{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T05:53:00Z","timestamp":1778133180956,"version":"3.51.4"},"reference-count":202,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81671445"],"award-info":[{"award-number":["81671445"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171365"],"award-info":[{"award-number":["62171365"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The innovation of biotechnologies has allowed the accumulation of omics data at an alarming rate, thus introducing the era of \u2018big data\u2019. Extracting inherent valuable knowledge from various omics data remains a daunting problem in bioinformatics. Better solutions often need some kind of more innovative methods for efficient handlings and effective results. Recent advancements in integrated analysis and computational modeling of multi-omics data helped address such needs in an increasingly harmonious manner. The development and application of machine learning have largely advanced our insights into biology and biomedicine and greatly promoted the development of therapeutic strategies, especially for precision medicine. Here, we propose a comprehensive survey and discussion on what happened, is happening and will happen when machine learning meets omics. Specifically, we describe how artificial intelligence can be applied to omics studies and review recent advancements at the interface between machine learning and the ever-widest range of omics including genomics, transcriptomics, proteomics, metabolomics, radiomics, as well as those at the single-cell resolution. We also discuss and provide a synthesis of ideas, new insights, current challenges and perspectives of machine learning in omics.<\/jats:p>","DOI":"10.1093\/bib\/bbab460","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T07:38:41Z","timestamp":1633678721000},"source":"Crossref","is-referenced-by-count":145,"title":["Machine learning meets omics: applications and perspectives"],"prefix":"10.1093","volume":"23","author":[{"given":"Rufeng","family":"Li","sequence":"first","affiliation":[{"name":"Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi\u2019an Jiaotong University Health Science Center, Xi\u2019an 710061, P. R. China"}]},{"given":"Lixin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an, 710129, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9834-3006","authenticated-orcid":false,"given":"Yungang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi\u2019an Jiaotong University Health Science Center, Xi\u2019an 710061, P. R. China"}]},{"given":"Juan","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Cell Biology and Genetics, School of Basic Medical Sciences, Xi\u2019an Jiaotong University Health Science Center, Xi\u2019an 710061, P. R. China"},{"name":"Key Laboratory of Environment and Genes Related to Diseases (Xi\u2019an Jiaotong University), Ministry of Education of China, Xi\u2019an 710061, P. R. China"}]}],"member":"286","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"key":"2022012000285950700_ref1","first-page":"851","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","year":"2017","journal-title":"Brief Bioinform"},{"key":"2022012000285950700_ref2","first-page":"9","article-title":"Artificial intelligence in dentistry: the way forward","volume-title":"J Dent Res","author":"Singh","year":"2020"},{"key":"2022012000285950700_ref3","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1068\/p180793","article-title":"An investigation of trained neural networks from a neurophysiological perspective","volume":"18","author":"Moorhead","year":"1989","journal-title":"Perception"},{"key":"2022012000285950700_ref4","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural 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Biol"},{"key":"2022012000285950700_ref81","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1038\/s41587-020-0573-5","article-title":"Sequence-specific prediction of the efficiencies of adenine and cytosine base editors","volume":"38","author":"Song","year":"2020","journal-title":"Nat Biotechnol"},{"key":"2022012000285950700_ref82","doi-asserted-by":"crossref","first-page":"e1005807","DOI":"10.1371\/journal.pcbi.1005807","article-title":"A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action","volume":"13","author":"Abadi","year":"2017","journal-title":"PLoS Comput Biol"},{"key":"2022012000285950700_ref83","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1038\/nbt.4317","article-title":"Predicting the mutations generated by repair of Cas9-induced double-strand breaks","volume":"37","author":"Allen","year":"2019","journal-title":"Nat Biotechnol"},{"key":"2022012000285950700_ref84","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1093\/bioinformatics\/btw074","article-title":"Gene expression inference with deep learning","volume":"32","author":"Chen","year":"2016","journal-title":"Bioinformatics"},{"key":"2022012000285950700_ref85","doi-asserted-by":"crossref","first-page":"i639","DOI":"10.1093\/bioinformatics\/btw427","article-title":"DeepChrome: deep-learning for predicting gene expression from histone modifications","volume":"32","author":"Singh","year":"2016","journal-title":"Bioinformatics"},{"key":"2022012000285950700_ref86","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/onc.2013.570","article-title":"Alternative splicing in cancer: implications for biology and therapy","volume":"34","author":"Chen","year":"2015","journal-title":"Oncogene"},{"key":"2022012000285950700_ref87","doi-asserted-by":"crossref","first-page":"i121","DOI":"10.1093\/bioinformatics\/btu277","article-title":"Deep 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binding","volume":"32","author":"Zeng","year":"2016","journal-title":"Bioinformatics"},{"key":"2022012000285950700_ref94","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1038\/nbt.2798","article-title":"Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape","volume":"32","author":"Sherwood","year":"2014","journal-title":"Nat Biotechnol"},{"key":"2022012000285950700_ref95","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s41587-019-0315-8","article-title":"Deciphering eukaryotic gene-regulatory logic with 100 million random promoters","volume":"38","author":"Boer","year":"2020","journal-title":"Nat Biotechnol"},{"key":"2022012000285950700_ref96","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12967-020-02630-3","article-title":"A machine learning-based clinical tool for diagnosing myopathy using multi-cohort microarray expression 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disease","volume":"6","author":"Su","year":"2020","journal-title":"NPJ Parkinsons Disease"},{"key":"2022012000285950700_ref100","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1016\/j.eswa.2012.08.070","article-title":"Parkinson's disease prediction using gene expression\u2014a projection based learning meta-cognitive neural classifier approach","volume":"40","author":"Babu","year":"2013","journal-title":"Expert Syst Appl"},{"key":"2022012000285950700_ref101","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1093\/bioinformatics\/btz772","article-title":"Cancer classification of single-cell gene expression data by neural network","volume":"36","author":"Kim","year":"2020","journal-title":"Bioinformatics"},{"key":"2022012000285950700_ref102","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1136\/gutjnl-2019-318217","article-title":"Morphological classification of pancreatic ductal adenocarcinoma that predicts molecular subtypes and correlates with clinical outcome","volume":"69","author":"Kalimuthu","year":"2020","journal-title":"Gut"},{"key":"2022012000285950700_ref103","doi-asserted-by":"crossref","first-page":"254","DOI":"10.3389\/fbioe.2020.00254","article-title":"Early diagnosis of hepatocellular carcinoma using machine learning method","volume":"8","author":"Zhang","year":"2020","journal-title":"Front Bioeng Biotechnol"},{"key":"2022012000285950700_ref104","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1158\/1078-0432.CCR-17-0853","article-title":"Deep learning-based multi-omics integration robustly predicts survival in liver cancer","volume":"24","author":"Chaudharyl","year":"2018","journal-title":"Clin Cancer Res"},{"key":"2022012000285950700_ref105","doi-asserted-by":"crossref","first-page":"3017","DOI":"10.1093\/bioinformatics\/btr502","article-title":"Semi-supervised learning improves gene expression-based prediction of cancer recurrence","volume":"27","author":"Shi","year":"2011","journal-title":"Bioinformatics"},{"key":"2022012000285950700_ref106","doi-asserted-by":"crossref","first-page":"404","DOI":"10.2215\/CJN.07420619","article-title":"Proteomics and metabolomics in kidney disease, including insights into etiology, treatment, and prevention","volume":"15","author":"Dubin","year":"2020","journal-title":"Clin J Am Soc Nephrol"},{"key":"2022012000285950700_ref107","doi-asserted-by":"crossref","first-page":"8247","DOI":"10.1073\/pnas.1705691114","article-title":"De novo peptide sequencing by deep learning","volume":"114","author":"Tran","year":"2017","journal-title":"Proc Natl Acad Sci USA"},{"key":"2022012000285950700_ref108","doi-asserted-by":"crossref","first-page":"12690","DOI":"10.1021\/acs.analchem.7b02566","article-title":"pDeep: predicting MS\/MS spectra of peptides with deep learning","volume":"89","author":"Zhou","year":"2017","journal-title":"Anal Chem"},{"key":"2022012000285950700_ref109","doi-asserted-by":"crossref","first-page":"10881","DOI":"10.1021\/acs.analchem.8b02386","article-title":"Improved peptide retention time prediction in liquid chromatography through deep learning","volume":"90","author":"Ma","year":"2018","journal-title":"Anal Chem"},{"key":"2022012000285950700_ref110","doi-asserted-by":"crossref","first-page":"17168","DOI":"10.1038\/s41598-019-52954-4","article-title":"DeepIso: a deep learning model for peptide feature detection from LC-MS map","volume":"9","author":"Zohora","year":"2019","journal-title":"Sci Rep"},{"key":"2022012000285950700_ref111","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1093\/bioinformatics\/btx724","article-title":"Deep learning for tumor classification in imaging mass spectrometry","volume":"34","author":"Behrmann","year":"2018","journal-title":"Bioinformatics"},{"key":"2022012000285950700_ref112","doi-asserted-by":"crossref","first-page":"P1133","DOI":"10.1016\/j.jalz.2017.06.1648","article-title":"[P3-431]: deep learning application in identifying proteomic risk markers for Alzheimer's disease","volume":"13","author":"An","year":"2017","journal-title":"Alzheimers Dement"},{"key":"2022012000285950700_ref113","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/s42256-020-0180-7","article-title":"An interpretable mortality prediction model for COVID-19 patients","volume":"2","author":"Yan","year":"2020","journal-title":"Nat Mach Intell"},{"key":"2022012000285950700_ref114","doi-asserted-by":"crossref","first-page":"e63","DOI":"10.1093\/nar\/gku117","article-title":"A comparative analysis of transcription factor binding models learned from PBM, HT-SELEX and ChIP data","volume":"42","author":"Orenstein","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2022012000285950700_ref115","doi-asserted-by":"crossref","first-page":"113565","DOI":"10.1016\/j.ab.2019.113565","article-title":"Discovering nuclear targeting signal sequence through protein language learning and multivariate analysis","volume":"591","author":"Guo","year":"2020","journal-title":"Anal Biochem"},{"key":"2022012000285950700_ref116","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1038\/s41467-018-08236-0","article-title":"Diverse motif ensembles specify non-redundant DNA binding activities of AP-1 family members in macrophages","volume":"10","author":"Fonseca","year":"2019","journal-title":"Nat Commun"},{"key":"2022012000285950700_ref117","doi-asserted-by":"crossref","first-page":"3028","DOI":"10.1093\/bioinformatics\/btx381","article-title":"MotifHyades: expectation maximization for de novo DNA motif pair discovery on paired sequences","volume":"33","author":"Wong","year":"2017","journal-title":"Bioinformatics"},{"key":"2022012000285950700_ref118","doi-asserted-by":"crossref","first-page":"W365","DOI":"10.1093\/nar\/gkx407","article-title":"HDOCK: a web server for protein-protein and protein-DNA\/RNA docking based on a hybrid strategy","volume":"45","author":"Yan","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2022012000285950700_ref119","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1093\/bioinformatics\/bty756","article-title":"Improving the prediction of protein-nucleic acids binding residues via multiple sequence profiles and the consensus of complementary methods","volume":"35","author":"Su","year":"2019","journal-title":"Bioinformatics"},{"key":"2022012000285950700_ref120","doi-asserted-by":"crossref","first-page":"D358","DOI":"10.1093\/nar\/gkt1115","article-title":"The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases","volume":"42","author":"Orchard","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2022012000285950700_ref121","doi-asserted-by":"crossref","first-page":"D369","DOI":"10.1093\/nar\/gkw1102","article-title":"The BioGRID interaction database: 2017 update","volume":"45","author":"Chatr-aryamontri","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2022012000285950700_ref122","first-page":"1","article-title":"Different protein-protein interface patterns predicted by different machine learning methods","volume":"7","author":"Wang","year":"2017","journal-title":"Sci Rep"},{"key":"2022012000285950700_ref123","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/s41592-019-0687-1","article-title":"Biophysical prediction of protein-peptide interactions and signaling networks using machine learning","volume":"17","author":"Cunningham","year":"2020","journal-title":"Nat Methods"},{"key":"2022012000285950700_ref124","doi-asserted-by":"crossref","first-page":"551","DOI":"10.2174\/1574893611666160815150746","article-title":"DeepInteract: deep neural network based protein-protein interaction prediction tool","volume":"12","author":"Patel","year":"2017","journal-title":"Curr Bioinforma"},{"key":"2022012000285950700_ref125","doi-asserted-by":"crossref","first-page":"i802","DOI":"10.1093\/bioinformatics\/bty573","article-title":"Predicting protein-protein interactions through sequence-based deep learning","volume":"34","author":"Hashemifar","year":"2018","journal-title":"Bioinformatics"},{"key":"2022012000285950700_ref126","doi-asserted-by":"crossref","first-page":"2586","DOI":"10.1074\/mcp.M110.001388","article-title":"Musite, a tool for global prediction of general and kinase-specific phosphorylation sites","volume":"9","author":"Gao","year":"2010","journal-title":"Mol Cell Proteomics"},{"key":"2022012000285950700_ref127","doi-asserted-by":"crossref","first-page":"e67008","DOI":"10.1371\/journal.pone.0067008","article-title":"In silico platform for prediction of N-, O- and C-Glycosites in eukaryotic protein sequences","volume":"8","author":"Chauhan","year":"2013","journal-title":"PLoS One"},{"key":"2022012000285950700_ref128","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1002\/pro.2494","article-title":"The structural and functional signatures of proteins that undergo multiple events of post-translational modification","volume":"23","author":"Pejaver","year":"2014","journal-title":"Protein Sci"},{"key":"2022012000285950700_ref129","doi-asserted-by":"crossref","first-page":"2766","DOI":"10.1093\/bioinformatics\/bty1051","article-title":"DeepPhos: prediction of protein phosphorylation sites with deep 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visualization","volume":"48","author":"Wang","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2022012000285950700_ref133","doi-asserted-by":"crossref","first-page":"1\u20131","DOI":"10.1096\/fasebj.2020.34.s1.03091","article-title":"SAPH-ire TFx: a machine learning recommendation method and Webtool for the prediction of functional post-translational modifications","volume":"34","author":"English","year":"2020","journal-title":"FASEB J"},{"key":"2022012000285950700_ref134","doi-asserted-by":"crossref","first-page":"243","DOI":"10.3390\/metabo10060243","article-title":"Machine learning applications for mass spectrometry-based metabolomics","volume":"10","author":"Liebal","year":"2020","journal-title":"Metabolites"},{"key":"2022012000285950700_ref135","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1007\/s11306-019-1612-4","article-title":"A comparative evaluation of the generalised predictive ability of eight machine learning algorithms across ten clinical metabolomics data sets for binary classification","volume":"15","author":"Mendez","year":"2019","journal-title":"Metabolomics"},{"key":"2022012000285950700_ref136","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1021\/acs.analchem.6b03678","article-title":"Artificial neural network for probabilistic feature recognition in liquid chromatography coupled to high-resolution mass spectrometry","volume":"89","author":"Woldegebriel","year":"2017","journal-title":"Anal Chem"},{"key":"2022012000285950700_ref137","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1021\/acs.analchem.9b04811","article-title":"Deep learning for the precise peak detection in high-resolution LC-MS data","volume":"92","author":"Melnikov","year":"2020","journal-title":"Anal Chem"},{"key":"2022012000285950700_ref138","doi-asserted-by":"crossref","first-page":"12407","DOI":"10.1021\/acs.analchem.9b02983","article-title":"Deep neural networks for classification of LC-MS spectral peaks","volume":"91","author":"Kantz","year":"2019","journal-title":"Anal Chem"},{"key":"2022012000285950700_ref139","doi-asserted-by":"crossref","first-page":"5629","DOI":"10.1021\/acs.analchem.8b05405","article-title":"Deep MS\/MS-aided structural-similarity scoring for unknown metabolite identification","volume":"91","author":"Ji","year":"2019","journal-title":"Anal Chem"},{"key":"2022012000285950700_ref140","doi-asserted-by":"crossref","first-page":"3500","DOI":"10.1039\/C6SC03738K","article-title":"Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer","volume":"8","author":"Inglese","year":"2017","journal-title":"Chem Sci"},{"key":"2022012000285950700_ref141","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1021\/acs.jproteome.7b00595","article-title":"Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data","volume":"17","author":"Alakwaa","year":"2018","journal-title":"J Proteome Res"},{"key":"2022012000285950700_ref142","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1021\/acs.analchem.7b03795","article-title":"Application of a deep neural network to metabolomics studies and its performance in determining important variables","volume":"90","author":"Date","year":"2018","journal-title":"Anal Chem"},{"key":"2022012000285950700_ref143","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.aca.2018.02.045","article-title":"Application of ensemble deep neural network to metabolomics studies","volume":"1037","author":"Asakura","year":"2018","journal-title":"Anal Chim Acta"},{"key":"2022012000285950700_ref144","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms4083","article-title":"Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease","volume":"5","author":"Mardinoglu","year":"2014","journal-title":"Nat Commun"},{"key":"2022012000285950700_ref145","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1038\/nbt.4072","article-title":"Recon3D enables a three-dimensional view of gene variation in human metabolism","volume":"36","author":"Brunk","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2022012000285950700_ref146","doi-asserted-by":"crossref","first-page":"8304260","DOI":"10.1155\/2019\/8304260","article-title":"Human systems biology and metabolic modelling: a review-from disease metabolism to precision medicine","volume":"2019","author":"Angione","year":"2019","journal-title":"Biomed Res Int"},{"key":"2022012000285950700_ref147","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.copbio.2019.11.007","article-title":"Recent advances on constraint-based models by integrating machine learning","volume":"64","author":"Rana","year":"2020","journal-title":"Curr Opin Biotechnol"},{"key":"2022012000285950700_ref148","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3390\/metabo8010004","article-title":"Machine learning methods for analysis of metabolic data and metabolic pathway modeling","volume":"8","author":"Cuperlovic-Culf","year":"2018","journal-title":"Metabolites"},{"key":"2022012000285950700_ref149","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.cbpa.2017.10.033","article-title":"Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era","volume":"42","author":"Zhou","year":"2018","journal-title":"Curr Opin Chem Biol"},{"key":"2022012000285950700_ref150","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.1007\/s00125-017-4325-0","article-title":"Early metabolic markers identify potential targets for the prevention of type 2 diabetes","volume":"60","author":"Peddinti","year":"2017","journal-title":"Diabetologia"},{"key":"2022012000285950700_ref151","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1093\/bib\/bbaa204","article-title":"Deep learning meets metabolomics: a methodological perspective","volume":"22","author":"Sen","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022012000285950700_ref152","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/nmeth.1315","article-title":"mRNA-Seq whole-transcriptome analysis of a single cell","volume":"6","author":"Tang","year":"2009","journal-title":"Nat Methods"},{"key":"2022012000285950700_ref153","doi-asserted-by":"crossref","first-page":"8845","DOI":"10.1093\/nar\/gku555","article-title":"Single-cell RNA-seq: advances and future challenges","volume":"42","author":"Saliba","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2022012000285950700_ref154","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1038\/nmeth.3971","article-title":"Diffusion pseudotime robustly reconstructs lineage branching","volume":"13","author":"Haghverdi","year":"2016","journal-title":"Nat Methods"},{"key":"2022012000285950700_ref155","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1038\/s41576-019-0095-5","article-title":"Publisher correction: challenges in unsupervised clustering of single-cell RNA-seq data","volume":"20","author":"Kiselev","year":"2019","journal-title":"Nat Rev Genet"},{"key":"2022012000285950700_ref156","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1038\/nmeth.2967","article-title":"Bayesian approach to single-cell differential expression analysis","volume":"11","author":"Kharchenko","year":"2014","journal-title":"Nat Methods"},{"key":"2022012000285950700_ref157","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/nrg3833","article-title":"Computational and analytical challenges in single-cell transcriptomics","volume":"16","author":"Stegle","year":"2015","journal-title":"Nat Rev Genet"},{"key":"2022012000285950700_ref158","first-page":"1","article-title":"An accurate and robust imputation method scImpute for single-cell RNA-seq data","volume":"9","author":"Li","year":"2018","journal-title":"Nat Commun"},{"key":"2022012000285950700_ref159","doi-asserted-by":"crossref","first-page":"e161","DOI":"10.1093\/nar\/gku864","article-title":"Svaseq: removing batch effects and other unwanted noise from sequencing data","volume":"42","author":"Leek","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2022012000285950700_ref160","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-018-07931-2","article-title":"Single-cell RNA-seq denoising using a deep count autoencoder","volume":"10","author":"Eraslan","year":"2019","journal-title":"Nat 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(Camb)"},{"key":"2022012000285950700_ref179","doi-asserted-by":"crossref","first-page":"3888","DOI":"10.1039\/C9LC00695H","article-title":"CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning","volume":"19","author":"Rossi","year":"2019","journal-title":"Lab Chip"},{"key":"2022012000285950700_ref180","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","article-title":"Image analysis and machine learning in digital pathology: challenges and opportunities","volume":"33","author":"Madabhushi","year":"2016","journal-title":"Med Image Anal"},{"key":"2022012000285950700_ref181","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1016\/j.jacr.2018.01.028","article-title":"Role of big data and machine learning in diagnostic decision support in radiology","volume":"15","author":"Syeda-Mahmood","year":"2018","journal-title":"J Am Coll 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