Machine learning for perturbational single-cell omics.

[1]  Anne E Carpenter,et al.  Predicting compound activity from phenotypic profiles and chemical structures , 2020, bioRxiv.

[2]  D. Shah,et al.  Causal Imputation via Synthetic Interventions , 2020, CLeaR.

[3]  D. Sculley,et al.  Using deep learning to annotate the protein universe , 2019, Nature Biotechnology.

[4]  David R. Kelley,et al.  Effective gene expression prediction from sequence by integrating long-range interactions , 2021, Nature Methods.

[5]  A. Regev,et al.  Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion , 2021, Nature Genetics.

[6]  Jimeng Sun,et al.  Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics , 2021, ArXiv.

[7]  Stefan G. Stark,et al.  pmVAE: Learning Interpretable Single-Cell Representations with Pathway Modules , 2021, bioRxiv.

[8]  David Lopez-Paz,et al.  In Search of Lost Domain Generalization , 2020, ICLR.

[9]  Kayvan Najarian,et al.  Machine learning approaches and databases for prediction of drug–target interaction: a survey paper , 2020, Briefings Bioinform..

[10]  J. T. H. Lee,et al.  Fast searches of large collections of single cell data using scfind , 2019, Nature Methods.

[11]  Avanti Shrikumar,et al.  Base-resolution models of transcription factor binding reveal soft motif syntax , 2019, Nature Genetics.

[12]  Fabian J Theis,et al.  Conditional out-of-distribution generation for unpaired data using transfer VAE. , 2020, Bioinformatics.

[13]  Anne E Carpenter,et al.  Image-based profiling for drug discovery: due for a machine-learning upgrade? , 2020, Nature reviews. Drug discovery.

[14]  Thierry Langer,et al.  A compact review of molecular property prediction with graph neural networks. , 2020, Drug discovery today. Technologies.

[15]  Luke Zappia,et al.  Sfaira accelerates data and model reuse in single cell genomics , 2020, bioRxiv.

[16]  Y. Saeys,et al.  Comprehensive benchmarking of single cell RNA sequencing technologies for characterizing cellular perturbation , 2020, bioRxiv.

[17]  Sheng Wang,et al.  DrugOrchestra: Jointly predicting drug response, targets, and side effects via deep multi-task learning , 2020, bioRxiv.

[18]  Dong Xu,et al.  Single-Cell Techniques and Deep Learning in Predicting Drug Response. , 2020, Trends in pharmacological sciences.

[19]  Xiuzhen Huang,et al.  Machine Learning Methods in Drug Discovery , 2020, Molecules.

[20]  John R. Haliburton,et al.  Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign , 2020, Proceedings of the National Academy of Sciences.

[21]  C. Rubio-Perez,et al.  A single-cell tumor immune atlas for precision oncology , 2020, bioRxiv.

[22]  Jianzhu Ma,et al.  Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. , 2020, Cancer cell.

[23]  Ellen D. Zhong,et al.  RNA timestamps identify the age of single molecules in RNA sequencing , 2020, Nature Biotechnology.

[24]  V. Zachariadis,et al.  A Highly Scalable Method for Joint Whole-Genome Sequencing and Gene-Expression Profiling of Single Cells. , 2020, Molecular cell.

[25]  Michael B. Stadler,et al.  Phenotypic landscape of intestinal organoid regeneration , 2020, Nature.

[26]  Zhihan Zhou,et al.  DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome , 2020, bioRxiv.

[27]  Qin Ma,et al.  Integrative Methods and Practical Challenges for Single-Cell Multi-omics. , 2020, Trends in biotechnology.

[28]  James M. McFarland,et al.  Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and therapeutic mechanism of action , 2020, Nature Communications.

[29]  A. Regev,et al.  Mapping multicellular programs from single-cell profiles , 2020, bioRxiv.

[30]  Kiya W. Govek,et al.  Massively parallel and time-resolved RNA sequencing in single cells with scNT-Seq , 2020, Nature Methods.

[31]  Anne E Carpenter,et al.  Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery , 2020, bioRxiv.

[32]  Fabian J. Theis,et al.  Query to reference single-cell integration with transfer learning , 2020, bioRxiv.

[33]  J. Knoblich,et al.  Human organoids: model systems for human biology and medicine , 2020, Nature Reviews Molecular Cell Biology.

[34]  Ian T. Fiddes,et al.  Single-cell sequencing of genomic DNA resolves sub-clonal heterogeneity in a melanoma cell line , 2020, Communications Biology.

[35]  Christoph A. Merten,et al.  Targeted Perturb-seq enables genome-scale genetic screens in single cells , 2020, Nature Methods.

[36]  Mark Chen,et al.  Language Models are Few-Shot Learners , 2020, NeurIPS.

[37]  P. Canoll,et al.  Deconvolution of cell type-specific drug responses in human tumor tissue with single-cell RNA-seq , 2020, Genome Medicine.

[38]  J. Shendure,et al.  Sci-fate characterizes the dynamics of gene expression in single cells , 2020, Nature Biotechnology.

[39]  Jacob C. Kimmel Disentangling latent representations of single cell RNA-seq experiments , 2020, bioRxiv.

[40]  Youngmi Yoon,et al.  Prediction of Side Effects Using Comprehensive Similarity Measures , 2020, BioMed research international.

[41]  Jure Leskovec,et al.  MARS: discovering novel cell types across heterogeneous single-cell experiments , 2020, Nature Methods.

[42]  Samantha A. Morris,et al.  Dissecting cell identity via network inference and in silico gene perturbation , 2023, Nature.

[43]  Jenna L. Pappalardo,et al.  Disease state prediction from single-cell data using graph attention networks , 2020, CHIL.

[44]  I. Amit,et al.  Single-cell genomic approaches for developing the next generation of immunotherapies , 2020, Nature Medicine.

[45]  Xiaolong Cheng,et al.  scMAGeCK links genotypes with multiple phenotypes in single-cell CRISPR screens , 2020, Genome Biology.

[46]  Smita Krishnaswamy,et al.  Uncovering axes of variation among single-cell cancer specimens , 2020, Nature Methods.

[47]  J. Stebbing,et al.  Patient-derived xenograft models—the future of personalised cancer treatment , 2020, British Journal of Cancer.

[48]  Thomas M. Norman,et al.  Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs , 2019, Nature Biotechnology.

[49]  Bo Thiesson,et al.  Explainable artificial intelligence model to predict acute critical illness from electronic health records , 2019, Nature Communications.

[50]  Jose Davila-Velderrain,et al.  A multiresolution framework to characterize single-cell state landscapes , 2019, Nature Communications.

[51]  Djork-Arné Clevert,et al.  De novo generation of hit-like molecules from gene expression signatures using artificial intelligence , 2018, Nature Communications.

[52]  Leonard J. Foster,et al.  Cell type prioritization in single-cell data , 2019, Nature Biotechnology.

[53]  Lior Pachter,et al.  Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins , 2019, Nature Biotechnology.

[54]  Jonathan S. Packer,et al.  Massively multiplex chemical transcriptomics at single-cell resolution , 2019, Science.

[55]  Jie Zheng,et al.  Emerging deep learning methods for single-cell RNA-seq data analysis , 2019, Quantitative Biology.

[56]  Willem Waegeman,et al.  Novel transformer networks for improved sequence labeling in genomics , 2019, bioRxiv.

[57]  Jamie L. Marshall,et al.  Single cell census of human kidney organoids shows reproducibility and diminished off-target cells after transplantation , 2019, Nature Communications.

[58]  Fabian J Theis,et al.  Generalizing RNA velocity to transient cell states through dynamical modeling , 2019, Nature Biotechnology.

[59]  Hyeonwoo Yu,et al.  Zero-shot Learning via Simultaneous Generating and Learning , 2019, NeurIPS.

[60]  Christoph Bock,et al.  Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data , 2019, Genome Biology.

[61]  Randall J. Platt,et al.  Mapping human cell phenotypes to genotypes with single-cell genomics , 2019, Science.

[62]  W. DuMouchel,et al.  Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology , 2019, bioRxiv.

[63]  Lovelace J Luquette,et al.  Identification of somatic mutations in single cell DNA-seq using a spatial model of allelic imbalance , 2019, Nature Communications.

[64]  Debora S. Marks,et al.  Interpretable Machine Learning for Perturbation Biology , 2019, bioRxiv.

[65]  Lior Pachter,et al.  A curated database reveals trends in single-cell transcriptomics , 2019, bioRxiv.

[66]  Thomas M. Norman,et al.  Exploring genetic interaction manifolds constructed from rich single-cell phenotypes , 2019, Science.

[67]  Jesse R. Dixon,et al.  Simultaneous profiling of 3D genome structure and DNA methylation in single human cells , 2019, Nature Methods.

[68]  Mohammad Lotfollahi,et al.  scGen predicts single-cell perturbation responses , 2019, Nature Methods.

[69]  Fabian J Theis,et al.  Deep learning: new computational modelling techniques for genomics , 2019, Nature Reviews Genetics.

[70]  Jennifer L Hu,et al.  MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices , 2019, Nature Methods.

[71]  Robert M. Vogel,et al.  Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen , 2019, Nature Communications.

[72]  Hanhui Ma,et al.  Model-based understanding of single-cell CRISPR screening , 2019, Nature Communications.

[73]  Panayiotis V. Benos,et al.  Causal network perturbations for instance-specific analysis of single cell and disease samples , 2019, bioRxiv.

[74]  Duhee Bang,et al.  Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations , 2019, Science Advances.

[75]  David van Dijk,et al.  Enhancing experimental signals in single-cell RNA-sequencing data using graph signal processing , 2019 .

[76]  Benjamin Haibe-Kains,et al.  Dr.VAE: improving drug response prediction via modeling of drug perturbation effects , 2019, Bioinform..

[77]  C. Mason,et al.  The Impact of Heterogeneity on Single-Cell Sequencing , 2019, Front. Genet..

[78]  Andrea Volkamer,et al.  Advances and Challenges in Computational Target Prediction , 2019, J. Chem. Inf. Model..

[79]  Keisha N. Hardeman,et al.  Quantifying Drug Combination Synergy along Potency and Efficacy Axes. , 2019, Cell systems.

[80]  Hiu Ting Chan,et al.  The Roles of Common Variation and Somatic Mutation in Cancer Pharmacogenomics , 2019, Oncology and Therapy.

[81]  Jacob M. Schreiber,et al.  A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens , 2019, Cell.

[82]  D. Erhan,et al.  A Benchmark for Interpretability Methods in Deep Neural Networks , 2018, NeurIPS.

[83]  Christoph H. Lampert,et al.  Zero-Shot Learning—A Comprehensive Evaluation of the Good, the Bad and the Ugly , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[84]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[85]  Marcin Kurdziel,et al.  Training neural networks on high-dimensional data using random projection , 2019, Pattern Analysis and Applications.

[86]  Bertrand Z. Yeung,et al.  Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics , 2018, Genome Biology.

[87]  Daniel Weindl,et al.  Efficient Parameter Estimation Enables the Prediction of Drug Response Using a Mechanistic Pan-Cancer Pathway Model. , 2018, Cell systems.

[88]  Michael I. Jordan,et al.  Deep Generative Modeling for Single-cell Transcriptomics , 2018, Nature Methods.

[89]  M. Hild,et al.  DRUG-seq for miniaturized high-throughput transcriptome profiling in drug discovery , 2018, Nature Communications.

[90]  Avanti Shrikumar,et al.  Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays , 2018, bioRxiv.

[91]  Erik Sundström,et al.  RNA velocity of single cells , 2018, Nature.

[92]  Di Wu,et al.  DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks , 2018, bioRxiv.

[93]  Tae Soon Kim,et al.  Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature , 2018, Scientific Reports.

[94]  Diogo M. Camacho,et al.  Next-Generation Machine Learning for Biological Networks , 2018, Cell.

[95]  Y. Moreau,et al.  Linking drug target and pathway activation for effective therapy using multi-task learning , 2018, Scientific Reports.

[96]  Robert Tibshirani,et al.  DRUG-NEM: Optimizing drug combinations using single-cell perturbation response to account for intratumoral heterogeneity , 2018, Proceedings of the National Academy of Sciences.

[97]  Alexander V. Favorov,et al.  Enter the Matrix: Factorization Uncovers Knowledge from Omics , 2018, Trends in genetics : TIG.

[98]  Paul Hoffman,et al.  Integrating single-cell transcriptomic data across different conditions, technologies, and species , 2018, Nature Biotechnology.

[99]  Laleh Haghverdi,et al.  Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors , 2018, Nature Biotechnology.

[100]  Weihua Li,et al.  In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts , 2018, Front. Chem..

[101]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

[102]  Nicholas M. Luscombe,et al.  Generative adversarial networks simulate gene expression and predict perturbations in single cells , 2018, bioRxiv.

[103]  A. Shaw,et al.  Tumour heterogeneity and resistance to cancer therapies , 2018, Nature Reviews Clinical Oncology.

[104]  Cole Trapnell,et al.  On the design of CRISPR-based single cell molecular screens , 2018, Nature Methods.

[105]  Kai Fan,et al.  Zero-Shot Learning via Class-Conditioned Deep Generative Models , 2017, AAAI.

[106]  David S. Wishart,et al.  DrugBank 5.0: a major update to the DrugBank database for 2018 , 2017, Nucleic Acids Res..

[107]  Zhaleh Safikhani,et al.  PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies , 2017, bioRxiv.

[108]  Cory Y. McLean,et al.  Sequential regulatory activity prediction across chromosomes with convolutional neural networks , 2017, bioRxiv.

[109]  Mikael Benson,et al.  Single-cell analyses to tailor treatments , 2017, Science Translational Medicine.

[110]  Casey S. Greene,et al.  Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders , 2017, bioRxiv.

[111]  Z. Bar-Joseph,et al.  Using neural networks for reducing the dimensions of single-cell RNA-Seq data , 2017, Nucleic acids research.

[112]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[113]  Anne E Carpenter,et al.  Opportunities and obstacles for deep learning in biology and medicine , 2017, bioRxiv.

[114]  Yu Yao,et al.  DeepPPI: Boosting Prediction of Protein-Protein Interactions with Deep Neural Networks , 2017, J. Chem. Inf. Model..

[115]  Angela N. Brooks,et al.  A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles , 2017, Cell.

[116]  Fabian J Theis,et al.  The Human Cell Atlas , 2017, bioRxiv.

[117]  Avanti Shrikumar,et al.  Learning Important Features Through Propagating Activation Differences , 2017, ICML.

[118]  Herman Yeger,et al.  Combination therapy in combating cancer , 2017, Oncotarget.

[119]  Richard S. Zemel,et al.  Prototypical Networks for Few-shot Learning , 2017, NIPS.

[120]  Andrew C. Adey,et al.  Single-Cell Transcriptional Profiling of a Multicellular Organism , 2017 .

[121]  André F. Rendeiro,et al.  Pooled CRISPR screening with single-cell transcriptome read-out , 2017, Nature Methods.

[122]  William Stafford Noble,et al.  Massively multiplex single-cell Hi-C , 2016, Nature Methods.

[123]  I. Amit,et al.  Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq , 2016, Cell.

[124]  Thomas M. Norman,et al.  A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response , 2016, Cell.

[125]  Thomas M. Norman,et al.  Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens , 2016, Cell.

[126]  A. Regev,et al.  Revealing the vectors of cellular identity with single-cell genomics , 2016, Nature Biotechnology.

[127]  O. Stegle,et al.  Deep learning for computational biology , 2016, Molecular systems biology.

[128]  Andrew D. Rouillard,et al.  Enrichr: a comprehensive gene set enrichment analysis web server 2016 update , 2016, Nucleic Acids Res..

[129]  A. Abyzov,et al.  Single-cell analysis of targeted transcriptome predicts drug sensitivity of single cells within human myeloma tumors , 2016, Leukemia.

[130]  Anne E Carpenter,et al.  Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes , 2016, Nature Protocols.

[131]  R. Aebersold,et al.  On the Dependency of Cellular Protein Levels on mRNA Abundance , 2016, Cell.

[132]  Nima Tajbakhsh,et al.  Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? , 2016, IEEE Transactions on Medical Imaging.

[133]  W. Koh,et al.  Single-cell genome sequencing: current state of the science , 2016, Nature Reviews Genetics.

[134]  Andrew H. Beck,et al.  PharmacoGx: an R package for analysis of large pharmacogenomic datasets , 2015, Bioinform..

[135]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[136]  Joshua A. Bittker,et al.  Correlating chemical sensitivity and basal gene expression reveals mechanism of action , 2015, Nature chemical biology.

[137]  J. Mesirov,et al.  The Molecular Signatures Database Hallmark Gene Set Collection , 2015 .

[138]  Honglak Lee,et al.  Learning Structured Output Representation using Deep Conditional Generative Models , 2015, NIPS.

[139]  Joshua M. Korn,et al.  High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response , 2015, Nature Medicine.

[140]  Alán Aspuru-Guzik,et al.  Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.

[141]  O. Troyanskaya,et al.  Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.

[142]  Xia Li,et al.  Gene Perturbation Atlas (GPA): a single-gene perturbation repository for characterizing functional mechanisms of coding and non-coding genes , 2015, Scientific Reports.

[143]  J. Michael Cherry,et al.  Ontology application and use at the ENCODE DCC , 2015, Database J. Biol. Databases Curation.

[144]  B. Guthrie,et al.  Drug-disease and drug-drug interactions: systematic examination of recommendations in 12 UK national clinical guidelines , 2015, BMJ : British Medical Journal.

[145]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[146]  Nci Dream Community A community effort to assess and improve drug sensitivity prediction algorithms , 2014 .

[147]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[148]  Yang Xie,et al.  A community computational challenge to predict the activity of pairs of compounds Citation , 2015 .

[149]  Markus A. Lill,et al.  PharmDock: a pharmacophore-based docking program , 2014, Journal of Cheminformatics.

[150]  Joshua C. Gilbert,et al.  An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules , 2013, Cell.

[151]  Edward Y. Chen,et al.  Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool , 2013, BMC Bioinformatics.

[152]  P. Clemons,et al.  Target identification and mechanism of action in chemical biology and drug discovery. , 2013, Nature chemical biology.

[153]  Sridhar Ramaswamy,et al.  Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells , 2012, Nucleic Acids Res..

[154]  I. Simon,et al.  Studying and modelling dynamic biological processes using time-series gene expression data , 2012, Nature Reviews Genetics.

[155]  Brad T. Sherman,et al.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists , 2008, Nucleic acids research.

[156]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[157]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[158]  Zixiang Xiong,et al.  Optimal number of features as a function of sample size for various classification rules , 2005, Bioinform..

[159]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[160]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.