mehrgardi A, et al. (2018). Estimation of the variance due to parent-of-origin effects for productive and reproductive traits in Lori-Bakhtiari sheep. Small Rumin Res 160, 95-102.
Amiri Roudbar M, Mohammadabadi MR, Mehrgardi AA, Abdollahi-Arpanahi A (2017) Estimates of variance components due to parent-of-origin effects for body weight in Iran-Black sheep. Small Rumin Res 149, 1-5
An J-Y, Meng FR, You ZH, et al. (2016) Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model. Protein Sci 25, 1825-1833.
Anderson S, Bankier AT, Barrell BG, et al. (1981) Sequence and organization of the human mitochondrial genome. Nature 290, 457-465.
Angermueller C, Pärnamaa T, Parts L, Stegle O (2016) Deep learning for computational biology. Mol Syst Biol 12(7), e 878.
Antonakoudis A, Barbosa R, Kotidis P, Kontoravdi C (2020) The era of big data: Genome-scale modelling meets machine learning. Comput Struct Biotechnol J 18, 3287-3300.
Arisdakessian CG, Poirion OB, Yunits B, et al. (2019) Deepimpute: an accurate, fast and scalable deep neural network method to impute single-cell rna-seq data. Genome Biol 20(1), e211.
Arshad MF, Burrai GP, Varcasia A, et al. (2024) The groundbreaking impact of digitalization and artificial intelligence in sheep farming. Res Vet Sci 170, e105197.
Asgari E, Mofrad M, Kobeissy F (2015) Continuous distributed representation of biological sequences for deep proteomics and genomics. PLoS ONE 10(11), e0141287.
Aslam B, Basit M, Nisar MA, et al. (2017) Proteomics: Technologies and their applications. J Chromatogr Sci 55(2), 182-196.
Avsec Z, Weilert M, Shrikumar A, et al. (2021) Base-resolution models of transcription factor binding reveal soft motif syntax. Nat Genet 53, 354-366.
Azencott CA (2018) Machine learning and genomics: precision medicine versus patient privacy. Philosophical Transactions of the Royal Society A: Math Phys Engin Sci 376, e20170350.
Baek J, Lee B, Kwon S, Yoon S (2018) LncRNAnet: Long non-coding RNA identification using deep learning. Bioinformatics 34(22), 3889-3897.
Bao J, Xie Q (2022) Artificial intelligence in animal farming: a systematic literature review. J Clean Prod 331, e12995.
Bar-Shira A, Panet A, Honigman A (1991) An RNA secondary structure juxtaposes two remote genetic signals for human t-cell leukemia virus type I RNA 3’-end processing. J Virol 65(10), 5165-5173.
Basciftci F, Gunduz KA (2019) Identification of acidosis disease in cattle using IoT. In: 4th International Conference on Computer Science and Engineering (UBMK). IEEE, pp. 58–62.
Beaulieu-Jones BK, Wu ZS, Williams C, et al. (2019) Privacy-preserving generative deep neural networks support clinical data sharing. Circ Cardiovasc Qual Outcomes 12(7), e005122.
Bedi P, Gole P (2021) Plant Disease Detection Using Hybrid Model Based on Convolutional Autoencoder and Convolutional Neural Network. Artif Intell Agric 5, 90-101.
Bejnordi BE, Veta M, van Diest PJ, et al. (2017) Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. J Am Med Assoc 318, 2199-2210.
Bell JT, Pai AA, Pickrell JK, et al. (2011) DNA methylation patterns associate with genetic and gene expression variation in hapmap cell lines. Genome Biol 12(1), R10.
Belokopytova P, Fishman V (2020) Predicting genome architecture: Challenges and solutions. Frontiers Genet 11, e 617202.
Ben Ayed R, Hanana M (2021) Artificial Intelligence to Improve the Food and Agriculture Sector. J Food Qual 2021, e5584754.
Bentley DR, Balasubramanian S, Swerdlow HP, et al. (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53-59.
Berckmans D (2017) General introduction to precision livestock farming. Anim Front 7, 6-11.
Berger MF, Mardis ER (2018) The emerging clinical relevance of genomics in cancer medicine. Nat Rev Clinic Oncol 15, 353-365.
Bersanelli M, Mosca E, Remondini D, et al. (2016) Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 17, S15.
Bhardwaj A, Kishore S, Pandey DK (2022) Artificial Intelligence in Biological Sciences. Life 12, e1430.
Bleidorn C (2016) Third generation sequencing: technology and its potential impact on evolutionary biodiversity research. Syst Biodivers 14, 1-8.
Bondi E, Oh H, Xu H, et al. (2019) Using Game Theory in Real Time in the Real World: A Conservation Case Study. Proc 18th Int Conf Autonomous Agents MultiAgent Syst, pp. 2336-2338.
Bonnel N, Marteau PF (2012) Lna: fast protein structural comparison using a laplacian characterization of tertiary structure. IEEE/ACM Trans. Comput Biol Bioinform 9, 1451-1458.
Braslavsky I, Hébert B, Kartalov E, Quake S (2003) Sequence information can be obtained from single DNA molecules. Proc Natl Acad Sci USA 100, 3960-3964.
Breschi A, Muñoz-Aguirre M, Wucher V, et al. (2020) A limited set of transcriptional programs define major histological types and provide the molecular basis for a cellular taxonomy of the human body. Genome Res 30(7), 1047-1059.
Bretschneider H, Gandhi S, Deshwar AG, et al. (2018) COSSMO: Predicting competitive alternative splice site selection using deep learning. Bioinformatics 34(13), i429-i437.
Brown PH, Tiley L, Cullen B (1991) Effect of RNA secondary structure on polyadenylation site selection. Genes Dev 5(7), 1277-1284.
Bucher P (1990) Weight matrix descriptions of four eukaryotic RNA polymerase II promoter elements derived from 502 unrelated promoter sequences. J Mol Biol 212, 563-578.
Buermans HP, den Dunnen JT (2014) Next generation sequencing technology: Advances and applications. Biochim Biophys Acta 1842(10), 1932-1941.
Callaway E (2020) It will change everything: DeepMind’s AI makes gigantic leap in solving protein structures. Nature 588, 203-204.
Camacho DM, Collins KM, Powers RK, et al. (2018) Next-generation machine learning for biological networks. Cell 173, 1581-1592.
Camargo AP, Sourkov V, Pereira GAG, Carazzolle MF (2020) RNAsamba: neural network-based assessment of the protein-coding potential of RNA sequences. NAR Genom Bioinform 2(1), lqz024.
Cao C, Liu F, Tan H, et al. (2018) Deep learning and its applications in biomedicine. Genom Proteom Bioinform 16, 17-32.
Caudai C, Salerno E, Zoppè M, et al. (2019a) Chromstruct 4: A python code to estimate the chromatin structure from hi-c data. IEEE/ACM Trans Comput Biol Bioinform 16, 1867-1878.
Caudai C, Salerno E, Zoppè M, Tonazzini A (2019b) Estimation of the spatial chromatin structure based on a multiresolution bead-chain model. IEEE/ACM Trans Comput Biol Bioinform 16, 550-559.
Cavill R, Keun HC, Holmes E, et al. (2009) Genetic algorithms for simultaneous variable and sample selection in metabonomics. Bioinformatics 25(1), 112-118.
Chai H, Zhou X, Zhang Z, et al. (2021) Integrating multi-omics data through deep learning for accurate cancer prognosis prediction. Computers in biology and medicine. 134, e104481.
Chang TW (1983) Binding of cells to matrixes of distinct antibodies coated on solid surface. J Immunol Methods 65, 217-223.
Chaudhary K, Poirion OB, Lu L, Garmire LX (2018) Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res 24, 1248–59.
Chaudhary K, Poirion OB, Lu L, Garmire LX (2018) Deep learning–based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res 24, 1248-1259.
Chen H, Engkvist O, Wang Y, et al. (2018) The rise of deep learning in drug discovery. Drug Discov Today 23(6), 1241-1250.
Chen J, Shao J (2000) Nearest neighbor imputation for survey data. J Off Stat 16(2), 113-131.
Chen K, Wei Z, Zhang Q, et al. (2019) Whistle: a high-accuracy map of the human n6-methyladenosine (m6a) epitranscriptome predicted using a machine learning approach. Nucleic Acids Res 47, e41.
Chen L, Xuan J, Riggins RB, et al. (2011) Identifying cancer biomarkers by network-constrained support vector machines. BMC Syst Biol 5, e161.
Cheng Y, Miura R, Tian B (2006) Prediction of mRNA polyadenylation sites by support vector machine. Bioinformatics 22(19), 2320-2325.
Ching T, Himmelstein DS, Beaulieu-Jones BK, et al. (2018) Opportunities and obstacles for deep learning in biology and medicine. J R Soc Interface 15(141), e20170387.
Chuai G, Ma H, Yan J, et al. (2018) DeepCRISPR: optimized crispr guide rna design by deep learning. Genome Biol 19, e80.
Cismondi F, Fialho AS, Vieira SM, et al. (2013) Missing data in medical databases: Impute, delete or classify? Artif Intell Med 58(1), 63-72.
Consortium IHGS (2004) Finishing the euchromatic sequence of the human genome. Nature 431, 931-945.
Cooper J, Noon M, Jones C, et al. (2013) Big data in life cycle assessment. J Ind Ecol 17(6), 796-799.
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20, 273-297.
Costanzo M, Baryshnikova A, Bellay J, et al. (2010) The genetic landscape of a cell. Science 327(5964), 425-431.
Costanzo M, VanderSluis B, Koch EN, et al. (2016) A global genetic interaction network maps a wiring diagram of cellular function. Science 353(6306), aaf1420,
Costello Z, Martin Garcia H (2018) A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data. Npj Syst Biol Appl 4, e19.
D’Agaro E, Rosa F, Akentieva NP (2021) New technology tools and life cycle analysis (LCA) applied to a sustainable livestock production. Eur J 5(3), 130-141.
Dao F-Y, Lv H, Yang Y, et al. (2020) Computational identification of n6-methyladenosine sites in multiple tissues of mammals. Comput Struct Biotechnol J 18, 1084-1091.
Das S, Deng X, Camphausen K, et al. (2019) An R package for multi-omic data driven prediction of synthetic lethality in cancers. Bioinformatics (Oxford, England) 35, 701-702.
de Montjoye Y-A, Farzanehfar A, Hendrickx J, Rocher L (2017) Solving artificial intelligence’s privacy problem. Field Action Sci Rep 2017, 80-83.
de Ridder D, de Ridder J, Reinders MJT (2013) Pattern recognition in bioinformatics. Brief Bioinform 14, 633-647.
De Souto MC, Jaskowiak PA, Costa IG (2015) Impact of missing data imputation methods on gene expression clustering and classification. BMC Bioinform 16, e64.
De Vries M, de Boer IJ (2010) Comparing environmental impacts for livestock products: a review of life cycle assessments. Livest Sci 128(1–3), 1-11.
Degroeve S, De Baets B, Van de Peer Y, Rouzé P (2002) Feature subset selection for splice site prediction. Boinformatics 18, 75-83.
Dekker J, Marti-Renom MA, Mirny LA (2013) Exploring the three-dimensional organization of genomes: interpreting chromatin interaction data. Nat Rev Genet 14, 390-403.
Devlin J, Chang M-W, Lee K, Toutanova K (2018) Pre-training of deep bidirectional transformers for language understanding. arXiv 1810, e04805.
Djebali S, Davis CA, Merkel A, et al. (2012) Landscape of transcription in human cells. Nature 489(7414), 101-108.
Doelman JC, Stehfest E, van Vuuren DP, et al. (2020) Afforestation for climate change mitigation: potentials, risks and trade-offs. Glob Change Biol 26(3), 1576-1591.
Dominissini D, Moshitch-Moshkovitz S, Schwartz S, et al. (2012) Topology of the human and mouse m6a RNA methylomes revealed by m6a-seq. Nature 485, 201-216.
Doncescu A, Tauzain B, Kabbaj N (2009) Machine learning applied to BRCA1 hereditary breast cancer data. In: 2009 International Conference on Advanced Information Networking and Applications Workshops. p. 942–947.
Došilovic FK, Brcic M, Hlupic N (2018) Explainable artificial intelligence: A survey. In 41st Int Conven Inform Commun Technol Elect Microelec (MIPRO). IEEE 2018, 0210–0215.
Dutta Majumder D, Ulrichs C, Majumder D, et al. (2007) Current Status and Future Trends of Nanoscale Technology and Its Impact on Modern Computing, Biology, Medicine and Agricultural Biotechnology. In Proc Int Conf Comput Theory Appl, Kolkata, pp. 563–572.
Eickholt J, Cheng J (2012) Predicting protein residue-residue contacts using deep networks and boosting. Bioinformatics 28(23), 3066-3072.
Eli-Chukwu CN (2019) Applications of Artificial Intelligence in Agriculture: A Review. Eng Technol Appl Sci Res 9, 4377-4383.
EPC (The ENCODE Project Consortium) (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57-74.
Eraslan G, Avsec Z, Gagneur J, Theis FJ (2019) Deep learning: new computational modelling techniques for genomics. Nat Rev Genet 20, 389-403.
Ernst J, Kellis M (2012) ChromHMM: automating chromatin-state discovery and characterization. Nat Methods 9, 215–6.
Ernst J, Kellis M (2015) Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues. Nature Biotechnology 33, 364-376.
Esteva A, Robicquet A, Ramsundar B, et al. (2019) A guide to deep learning in healthcare. Nat Med 25, 24-29.
Fang C, Shang Y, Xu D (2018) MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction. Proteins 86, 592-598.
Fang C, Shang Y, Xu D (2018) Prediction of protein backbone torsion angles using deep residual inception neural networks. IEEE/ACM Trans Comput Biol Bioinform 10, e1109.
FAO (2022) How to Feed the World in 2050: Global Agriculture Towards 2050. Available online: https://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf (accessed on 21 July 2022).
Faraggi E, Zhang T, Yang Y, et al. (2012) Spine x: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles. J Comput Chemy 33, 259-267.
Fickett JW, Tung CS (1992) Assessment of protein coding measures. Nucleic Acids Res 20, 6441-6450.
Fleischmann R, Adams MD, White O, et al. (1995) Whole-genome random sequencing and assembly of haemophilus influenzae rd. Science 269(5223), 496-512.
Fortelny N, Bock C (2020) Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data. Genome Biol 21, e190.
Frankish A, Daiekhans M, Ferreira M, et al. (2019) GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res 47(D1), D766-773.
Frith MC, Bailey TL, Kasukawa T, et al. (2006) Discrimination of Non-Protein-Coding Transcripts from Protein-Coding mRNA. RNA Biol 3(1), 40-48.
Frye M, Jaffrey SR, Pan T, et al. (2016) RNA modifications: What have we learned and where are we headed? Nat Rev Genet 17(6), 365-372.
Fudenberg G, Kelley DR, Pollard KS (2020) Predicting 3d genome folding from DNA sequence with akita. Nat Methods 17, 1111-1117.
Gao J, Yang Y, Zhou Y (2018) Grid-based prediction of torsion angle probabilities of protein backbone and its application to discrimination of protein intrinsic disorder regions and selection of model structures. BMC Bioinformatics 19, e29.
Garcia-Blanco M, Baraniak AP, Lasda EL (2004) Alternative splicing in disease and therapy. Nat Biotechnol 22, 535-546.
Garrow AG, Agnew A, Westhead DR (2005) Tmb-hunt: a web server to screen sequence sets for transmembrane beta-barrel proteins. Nucleic Acids Res 33, W188-W192.
Gautam C, Ravi V (2014) Evolving clustering based data imputation. In Int Conf Circuits Power Comput Technol (ICCPCT-2014). IEEE 2014, 1763-1769.
Ghahramani A, Watt FM, Luscombe NM (2018) Generative adversarial networks simulate gene expression and predict perturbations in single cells. BioRxiv 2018, e262501.
Ghotbaldini H, Mohammadabadi MR, Nezamabadi-pour H, et al. (2019) Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed. Acta Scientiarum Anim Sci 41, e45282.
Giannoulatou E, Park SH, Humphreys DT, Ho JW (2014) Verification and validation of bioinformatics software without a gold standard: a case study of bwa and bowtie. BMC Bioinformatics 15, S15.
Gilpin W, Huang Y, Forger DB (2020) Learning dynamics from large biological data sets: Machine learning meets systems biology. Curr Opin Syst Biol 22, 1-7.
Goffeau A, Barrell BG, Bussey H, et al. (1996) Life with 6000 Genes. Science 274, 546-567.
Gogichaeva NV, Williams T, Alterman M (2007) Maldi tof/tof tandem mass spectrometry as a new tool for amino acid analysis. J Am Soc Mass Spectrom 18(2), 279-284.
Gong L, Yu M, Jiang S, et al. (2021) Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN. Sensors 21, e4537.
Gong W, Kwak I-Y, Pota P, et al. (2018) Drimpute: imputing dropout events in single cell rna sequencing data. BMC Bioinformatics 19(1), e220.
Goodfellow I, Pouget-Abadie J, Mirza M, et al. (2014) Generative adversarial nets. ArXiv 1406, e2661.
Grapov D, Fahrmann J, Wanichthanarak K, Khoomrung S (2018) Rise of deep learning for genomic, proteomic, and metabolomic data integration in precision medicine. Omics 22, 630-136.
Greenbaum D, Sboner A, Mu XJ, Gerstein M (2011) Genomics and privacy: implications of the new reality of closed data for the field. PLoS Comput Biol 7, e1002278.
Gui H, Li M, Sham PC, Cherny SS (2011) Comparisons of seven algorithms for pathway analysis using the wtccc crohn’s disease dataset. BMC Res Notes 4, e386.
Gupta S, Chaudhary K, Kumar R, et al. (2016) Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine. Sci Rep 6, e23857.
Gusmao EG, Dieterich C, Zenke M, Costa IG (2014) Detection of active transcription factor binding sites with the combination of dnase hypersensitivity and histone modifications. Bioinformatics 30, 3143-3151.
Haenssle HA, Fink C, Schneiderbauer R, et al (2018) Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 29, 1836-1842.
Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intelligent Syst 24, 8-12.
Hamamoto R, Komatsu M, Takasawa K, et al. (2020) Epigenetics analysis and integrated analysis of multiomics data, including epigenetic data, using artificial intelligence in the era of precision medicine. Biomolecules 10, e62.
Hansen KD, Wu Z, Irizarry RA, Leek JT (2011) Sequencing technology does not eliminate biological variability. Nat Biotechnol 29, 572-573.
Hao J, Astle W, De Iorio M, Ebbels T (2012) Batman-an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a bayesian model. Bioinformatics 28, 2088-2090.
Haque F, Li J, Wu H, et al. (2013) Solid-state and biological nanopore for real-time sensing of single chemical and sequencing of DNA. Nano Today 81, 56-74.
Harfouche AL, Jacobson DA, Kainer D, et al. (2019) Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence. Trends Biotechnol 37, 1217-1235.
Harmanci A, Gerstein M (2018) Analysis of sensitive information leakage in functional genomics signal profiles through genomic deletions. Nat Commun 9, 1-10.
Heather J, Chain B (2016) The sequence of sequencers: The history of sequencing DNA. Genomics 107, 1–8.
Heintzman ND, Stuart RK, Hon G, el al. (2007) Distinct and predictive chromatin signatures of transcriptional promoters and enhancers in the human genome. Nat Genet 39, 311-318.
Heitjan DF (1997) Annotation: what can be done about missing data? approaches to imputation. Am J Public Health 87, 548-450.
Hejase H, Chan CY (2015) Improving drug sensitivity prediction using different types of data. Pharmacometrics Syst Pharmacol 4(2), e2.
Heje Grønbech C, Vording MF, Timshel PN, et al. (2020) scvae: Variational auto-encoders for single-cell gene expression data. Bioinformatics 36(16), 4415-4422
Hieter P, Boguski M (1997) Functional genomics: it’s all how you read it. Science 278(5338), 601-602.
Hill ST, Kuintzle R, Teegarden A, et al. (2018) A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential. Nucleic Acids Res 46, 8105-8113.
Hillenkamp F, Karas M, Beavis R, Chait B (1991) Matrix-assisted laser desorption/ionization mass spectrometry of biopolymers. Analytical chemistry 63(24), 1193A-1203A.
Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18, 1527-1554.
Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313, 504-507.
Hiranuma N, Lundberg SM, Lee SI (2019) AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification. Nucleic Acid Res 47, e58.
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9, 1735-1780.
Hoffman MM, Buske OJ, Wang J, et al. (2012) Unsupervised pattern discovery in human chromatin structure through genomic segmentation. Nat Methods 9, 473-476.
Holder LB, Haque MM, Skinner MK (2017) Machine learning for epigenetics and future medical applications. Epigenetics 12, 505-514.
Huang L, Liao L, Wu CH (2018) Completing sparse and disconnected protein-protein network by deep learning. BMC Bioinformatics 19, e103.
Huang S, Cai N, Pacheco PP, et al. (2018) Applications of support vector machine (svm) learning in cancer genomics. Cancer Genomics Proteomics 15, 41–51.
Huang S, Chaudhary K, Garmire LX (2017) More is better: recent progress in multiomics data integration methods. Front Genet 8, e84.
Husson F, Josse J (2013) Handling missing values in multiple factor analysis. Food Qual Prefer 30, 77-85.
Ideker T, Galitski T, Hood L (2001) A new approach to decoding life: systems biology. Annu Rev Genomics Hum Genet 2, 343-272.
Ionita-Laza I, McCallum K, Xu B, Buxbaum JD (2016) A spectral approach integrating functional genomic annotations for coding and noncoding variants. Nat Genet 48, 214-220.
Israelsen BW, Ahmed NR (2019) dave...i can assure you...that it’s going to be all right...a definition, case for, and survey of algorithmic assurances in humanautonomy trust relationships. ACM Comput Surv (CSUR) 51, 1-37.
Jacobson MP, Pincus DL, Rapp CS, et al. (2004) A hierarchical approach to all-atom protein loop prediction. Proteins 55(2), 351-367.
Jafari Ahmadabadi SAA, Askari-Hemmat H, Mohammadabadi M, et al. (2023) The effect of Cannabis seed on DLK1 gene expression in heart tissue of Kermani lambs. Agric Biotechnol J 15(1), 217-234.
Jaganathan K, Panagiotopoulou SK, McRae JF, et al. (2019) Predicting Splicing from Primary Sequence with Deep Learning. Cell 176, 535–548.e24.
Jha A, Gazzara MR, Barash Y (2017) Integrative deep models for alternative splicing. Bioinformatics 33(14), i274-i282.
Jha K, Doshi A, Patel P, Shah MA (2019) Comprehensive Review on Automation in Agriculture Using Artificial Intelligence. Artif Intell Agric 2, 1-12.
Joly Y, Feze IN, Song L, Knoppers BM (2017) Comparative approaches to genetic discrimination: chasing shadows? Trends Genet 33, 299-302.
Josse J, Husson F (2016) Missmda: a package for handling missing values in multivariate data analysis. J Stat Softw 70, 1-31.
Ju S, Lim H, Heo J (2019) Machine Learning Approaches for Crop Yield Prediction with MODIS and Weather Data. In Proceedings of the 40th Asian Conference on Remote Sensing, ACRS 2019: Progress of Remote Sensing Technology for Smart Future, Daejeon, Korea.
Jumper J, Evans R, Pritzel A, et al. (2021) Highly accurate protein structure prediction with alphafold. Nature 596, 583-589.
Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637.
Kahles A, Lehman KV, Toussaint SC, et al. (2018) Comprehensive analysis of alternative splicing across tumors from 8,705 patients. Cancer Cell 34(2), 211–224.e6.
Kalinin AA, Higgins GA, Reamaroon N, et al. (2018) Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 19(7), 629-650.
Kalkatawi M, Rangkuti F, Schramm M, et al. (2012) Dragon polya spotter: predictor of poly(a) motifs within human genomic DNA sequences. Bioinformatics 28(1), 127-129.
Kang YJ, Yang DC, Kong L, et al. (2017) CPC2: A fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res 45(W1), W12-W16.
Kaye J (2012) The tension between data sharing and the protection of privacy in genomics research. Ann Rev Genom Human Genet 13, 415-431.
Kelchtermans P, Bittremieux W, Grave KD, et al. (2013) Machine learning applications in proteomics research: How the past can boost the future. Proteomics 14, 353-366.
Kelley DR, Reshef YA, Bileschi M, et al. (2018) Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Res 28(5), 739-750.
Kelley DR, Snoek J, Rinn JL (2016) Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Research 26, 990-999.
Khodayari A, Maranas CD (2016) A genome-scale escherichia coli kinetic metabolic model k-E .coli457 satisfying flux data for multiple mutant strains. Nat Commun 7, e13806.
Khorshidi M, Mohammadabadi MR, Esmailizadeh AK, et al. (2019) Comparison of artificial neural network and regression models for prediction of body weight in Raini Cashmere goat. Iran J Appl Anim Sci 9 (3), 453-461.
Killoran N, Lee LJ, Delong A, et al. (2017) Generating and designing dna with deep generative models. arxiv 1712, e06148.
Kim H, Park H (2003) Protein secondary structure prediction based on an improved support vector machines approach. Protein Eng 16, 553-560.
Kim J, Tae D, Seok J (2020) A survey of missing data imputation using generative adversarial networks. In Int Conf AI Inform Commun (ICAIIC). IEEE 2020, 454–456.
Kim M, Gilley JE (2008) Artificial Neural Network Estimation of Soil Erosion and Nutrient Concentrations in Runoff from Land Application Areas. Comput Electron Agric 64, 268-275.
Kircher M, Witten DM, Jain P, et al. (2014) A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet 46, 310-315.
Kitano H (2002). Systems biology: a brief overview. Science 295, 1662-1664.
Klyushin D, Tymoshenko A (2021) Optimization of Drip Irrigation Systems Using Artificial Intelligence Methods for Sustainable Agriculture and Environment. Stud Comput Intell 912, 3-17.
Koh PW, Pierson E, Kundaje A (2017) Denoising genome-wide histone chip-seq with convolutional neural networks. Bioinformatics 33, 225-233.
Koike A, Takagi T (2004) Prediction of protein-protein interaction sites using support vector machines. Protein Eng Des Sel 17(2), 165-173.
Kong L, Zhang Y, Ye ZQ, et al. (2007) CPC: Assess the protein-coding potential of transcripts using sequence features and support vector machine. Nucleic Acids Res 35(W), W345-349.
Kramer MA (1991) Nonlinear principal component analysis using autoassociative neural networks. AIChE J 37, 233-243.
Kristensen LS, Andersen MS, Stagsted LVW, et al. (2019) The biogenesis, biology and characterization of circular RNAs. Nat Rev Genet 20(11), 675-691.
Lal A, Chiang ZD, Duarte FM, et al. (2021) Deep learning-based enhancement of epigenomics data with atacworks. Nat Commun 12, e1507.
Lander E, Linton LM, Birren B, et al. (2001) Initial sequencing and analysis of the human genome. Nature 409(6822), 860-921.
Landgrebe T, Paclík P, Tax DM, et al. (2004) Cost-basedmclassifier evaluation for imbalanced problems. In: Fred A, Caelli TM, Duin RPW, Campilho AC, de Ridder D (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin,
Lapuschkin S, Waldchen S, Binder A, et al. (2019) Unmasking clever hans predictors and assessing what machines really learn. Nat Commun 10, 1-8.
Lawlor B, Walsh P (2015) Engineering bioinformatics: building reliability, performance and productivity into bioinformatics software. Bioengineered 6, 193-203.
Lazar C, Meganck S, Taminau J, et al. (2013) Batch effect removal methods for microarray gene expression data integration: a survey. Briefings in Bioinformatics 14, 469-490.
Le NQK, Ho QT, Nguyen TTD, Ou YYA (2021) Transformer architecture based on bert and 2d convolutional neural network to identify dna enhancers from sequence information. Brief Bioinform 22(5), bbab005.
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521, 436-444.
LeCun Y, Buttou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceedings IEEE 86, 2278-2324.
Lehner B (2013) Genotype to phenotype: lessons from model organisms for human genetics. Nature reviews. Genetics 14, 168–78.
Leoni G, Le Pera L, Ferrè F, et al. (2011) Coding potential of the products of alternative splicing in human. Genome Biol 12(1), R9.
Leprevost FDV, Barbosa VC, Francisco EL, et al. (2014) On best practices in the development of bioinformatics software. Front Genet 5, e199.
Leung M, Delong A, Frey B (2017) Inference of the human polyadenylation code. Bioinformatics 34, 2889-2798.
Leung MK, Xiong HY, Lee LJ, Frey BJ (2014) Deep learning of the tissue-regulated splicing code. Bioinformatics 30(12), i121–i129.
Li A, Zhang J, Zhou Z. PLEK (2014) A tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme. BMC Bioinformatics 15(1), e311.
Li H, Giger ML, Huynh BQ, Antropova N (2017) Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms. J Med Imaging 4(4), e 041304.
Li VR, Zhang Z, Troyanskaya OG (2021) CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes. Bioinformatics 37, i342-348.
Li WV, Li JJ (2018) An accurate and robust imputation method scimpute for single cell rna-seq data. Nat Commun 2018;9(1), e997.
Li Y, Huang Ch, Ding L, et al. (2019) Deep learning in bioinformatics: Introduction, application, and perspective in the big data era. Methods 166, 4-21.
Liakos KG, Busato P, Moshou D, et al. (2018) Machine Learning in Agriculture: A Review. Sensors 18, e2674.
Libbrecht MW, Stafford Noble W (2015) Machine learning applications in genetics and genomics. Nat Rev Genet 16, 321-332.
Lin D, Bonora G, Yardimci GG, Noble WS (2019) Computational methods for analyzing and modeling genome structure and organization. Wiley Interdiscip Rev Syst Biol Med 11(1), e1435.
Linaza MT, Posada J, Bund J, et al. (2021) Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture. Agronomy 11, e1227.
Little R, Rubin D (1987) Statistical Analysis with Missing Data. John Wiley & Sons Publication.
Liu H, Han H, Li J, Wong L (2005) DNAFSMiner: a web-based software toolbox to recognize two types of functional sites in DNA sequences. Bioinformatics 21(5), 671-673.
Liu J, Gough J, Rost B (2006) Distinguishing protein-coding from non-coding RNAs through support vector machines. PLoS Genet 2(4), e29.
Liu X (2017) Deep recurrent neural network for protein function prediction from sequence. Arxiv 1701, e08318.
Louhimo R, Hautaniemi S (2011) CNAmet: an R package for integrating copy number, methylation and expression data. Bioinformatics 27, 887-888.
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for rna-seq data with deseq2. Genome Biol 15, e550.
Lowe R, Shirley N, Bleackley M, et al. (2017) Transcriptomics technologies. PLOS Computational Biology 13, e1005457.
Ma J, Yu MK, Fong S, et al. (2018) Using deep learning to model the hierarchical structure and function of a cell. Nat Methods 15, 290-298.
Machnicka MA, Milanowska K, Oglou OO, et al. (2013) Modomics: a database of RNA modification pathways–2013 update. Nucleic Acids Res 41, D262-D267.
Mahmud M, Kaiser MS, McGinnity TM, Hussain A (2021) Deep learning in mining biological data. Cognit Comput 2021, 1-33.
Mahto AK, Alam MA, Biswas R, et al. (2021) Short-Term Forecasting of Agriculture Commodities in Context of Indian Market for Sustainable Agriculture by Using the Artificial Neural Network. J Food Qual 2021, e9939906.
Malarvizhi MR, Thanamani AS (2012) K-nearest neighbor in missing data imputation. Int J Engin Res Develop 5, 5-7.
Malta TM, Sokolov A, Gentles AJ, et al. (2018) Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173(2), 338–354.e15.
Maniatis T, Tasic B (2002) Alternative pre-mRNA splicing and proteome expansion in metazoans. Nature 418, 236-243.
Marbaniang CN, Vogel J (2016). Emerging roles of RNA modifications in bacteria. Curr Opin Microbiol 30, 50-57.
Marchetti CF, Ugena L, Humplík JF, et al. (2019) A Novel Image-Based Screening Method to StudyWater-Deficit Response and Recovery of Barley Populations Using Canopy Dynamics Phenotyping and Simple Metabolite Profiling. Front Plant Sci 10, e1252.
Margulies M, Egholm M, Altman WE, et al. (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437, 376-380.
Martens D, Baesens B, Van Gestel T, Vanthienen J (2007) Comprehensible credit scoring models using rule extraction from support vector machines. Eur J Operation Res 183, 1466-1476.
Martin AR, Kanai M, Kamatani Y, et al. (2019) Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet 51(4), 584-591
Marx V (2013) The big challenges of big data. Nature 498, 255-260.
Masoudzadeh SH, Mohammadabadi MR, Khezri A, et al. (2020). Dlk1 gene expression in different Tissues of lamb. Iran J Appl Anim Sci 10, 669-677.
Mathlin J, Le Pera L, Colombo T (2020) A census and categorization method of epitranscriptomic marks. Int J Mol Sci 21(13), e4684.
Matias FI, Caraza-Harter MV, Endelman JB (2020) FIELDimageR: An R Package to Analyze Orthomosaic Images from Agricultural Field Trials. Plant Phenome J 3, e20005.
McCarthy J, Minsky M, Rochester N, Shannon CEA (2006) Proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine 27, 12-14.
McInnes L, Healy J, Melville J (2018) Umap: Uniform manifold approximation and projection for dimension reduction. ArXiv e1802.03426.
McIntyre LM, Lopiano KK, Morse AM, et al. (2011) RNA-seq: technical variability and sampling. BMC Genomics 12, e293.
Meyer KD, Saletore Y, Zumbo P, et al. (2012) Comprehensive analysis of mRNA methylation reveals enrichment in 3’ UTRs and near stop codons. Cell 149, 1635-1646.
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv 1301, e3781
Min S, Lee B, Yoon S (2016) Deep learning in bioinformatics. Briefings in Bioinformatics 18, 851-869.
Miotto R, Wang F, Wang S, et al. (2018) Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics 19, 1236-1246.
Mogili UR, Deepak BBVL (2018) Review on Application of Drone Systems in Precision Agriculture. Procedia Comput Sci 133, 502-509.
Mohamadipoor L, Mohammadabadi M, Amiri Z, et al. (2021) Signature selection analysis reveals candidate genes associated with production traits in Iranian sheep breeds. BMC Vet Res 17(1), 1-9.
Mohammadabadi M, Golkar A, Askari Hesni M (2023) The effect of fennel (Foeniculum vulgare) on insulin-like growth factor 1 gene expression in the rumen tissue of Kermani sheep. Agric Biotechnol J 15(4), 239-256.
Mohammadabadi M, Masoudzadeh SH, Khezri A, et al. (2021) Fennel (Foeniculum vulgare) seed powder increases Delta-Like Non-Canonical Notch Ligand 1 gene expression in testis, liver, and humeral muscle tissues of growing lambs. Heliyon 7(12), e08542.
Mohd NFN, Ahamed HMNH, Abdullah R, et al. (2022) A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction. Algorithms 15, e218.
Mollet I, Ben-Dov C,Felicio-Silva D, et al. (2010) Unconstrained mining of transcript data reveals increased alternative splicing complexity in the human transcriptome. Nucleic Acids Res 38(14), 4740-4754.
Montavon G, Samek W, Müller KR (2018) Methods for interpreting and understanding deep neural networks. Digit Signal Process 73, 1-15.
Morozova O, Marra MA (2008) Applications of next-generation sequencing technologies in functional genomics. Genomics 92(5), 255-264.
Muzio G, O’Bray L, Borgwardt K (2021) Biological network analysis with deep learning. Brief Bioinform 22, 1515-1530.
Myszczynska MA, Ojamies PN, Lacoste AMB, et al. (2020) Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol 16(8), 440-456.
Nagano T, Fraser P (2011) No-Nonsense Functions for Long Noncoding RNAs. Cell 145, 178-181.
Nagashima T, Yamaguhi K, Urakami K, et al. (2020) Japanese version of the cancer genome atlas, JCGA, established using fresh frozen tumors obtained from 5143 cancer patients. Cancer Sci 111(2), 687-699.
Nguyen SP, Li Z, Xu D, Shang Y (2017) New deep learning methods for protein loop modeling. IEEE/ACM Trans Comput Biol Bioinform 16, 596-606.
NIH (2020) The Cancer Genome Atlas - Cancer Genome – TCGA.
Noguera-Solano R, Ruiz-Gutiérrez R, Rodriguez-Caso JM (2013) Genome: twisting stories with DNA. Endeavour 37(4), 213–219.
Norman TM, Horlbeck MA, Replogle JM, et al. (2019) Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science 365, 786-793.
Nounou M, Nounou H, Meskin N, Datta A (2012) Wavelet-based multiscale filtering of genomic data. In IEEE/ACM Int Conf Adv Social Netw Anal Mining IEEE 2012, 804–809.
Nounou MN, Nounou HN, Mansouri M (2013) Model-based and model-free filtering of genomic data. Network Modeling Analysis in Health Informatics and Bioinformatics 2, 109-121.
Ostrovsky-Berman M, Frankel B, Polak P, Yaari G (2021) Immune2vec: Embedding b/t cell receptor sequences in RN, using natural language processing. Front Immunol 12, e680687.
Ozata DM, Gainetdinov I, Zoch A, et al. (2019) PIWI interacting RNAs: small RNAs with big functions. Nat Rev Genet 20, 89-108.
Paeng K, Hwang S, Park S, Kim MA (2017) Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support; Springer: Cham, Switzerland, ISBN 9783319675572, Volume 10553 LNCS, pp. 231–239.
Pandey DK, Chaudhary B (2016) Domestication-Driven Gossypium Profilin 1 (GhPRF1) Gene Transduces Early Flowering Phenotype in Tobacco by Spatial Alteration of Apical/Floral-Meristem Related Gene Expression. BMC Plant Biol 16, e201310.
Pandey DK, Chaudhary B (2021) Transcriptional Loss of Domestication-Driven Cytoskeletal GhPRF1 Gene Causes Defective Floral and Fiber Development in Cotton (Gossypium). Plant Mol Biol 107, 519-532.
Paraforos DS, Vassiliadis V, Kortenbruck D, et al. (2016) A Farm Management Information System Using Future Internet Technologies. IFAC-PapersOnLine 49, 324–329.
Park Y, Kellis M (2015) Deep learning for regulatory genomics. Nature Biotechnology 33, 825-826.
Partel V, Costa L, Ampatzidis Y (2021) Smart Tree Crop Sprayer Utilizing Sensor Fusion and Artificial Intelligence. Comput Electron Agric 191, e106556.
Patti G, Yanes Ó, Siuzdak G (2012) Innovation: Metabolomics: the apogee of the omics trilogy. Nat Rev Mol Cell Biol 13, 263-269.
Paulsen J, Sekelja M, Oldenburg AR, et al. (2017) Chrom3d: three-dimensional genome modeling from hi-c and nuclear lamin-genome contacts. Genome Biol 18(1), e21.
Pazouki E (2021) A Practical Surface Irrigation Design Based on Fuzzy Logic and Meta-Heuristic Algorithms. Agric. Water Manag 256, e107069.
Pellegrini M, Baglioni M, Geraci F (2016) Protein complex prediction for large protein protein interaction networks with the core&peel method. BMC Bioinformatics 17, 37-58.
Perez E, Capper D (2020) Invited review: Dna methylation-based classification of paediatric brain tumours. Neuropathol Appl Neurobiol 46, 28-47.
Petrazzini BO, Naya H, Lopez-Bello F, et al. (2021) Evaluation of different approaches for missing data imputation on features associated to genomic data. BioData Mining 14, 1–13.
Pian C, Zhang G, Chen Z, et al. (2016) LncRNApred: Classification of Long Non-Coding RNAs and Protein-Coding Transcripts by the Ensemble Algorithm with a New Hybrid Feature. PLOS ONE 11(5), e0154567.
Pomyen Y, Wanichthanarak K, Poungsombat P, et al. (2020) Deep metabolome: Applications of deep learning in metabolomics. Comput Struct Biotechnol J 18, 2818-2825.
Poplin R, Chang PC, Alexander D, et al. (2018) A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol 36, 983-987.
Pour Hamidi S, Mohammadabadi MR, Asadi Foozi M, Nezamabadi-pour H (2017) Prediction of breeding values for the milk production trait in Iranian Holstein cows applying artificial neural networks. J Livestock Sci Technol 5 (2), 53-61.
Qi H, Zhang H, Zhao Y, et al. (2021) Mvp: predicting pathogenicity of missense variants by deep learning. Nat Commun 12(1), e510.
Qin Q, Feng J (2017) Imputation for transcription factor binding predictions based on deep learning. PLoS Comput Biol 13, e1005403.
Quang D, Xie X, Dan Q (2016) a hybrid convolutional and recurrent deep neural network for quantifying the function of dna sequences. Nucleic Acids Res 44, e107.
Ragoza M, Hochuli J, Idrobo E, et al. (2017) Protein-ligand scoring with convolutional neural networks. J Chem Inf Model 57, 942-957.
Raissi M, Perdikaris P, Karniadakis E (2019) Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378, 686-707.
Rao RR, Makkithaya K (2017) Learning from a class imbalanced public health dataset: A cost-based comparison of classifier performance. Int J Elect Comput Engin 7, e2215.
Ravanbakhsh S, Liu P, Bjordahl TC, et al. (2015) Accurate, fully-automated NMR spectral profiling for metabolomics. PLOS ONE 10(5), e0124219.
Ravi D, Wong Ch, Deligianni F, et al. (2017) Deep learning for health informatics. IEEE J Biomed Health Inform 21(1), 4-21.
Razin A, Cedar H (1991) DNA methylation and gene expression. Microbiol Mol Biol Rev 55, 451-458.
Richardson S, Tseng GC, Sun W (2016) Statistical methods in integrative genomics. Annual Rev Stat Appl 3, 181-209.
Rifaioglu AS, Dog˘an T, Martin MJ, et al. (2019) Deepred: Automated protein function prediction with multi-task feed-forward deep neural networks. Sci Rep 9, e7344.
Rives A, Meier J, Sercu T, et al. (2021) Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc Natl Acad Sci USA 118(15), e2016239118.
Rokach L, Maimon O (2014) Data mining with decision trees - theory and applications. In: Series in Machine Perception and Artificial Intelligence (2nd Ed.), Pages 328.
Rosenblatt F (1958) The perceptron: A probabilistic model for information storage and organization in the brain. Psychol Rev 65, 386-408.
Rost B, Sander C (1993) Secondary structure prediction of all-helical proteins in two states. Protein Eng. 6, 831-836.
Saad OM, Chen Y (2020) Deep denoising autoencoder for seismic random noise attenuation. Geophysics 85, V367-V376.
Safaei SMH, Dadpasand M, Mohammadabadi M, et al. (2022) An Origanum majorana Leaf Diet Influences Myogenin Gene Expression, Performance, and Carcass Characteristics in Lambs. Animals 13(1), e14.
Salakhutdinov R, Hinton G (2009) Deep Boltzmann machines. Proc 20th Int Conf Artif Intell Stat, PMLR 5, 448-455.
Salamov A, Solovyev V (1997) Recognition of 3’-processing sites of human mRNA precursors. Comput Appl Biosci 13(1), 23-28.
Salekin S, Mostavi M, Chiu Y, et al. (2020) Predicting sites of epitranscriptome modifications using unsupervised representation learning based on generative adversarial networks. Front Phys 8, e196.
Saletore Y, Meyer K, Korlach J, et al. (2012) The birth of the Epitranscriptome: deciphering the function of RNA modifications. Genome Biol 13(10), e175.
Salovska B, Zhu H, Gandhi T, et al. (2020) Isoform-resolved correlation analysis between mRNA abundance regulation and protein level degradation. Mol Syst Biol 16(3), e9170.
Samek W, Müller KR (2019) Towards explainable artificial intelligence. In Explainable AI: interpreting, explaining and visualizing deep learning. Springer, 5-22.
Sánchez-Vega F, Mina M, Armenia J, et al. (2018) Oncogenic signaling pathways in the cancer genome atlas. Cell 173, 321-37.
Sanger F, Air GM, Barrell BG, et al. (1977) Nucleotide sequence of bacteriophage phi x174 DNA. Nature 265, 687-695.
Sanger F, Nicklen S, Coulson A (1997) DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci USA 74(12), 5463-5467.
Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary dna microarray. Science 270, 467-470.
Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw 61, 85-117.
Schneider EA, Hugo WA (2017) Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts. BMC Genomics 18, e804,
Scholten MT, De Boer I, Gremmen B, Lokhorst C (2013) Livestock farming with care: towards sustainable production of animal-source food. NJAS: Wagening J Life Sci 66(1), 3-5.
Schwessinger R, Gosden M, Downes D, et al. (2020) Deepc: predicting 3d genome folding using megabasescale transfer learning. Nat Methods 17, 1118-1124.
Seemann T (2013) Ten recommendations for creating usable bioinformatics command line software. Gigascience 2(1), e15.
Segal E, Fondufe-Mittendorf Y, Chen L, et al. (2006) A genomic code for nucleosome positioning. Nature 442, 772-778.
Senior AW, Evans R, Jumper J, et al. (2020) Improved protein structure prediction using potentials from deep learning. Nature 577, 706-710.
Serra F, Bau D, Goodstadt M et al. (2017) Automatic analysis and 3d-modelling of hi-c data using tadbit reveals structural features of the fly chromatin colors. PLOS Comput Biol 13(7), e1005665.
Seyhan K, Nguyen TN, Akleylek S, et al. (2021) Bi-GISIS KE: Modified Key Exchange Protocol with Reusable Keys for IoT Security. J Inf Secur Appl 58, e102788.
Shah G, Shah A, Shah M (2019) Panacea of Challenges in Real-World Application of Big Data Analytics in Healthcare Sector. J Data Inf Manag 1, 107-116.
Sharma R, Kumar N, Sharma BB (2022) Applications of Artificial Intelligence in Smart Agriculture: A Review. In Recent Innovations in Computing. Lecture Notes in Electrical Engineering; Springer Science and Business Media Deutschland GmbH: Berlin/Heidelberg, Germany, Volume 832, pp. 135–142.
Shen R, Olshen AB, Ladanyi M (2009) Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906-2912.
Shen S, Park JW, Huang J, et al. (2012) MATS: A Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data. Nucleic Acids Res 40(8), e61
Shen Z, Bao W, Huang DS (2018) Recurrent neural network for predicting transcription factor binding sites. Sci Rep 8(1), e15270.
Shokri S, Khezri A, Mohammadabadi M, Kheyrodin H (2023) The expression of MYH7 gene in femur, humeral muscle and back muscle tissues of fattening lambs of the Kermani breed. Agric Biotechnol J 15(2), 217-236.
Sidore C, Busonero F, Maschio A, et al. (2015) Genome sequencing elucidates sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nat Genet 47(11), 1272-1281.
Silver D, Huang A, Maddison CJ, et al. (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529, 484-489.
Sønderby SK, Winther O (2015) Protein secondary structure prediction with long short term memory networks. Arxiv e1412.7828.
Song B, Li Z, Lin X, et al. (2021) Pretraining model for biological sequence data. Brief Funct Genomics 20(3), 181-195.
Sonsare PM, Gunavathi C (2019) Investigation of machine learning techniques on proteomics: A comprehensive survey. Prog Biophys Mol Biol 149, 54-69.
Spanaki K, Karafili E, Sivarajah U, et al. (2022) Artificial Intelligence and Food Security: Swarm Intelligence of AgriTech Drones for Smart AgriFood Operations. Prod Plan Control 33(16), 1498-1516.
Spencer M, Eickholt J, Cheng J (2015) A deep learning network approach to ab initio protein secondary structure prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics 12, 103-112.
Stark R, Grzelak M, Hadfield J (2019) RNA sequencing: the teenage years. Nat Rev Genet 20(11), 631-656.
Stathias V, Turner J, Koleti A, et al. (2020) Lincs data portal 2.0: next generation access point for perturbation-response signatures. Nucleic Acids Res 48(D1), D431-D439.
Stormo GD, Schneider TD, Gold L, Ehrenfeucht A (1982) Use of the ‘perceptron’ algorithm to distinguish translational initiation sites in E. coli. Nucleic Acids Research 10, 2997-3011.
Strombom D, King AJ (2018) Robot collection and transport of objects: a biomimetic process. Front Robot AI 5, e48.
Stueve TR, Marconett CN, Zhou B, et al. (2016) The importance of detailed epigenomic profiling of different cell types within organs. Epigenomics 8, 817-829.
Swan AL, Mobasheri A, Allaway D, Liddell S, Bacardit J (2013) Application of machine learning to proteomics data: Classification and biomarker identification in postgenomics biology. OMICS 17, 595–610.
Sweeney L (2002) k-anonymity: A model for protecting privacy. Int J Uncertain Fuzz Knowledge-Based Syst 10, 557-570.
Sweeney L, Abu A, Winn J (2013) Identifying participants in the personal genome project by name (a re-identification experiment). ArXiv 1304, e7605.
Szappanos B, Kovács K, Szamecz B, et al. (2011) An integrated approach to characterize genetic interaction networks in yeast metabolism. Nat Genet 43, 656-662.
Tabaska JE, Zhang M (1999) Detection of polyadenylation signals in human DNA sequences. Gene 231(1–2), 77-86.
Talavera D, Kershaw CJ, Costello JL, et al. (2018) Archetypal transcriptional blocks underpin yeast gene regulation in response to changes in growth conditions. Sci Rep 8(1), e7949.
Talaviya T, Shah D, Patel N, et al. (2020) Implementation of Artificial Intelligence in Agriculture for Optimisation of Irrigation and Application of Pesticides and Herbicides. Artif Intell Agric 4, 58-73.
TCGA 2020. The Cancer Genome Atlas Program. Cancer Genome. National Cancer Institute, USA.
Thornton PK (210) Livestock production: recent trends, future prospects. Philos Trans R Soc B: Biol Sci 365(1554), 2853-2867.
van Agthoven MA, Lam YPY, O’Connor P, et al. (2019) Two dimensional mass spectrometry: new perspectives for tandem mass spectrometry. European Biophys J 48, 213-229.
van Buuren S (2018) Flexible imputation of missing data. CRC Press.
van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Machine Learning Res 9, 2579-2605.
van Hulse J, Khoshgoftaar TM, Napolitano A, Wald R (2012) Threshold-based feature selection techniques for high-dimensional bioinformatics data. Netw Model Anal Health Inform Bioinform 1, 47-61.
van IJzendoorn DG, Szuhai K, Brujin IHB, et al. (2019) Machine learning analysis of gene expression data reveals novel diagnostic and prognostic biomarkers and identifies therapeutic targets for soft tissue sarcomas. PLoS Comput Biol 15(2), e1006826.
Vaswani A, Shazeer N, Parmar N, et al. (2017) Attention is all you need. ArXivabs 1706, e03762.
Velculescu VE, Zhang L, Vogelstein B, Kinzler KW (1995) Serial Analysis of Gene Expression. Science 270, 484-487.
Velculescu VE, Zhang L, Zhou W, et al. (1997) Characterization of the Yeast Transcriptome. Cell 88, 243-251.
Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proc 25th Int Conf Machine Learn 1, 1096-1103.
Vincent P, Larochelle H, Lajoie I, et al. (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11, 3371-3408.
Vlahou A, Schorge JO, Gregory BW, Coleman RL (2003) Diagnosis of ovarian cancer using decision tree classification of mass spectral data. J Biomed Biotechnol 2003, 308-314.
Voillet V, Besse P, Liaubet L, et al. (2016) Handling missing rows in multi-omics data integration: multiple imputation in multiple factor analysis framework. BMC Bioinformatics 17, 1-16.
Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep Learning for Computer Vision: A Brief Review. Comput Intell Neurosci 2018, e7068349.
Wainberg M, Merico D, Delong A, Frey BJ (2018) Deep learning in biomedicine. Nat Biotechnol 36, 829-838.
Wan F, Li S, Tian T, et al. (2020) Exp2sl: A machine learning framework for cell-line-specific synthetic lethality prediction. Frontiers Pharmacol 11, e112.
Wang B, Mezlini AM, Demir F, et al. (2014) Similarity network fusion for aggregating data types on a genomic scale. Nat Methods 11(3), 333-337.
Wang E, Sandberg R, Khrebtukova I, et al. (2008) Alternative isoform regulation in human tissue transcriptomes. Nature 456, 470-476.
Wang J, Agarwal D, Huang M, et al. (2019) Data denoising with transfer learning in single-cell transcriptomics. Nat Methods 16, 875-878.
Wang J, Xie X, Shi J, et al. (2020) Denoising autoencoder, a deep learning algorithm, aids the identification of a novel molecular signature of lung adenocarcinoma. Genom Proteom Bioinform 18, 468-480.
Wang L, Park HJ, Dasari S, et al. (2013) CPAT: Coding-potential assessment tool using an alignmentfree logistic regression model. Nucleic Acids Res 41(6), e74.
Wang P, Liu X, Berzin TM, et al. (2020) Effect of a Deep-Learning Computer-Aided Detection System on Adenoma Detection during Colonoscopy (CADe-DB Trial): A Double-Blind Randomised Study. Lancet Gastroenterol Hepatol 5, 343-351.
Wang S, Peng J, Ma J, Xu J (2016) Protein secondary structure prediction using deep convolutional neural fields. Sci Rep 6, e18962.
Wang Y, Liu T, Xu D, et al. (2016) Predicting DNA methylation state of CpG dinucleotide using genome topological features and deep networks. Sci Rep 22(6), e19598.
Wang Y, Mao H, Yi Z (2017) Protein secondary structure prediction by using deep learning method. Knowledge-Based Syst 118, 115-123.
Wang Y, Zhang X-S, Chen L (2018) Integrating data- and model-driven strategies in systems biology. BMC Systems Biol 12, e38.
Wang Z, Gerstein M, Snyder M (2010) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10, 57-63.
Way GP, Allaway RJ, Bouley SJ, et al. (2017) A machine learning classifier trained on cancer transcriptomes detects nf1 inactivation signal in glioblastoma. BMC Genomics 18(1), e127.
Weiss R (2010) Bayesian methods for data analysis. Am J Ophthalmol 149(2), 187-188.
Werner S, Schmidt L, Marchand V, et al. (2020) Machine learning of reverse transcription signatures of variegated polymerases allows mapping and discrimination of methylated purines in limited transcriptomes. Nucleic Acids Res 48, 3734-3746.
Wilkins M, Sanchez JC, Gooley AA, et al. (1996) Progress with proteome projects: why all proteins expressed by a genome should be identified and how to do it. Biotechnol Genet Eng Rev 13, 19-50.
Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1, 270-280.
Wolff J, Pauling J, Keck A, Baumbach J (2020) The economic impact of artificial intelligence in health care: Systematic review. J Med Internet Res 22, e16866.
Woloszynek S, Zhao Z, Chen J, Rosen G (2019) 16s rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses. PLoS Comput Biol 15(2): e1006721.
Wood A, Altman M, Bembenek A, et al. (2018) Differential Privacy: A Primer for a Non-Technical Audience. Vand J Ent Technol Law 21(1), 209-275.
Wu C, Alwine J (2004) Secondary structure as a functional feature in the downstream region of mammalian polyadenylation signals. Molecular and Cellular Biology 24, 2789-2796.
Wu C, Zhou F, Ren J, et al. (2019) A selective review of multi-level omics data integration using variable selection. High-Throughput 8(1), e4.
Wu ST, Roberts K, Datta S, et al. (2020) Deep learning in clinical natural language processing: a methodical review. J Am Med Inform Assoc 27(3), 457-470.
Wu Y, Schuster M, Chen Z, et al. (2016) Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. ArXiv 1609, e08144.
Xia Z, Li Y, Zhang B, et al. (2019) Deerect-polya: a robust and generic deep learning method for pas identification. Bioinformatics 35(14), 2371-2379.
Xu J, Yang P, Xue S, et al. (2019) Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives. Hum Genet 138(2), 109-124.
Yang J, Lodgher A (2019) Fundamental defensive programming practices with secure coding modules. In: 2019 International Conference on Security and Management. Las Vegas, Nevada.
Yang Y, Sun H, Zhang Y, et al. (2021) Dimensionality reduction by umap reinforces sample heterogeneity analysis in bulk transcriptomic data. Cell Rep 36(4), e109442.
Yates JA (2011) A century of mass spectrometry: from atoms to proteomes. Nat Methods 8, 633–637 (2011).
Yaxley KJ, Joiner KF, Abbass H (2021) Drone approach parameters leading to lower stress sheep flocking and movement: sky shepherding. Sci Rep 11, e7803.
Yazdani A, Lu L, Raissi M, Karniadakis GE (2020) Systems biology informed deep learning for inferring parameters and hidden dynamics. PLOS Comput Biol 16, e1007575.
Yousefi S, Amrollahi F, Amgad M, et al. (2017) Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Sci Rep 7 (1), e11707.
Yu MK, Kramer M, Dutkowski J, et al. (2016) Translation of genotype to phenotype by a hierarchy of cell subsystems. Cell Systems 2, 77-88.
Yuan Y, Liang Y, Yi L, et al. (2008) Uncorrelated linear discriminant analysis (ULDA): A powerful tool for exploration of metabolomics data. Chemom Intell Lab Syst 93(1), 70-79.
Yuan Y. Li C, Kim J, et al. (2016) Deepgene: an advanced cancer type classifier based on deep learning and somatic point mutations. BMC Bioinformatics 17, e476.
Yue T, Wang H (2018) Deep learning for genomics: A concise overview. arXiv 1802, e00810.
Zamboni N, Saghatelian A, Patti G (2015) Defining the metabolome: size, flux, and regulation. Mol Cell 58(4), 699-706.
Zampieri G, Vijayakumar S, Yaneske E, Angione C (2019) Machine and deep learning meet genome-scale metabolic modeling. PLoS Comput Biol 15, e1007084.
Zeng L, Das S, Burne RA (2010) Utilization of lactose and galactose by streptococcus mutans: Transport, toxicity, and carbon catabolite repression. J Bacteriol 192, 2434-2444.
Zhang F, Song H, Zeng M, et al. (2019) Deepfunc: A deep learning framework for accurate prediction of protein functions from protein sequences and interactions. Proteomics 19(12), e1900019.
Zhang G, Zeng T, Dai Z, Dai X (2021) Prediction of CRISPR/Cas9 single guide RNA cleavage efficiency and specificity by attention-based convolutional neural networks. Comput Struct Biotechnol J 19, 1445-1457.
Zhang L, Qin X, Liu M, et al. (2021) DNN-m6A: A cross-species method for identifying RNA N6-Methyladenosine sites based on deep neural network with multi-information fusion. Genes 12(3), e354.
Zhang S, Li Q, Liu J, Zhou XJ (2011) A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics 27, i401-i409.
Zhang Y, An L, Yue F, Hardison RC (2016) Jointly characterizing epigenetic dynamics across multiple human cell types. Nucleic Acids Research 44, 6721-6731.
Zhang Z, Pan Z, Ying Y, et al. (2019) Deep-learning augmented RNA-seq analysis of transcript splicing. Nat Methods 16(4), 307-310.
Zhang Z, Wu S, Stenoien D, Pasa-Tolic L (2014) High-throughput proteomics. Annu Rev Anal Chem 7, 427-454.
Zhang Z, Zhao Y, Liao X, et al. (2019) Deep learning in omics: a survey and guideline. Brief Funct Genomics 18, 41-57.
Zhao B, Rubinstein BI, Gemmell J, Han J (2012) A bayesian approach to discovering truth from conflicting sources for data integration. ArXiv 1203, e0058
Zhou J, Troyanskaya OG (2015) Predicting effects of noncoding variants with deep learning–based sequence model. Nature Methods 12, 931–934.
Zhou Y, Xia Q, Zhang Z, et al. (2022) Artificial Intelligence and Machine Learning for the Green Development of Agriculture in the Emerging Manufacturing Industry in the IoT Platform. Acta Agric Scand Sect B Soil Plant Sci 72, 284-299.
Zhou Y, Zeng P, Li Y-H, et al. (2016) Sramp: prediction of mammalian n6-methyladenosine (m6a) sites based on sequence-derived features. Nucleic Acids Res 44, e91.
Zhou ZH, Jiang Y, Chen SF (2003) Extracting symbolic rules from trained neural network ensembles. AI Communications 16, 3-15.
Zitnik M, Agrawal M, Leskovec J (2018) Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34(13), i457-466.
Zou J, Huss M, Abid A, et al. (2019) A primer on deep learning in genomics. Nature Genet 51, 12-18.
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