收集生物、基因、医学人工智能相关资料
Collect biological, genetic, medical artificial intelligence related materials
- Helmholtz Zentrum München, German Research Center for Environmental Health, Theis lab
- Kellis Lab at MIT Computer Science and Broad Institute
- Deep learning: new computational modelling techniques for genomics Eraslan, G., Avsec, Ž., Gagneur, J. et al. Nat Rev Genet 20, 389–403 (2019). https://doi.org/10.1038/s41576-019-0122-6
- Computational Systems Biology: Deep Learning in the Life Sciences
- Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology Richards, S., Aziz, N., Bale, S. et al. Genet Med 17, 405–423 (2015). https://doi.org/10.1038/gim.2015.30
- The Biology of Human Diseases, as Revealed Through Genomics
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*** Predicting effects of noncoding variants with deep learning–based sequence model Zhou J, Troyanskaya O G. Nature methods, 2015, 12(10): 931-934.
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*** Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk Zhou J, Theesfeld C L, Yao K, et al. Nature genetics, 2018, 50(8): 1171-1179.
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DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning Arloth J, Eraslan G, Andlauer T F M, et al. PLOS Computational Biology, 2020, 16(2): e1007616.
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Graph Neural Networks: A Review of Methods and Applications Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun
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A Comprehensive Survey on Graph Neural Networks Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
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DANN: a deep learning approach for annotating the pathogenicity of genetic variants Quang D, Chen Y, Xie X. Bioinformatics, 2015, 31(5): 761-763.
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*** A general framework for estimating the relative pathogenicity of human genetic variants Kircher, M., Witten, D., Jain, P. et al. Nat Genet 46, 310–315 (2014). https://doi.org/10.1038/ng.2892
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*** CADD: predicting the deleteriousness of variants throughout the human genome Rentzsch P, Witten D, Cooper G M, et al. Nucleic acids research, 2019, 47(D1): D886-D894.
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*** Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning Alipanahi, B., Delong, A., Weirauch, M. et al. Nat Biotechnol 33, 831–838 (2015). https://doi.org/10.1038/nbt.3300
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Gene co-expression analysis for functional classification and gene–disease predictions[J]. van Dam S, Vosa U, van der Graaf A, et al. Briefings in bioinformatics, 2018, 19(4): 575-592.
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*** FTO obesity variant circuitry and adipocyte browning in humans. Claussnitzer, Melina, et al. New England Journal of Medicine 373.10 (2015): 895-907.
- DeepWAS: Directly integrating regulatory information into GWAS using machine learning
- Graph attention networks Veličković P, Cucurull G, Casanova A, et al. . arXiv preprint arXiv:1710.10903, 2017.
- BioBombe: Sequentially compressed gene expression features enhances biological signatures
- DANN
- DeepBind
- A unified approach to interpreting model predictions Lundberg S M, Lee S I. Advances in neural information processing systems. 2017: 4765-4774.
- shap: A game theoretic approach to explain the output of any machine learning model
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Pan-cancer analysis of whole genomes Campbell, P.J., Getz, G., Korbel, J.O. et al. Nature 578, 82–93 (2020). https://doi.org/10.1038/s41586-020-1969-6
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The repertoire of mutational signatures in human cancerAlexandrov L B, Kim J, Haradhvala N J, et al. Nature, 2020, 578(7793): 94-101.
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The evolutionary history of 2,658 cancers Gerstung M, Jolly C, Leshchiner I, et al. Nature, 2020, 578(7793): 122-128.
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Patterns of somatic structural variation in human cancer genomes Li, Y., Roberts, N.D., Wala, J.A. et al. Nature 578, 112–121 (2020). https://doi.org/10.1038/s41586-019-1913-9
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Genomic basis for RNA alterations in cancerCalabrese, C., Davidson, N.R., Demircioğlu, D. et al. Nature 578, 129–136 (2020). https://doi.org/10.1038/s41586-020-1970-0
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Analyses of non-coding somatic drivers in 2,658 cancer whole genomes Rheinbay, E., Nielsen, M.M., Abascal, F. et al. Nature 578, 102–111 (2020). https://doi.org/10.1038/s41586-020-1965-x
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Comprehensive molecular characterization of mitochondrial genomes in human cancers Yuan, Y., Ju, Y.S., Kim, Y. et al. Nat Genet 52, 342–352 (2020). https://doi.org/10.1038/s41588-019-0557-x
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Disruption of chromatin folding domains by somatic genomic rearrangements in human cancerAkdemir, K.C., Le, V.T., Chandran, S. et al. Nat Genet 52, 294–305 (2020). https://doi.org/10.1038/s41588-019-0564-y
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*** A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns Jiao, W., Atwal, G., Polak, P. et al. Nat Commun 11, 728 (2020). https://doi.org/10.1038/s41467-019-13825-8
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Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives Xu J, Yang P, Xue S, et al. Human genetics, 2019, 138(2): 109-124.
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Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden Chalmers, Z.R., Connelly, C.F., Fabrizio, D. et al. Genome Med 9, 34 (2017).
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*** Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Zhou, J., Park, C.Y., Theesfeld, C.L. et al. Nat Genet 51, 973–980 (2019). https://doi.org/10.1038/s41588-019-0420-0
- Circulating tumor DNA 5-hydroxymethylcytosine as a novel diagnostic biomarker for esophageal cancer Tian, X., Sun, B., Chen, C. et al. Cell Res 28, 597–600 (2018). https://doi.org/10.1038/s41422-018-0014-x
- Integrating multi-omics data with deep learning for predicting cancer prognosis Hua Chai, Xiang Zhou, Zifeng Cui, Jiahua Rao, Zheng Hu, Yutong Lu, Huiying Zhao, Yuedong Yang. bioRxiv 807214; doi: https://doi.org/10.1101/807214
- Cancer classification of single-cell gene expression data by neural network Kim B H, Yu K, Lee P C W. Bioinformatics, 2020, 36(5): 1360-1366.
- Deep learning based tumor type classification using gene expression data Lyu B, Haque A. //Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics. 2018: 89-96.
- Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA M.C.Liu1, G.R.Oxnard, E.A.Klein, C.Swanton, M.V.Seiden, https://doi.org/10.1016/j.annonc.2020.02.011
- Microbiome analyses of blood and tissues suggest cancer diagnostic approach Poore, G.D., Kopylova, E., Zhu, Q. et al. Nature 579, 567–574 (2020). https://doi.org/10.1038/s41586-020-2095-1
- *** Identification of 12 cancer types through genome deep learning Sun, Y., Zhu, S., Ma, K. et al. Sci Rep 9, 17256 (2019). https://doi.org/10.1038/s41598-019-53989-3
- DeepMicro: deep representation learning for disease prediction based on microbiome data Oh, M., Zhang, L. Sci Rep 10, 6026 (2020). https://doi.org/10.1038/s41598-020-63159-5
- Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning Lai, Y., Chen, W., Hsu, T. et al. Sci Rep 10, 4679 (2020). https://doi.org/10.1038/s41598-020-61588-w
- A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data Ainscough, B.J., Barnell, E.K., Ronning, P. et al. Nat Genet 50, 1735–1743 (2018). https://doi.org/10.1038/s41588-018-0257-y
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning Roy, S., Kumar, R., Mittal, V. et al. Sci Rep 10, 4113 (2020). https://doi.org/10.1038/s41598-020-60740-w
- Integrating multi-platform genomic datasets for kidney renal clear cell carcinoma subtyping using stacked denoising autoencoders Gu, T., Zhao, X. Sci Rep 9, 16668 (2019). https://doi.org/10.1038/s41598-019-53048-x
- OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicineLópez-Cortés, A., Paz-y-Miño, C., Guerrero, S. et al. Sci Rep 10, 5285 (2020). https://doi.org/10.1038/s41598-020-62279-2
- A community effort to create standards for evaluating tumor subclonal reconstruction Salcedo, A., Tarabichi, M., Espiritu, S.M.G. et al. Nat Biotechnol 38, 97–107 (2020). https://doi.org/10.1038/s41587-019-0364-z
- *** Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer Zhang P, Yang M, Zhang Y, et al. Cell reports, 2019, 27(6): 1934-1947. e5.
- *** Deep learning based tumor type classification using gene expression data[C] Lyu B, Haque A. Deep learning based tumor type classification using gene expression data[C]//Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics. 2018: 89-96.
- Deep learning–based multi-omics integration robustly predicts survival in liver cancer[J]. Chaudhary K, Poirion O B, Lu L, et al. Clinical Cancer Research, 2018, 24(6): 1248-1259.
- CrossHub: a tool for multi-way analysis of The Cancer Genome Atlas (TCGA) in the context of gene expression regulation mechanisms[J] Krasnov G S, Dmitriev A A, Melnikova N V, et al. Nucleic acids research, 2016, 44(7): e62-e62.
- Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models Yousefi, S., Amrollahi, F., Amgad, M. et al. Sci Rep 7, 11707 (2017). https://doi.org/10.1038/s41598-017-11817-6
- *** DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. Gao, F., Wang, W., Tan, M. et al. Oncogenesis 8, 44 (2019). https://doi.org/10.1038/s41389-019-0157-8
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of a random forest and a deep neural network Lee, K., Jeong, H., Lee, S. et al. Sci Rep 9, 16927 (2019).
- Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data. Maros, M.E., Capper, D., Jones, D.T.W. et al. Nat Protoc 15, 479–512 (2020). https://doi.org/10.1038/s41596-019-0251-6
- tcga: Microbial analysis in TCGA data
- cancer-data: TCGA data acquisition and processing for Project Cognoma
- DeepMicro: Deep representation learning for disease prediction based on microbiome data
- overall_survival_nsclc: bimodal DNN for NSCLC patient overall survival prediction
- DeepSVR
- cancer_subtyping
- *** DL-based-Tumor-Classification: Deep Learning Based Tumor Type Classification Using Gene Expression Data
- SurvivalNet: SurvivalNet is a package for building survival analysis models using deep learning.
- DeepCC: a deep learning-based framework for cancer classification
- *** CPEM: Accurate cancer type classification based on somatic alterations using an ensemble of random forests and a deep neural network
- CancerSEA: a cancer single-cell state atlas Yuan H, Yan M, Zhang G, et al. Nucleic acids research, 2019, 47(D1): D900-D908.
- The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly Pinese, M., Lacaze, P., Rath, E.M. et al. Nat Commun 11, 435 (2020). https://doi.org/10.1038/s41467-019-14079-0
- DisGeNET is a discovery platform containing one of the largest publicly available collections of genes and variants associated to human diseases
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*** RNA sequencing: the teenage years Stark R, Grzelak M, Hadfield J. Nature Reviews Genetics, 2019, 20(11): 631-656. PDF 中文: 笔记1 笔记2
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*** Current best practices in single‐cell RNA‐seq analysis: a tutorial Luecken M D, Theis F J. Molecular systems biology, 2019, 15(6). 中文：重磅综述：三万字长文读懂单细胞RNA测序分析的最佳实践教程 （原理、代码和评述）
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*** Single-cell RNA-seq denoising using a deep count autoencoder Eraslan, G., Simon, L.M., Mircea, M. et al. Nat Commun 10, 390 (2019). https://doi.org/10.1038/s41467-018-07931-2
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*** Construction of a human cell landscape at single-cell level Han, X., Zhou, Z., Fei, L. et al. Nature (2020). https://doi.org/10.1038/s41586-020-2157-4
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Clustering single-cell RNA-seq data with a model-based deep learning approach Tian, T., Wan, J., Song, Q. et al. Nat Mach Intell 1, 191–198 (2019). https://doi.org/10.1038/s42256-019-0037-0
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Imputing Single-cell RNA-seq data by combining Graph Convolution and Autoencoder Neural Networks Jiahua Rao, Xiang Zhou, Yutong Lu, Huiying Zhao, Yuedong Yang bioRxiv 2020.02.05.935296; doi: https://doi.org/10.1101/2020.02.05.935296
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Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling Zhang, A.W., O’Flanagan, C., Chavez, E.A. et al. Nat Methods 16, 1007–1015 (2019). https://doi.org/10.1038/s41592-019-0529-1
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DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data Arisdakessian, C., Poirion, O., Yunits, B. et al. Genome Biol 20, 211 (2019). https://doi.org/10.1186/s13059-019-1837-6
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Unsupervised generative and graph representation learning for modelling cell differentiation Ioana Bica, Helena Andrés-Terré, Ana Cvejic, Pietro Liò. bioRxiv 801605; doi: https://doi.org/10.1101/801605
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A comparison of automatic cell identification methods for single-cell RNA sequencing data Abdelaal, T., Michielsen, L., Cats, D. et al. Genome Biol 20, 194 (2019). https://doi.org/10.1186/s13059-019-1795-z github: scRNAseq_Benchmark
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Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning Deng Y, Bao F, Dai Q, et al. Nature methods, 2019, 16(4): 311-314.
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Deep learning for inferring gene relationships from single-cell expression data Yuan Y, Bar-Joseph Z. Proceedings of the National Academy of Sciences, 2019, 116(52): 27151-27158.
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Emerging deep learning methods for single-cell RNA-seq data analysis Zheng, J., Wang, K. Quant Biol 7, 247–254 (2019). https://doi.org/10.1007/s40484-019-0189-2
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DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data Arisdakessian, C., Poirion, O., Yunits, B. et al. Genome Biol 20, 211 (2019). https://doi.org/10.1186/s13059-019-1837-6
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Deep learning enables accurate clustering and batch effect removal in single-cell RNA-seq analysis Xiangjie Li, Yafei Lyu, Jihwan Park, Jingxiao Zhang, Dwight Stambolian, Katalin Susztak, Gang Hu, Mingyao Li. bioRxiv 530378; doi: https://doi.org/10.1101/530378
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Using neural networks for reducing the dimensions of single-cell RNA-Seq dataLin C, Jain S, Kim H, et al. Nucleic acids research, 2017, 45(17): e156-e156.
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DigitalDLSorter: Deep-Learning on scRNA-Seq to deconvolute gene expression data Torroja C, Sanchez-Cabo F. Frontiers in genetics, 2019, 10: 978.
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Tools for the analysis of high-dimensional single-cell RNA sequencing data. Nat Rev Nephrol (2020) Wu, Y., Zhang, K. https://doi.org/10.1038/s41581-020-0262-0
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Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data Mieth, B., Hockley, J.R.F., Görnitz, N. et al. Sci Rep 9, 20353 (2019). https://doi.org/10.1038/s41598-019-56911-z
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Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks Marouf, M., Machart, P., Bansal, V. et al. Nat Commun 11, 166 (2020). https://doi.org/10.1038/s41467-019-14018-z
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Reconstruction of Cell-type-Specific Interactomes at Single-Cell Resolution Mohammadi S, Davila-Velderrain J, Kellis M. Cell Systems, 2019, 9(6): 559-568. e4.
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Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling Zhang, A.W., O’Flanagan, C., Chavez, E.A. et al. Nat Methods 16, 1007–1015 (2019). https://doi.org/10.1038/s41592-019-0529-1
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PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells Wolf, F.A., Hamey, F.K., Plass, M. et al. Genome Biol 20, 59 (2019). https://doi.org/10.1186/s13059-019-1663-x
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Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks Marouf, M., Machart, P., Bansal, V. et al. Nat Commun 11, 166 (2020). https://doi.org/10.1038/s41467-019-14018-z
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A reference map of the human binary protein interactome Luck, K., Kim, D., Lambourne, L. et al. Nature (2020). https://doi.org/10.1038/s41586-020-2188-x
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*** Single-cell RNA-seq data analysis on the receptor ACE2 expression reveals the potential risk of different human organs vulnerable to 2019-nCoV infection Zou X, Chen K, Zou J, et al. Frontiers of medicine, 2020: 1-8.
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Comprehensive integration of single-cell data Stuart T, Butler A, Hoffman P, et al. Cell, 2019, 177(7): 1888-1902. e21.
- *** DCA: Deep count autoencoder for denoising scRNA-seq data
- scDeepCluster for Single Cell RNA-seq data
- cellassign: Automated, probabilistic assignment of cell types in scRNA-seq data
- scVI: scDeep generative modeling for single-cell omics data
- scVAE: Variational auto-encoders for single-cell gene expression data
- KPNN: Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data
- deepimpute: An accurate and efficient deep learning method for single-cell RNA-seq data imputation
- DiffVAE: Unsupervised generative and graph neural methods for modelling cell differentiation
- scScope
- CNNC: covolutional neural network based coexpression analysis
- deepimpute: An accurate and efficient deep learning method for single-cell RNA-seq data imputation
- digitalDLSorter: A pipeline to generate a Deep Nerual Network cell type deconvolution model for bulk RNASeq samples from single cell experiment data
- Deep learning approaches for single cell data
- awesome-single-cell
- singlecelldb: Deep learning approaches for single cell data
- scHCL: A tool defines cell types in human based on single-cell digital expression
- SCINET: Single-Cell Imputation and NETwork inference
- paga: Mapping out the coarse-grained connectivity structures of complex manifolds.
- Integrative analysis of 111 reference human epigenomes Kundaje, A., Meuleman, W., Ernst, J. et al. Nature 518, 317–330 (2015).
- An integrated encyclopedia of DNA elements in the human genome Dunham, I., Kundaje, A., Aldred, S. et al. Nature 489, 57–74 (2012).
- Integrative analysis of 10,000 epigenomic maps across 800 samples for regulatory genomics and disease dissection Carles B. Adsera, Yongjin P. Park, Wouter Meuleman, Manolis Kellis. bioRxiv 810291; doi: https://doi.org/10.1101/810291
- Towards clinical utility of polygenic risk scores Samuel A Lambert, Gad Abraham, Michael Inouye, Human Molecular Genetics, Volume 28, Issue R2, 15 October 2019, Pages R133–R142, https://doi.org/10.1093/hmg/ddz187
- A machine-learning heuristic to improve gene score prediction of polygenic traits Paré, G., Mao, S. & Deng, W.Q. Sci Rep 7, 12665 (2017). https://doi.org/10.1038/s41598-017-13056-1
- Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status Gola D, Erdmann J, Müller‐Myhsok B, et al. Genetic Epidemiology, 2020.
- Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps Mahajan, A., Taliun, D., Thurner, M. et al. Nat Genet 50, 1505–1513 (2018). https://doi.org/10.1038/s41588-018-0241-6
- Machine learning SNP based prediction for precision medicine Ho D S W, Schierding W, Wake M, et al. Frontiers in Genetics, 2019, 10.
- Analysis of polygenic risk score usage and performance in diverse human populations Duncan, L., Shen, H., Gelaye, B. et al. Nat Commun 10, 3328 (2019). https://doi.org/10.1038/s41467-019-11112-0
- Polygenic and clinical risk scores and their impact on age at onset and prediction of cardiometabolic diseases and common cancers Mars, N., Koskela, J.T., Ripatti, P. et al. Nat Med (2020). https://doi.org/10.1038/s41591-020-0800-0
- The personal and clinical utility of polygenic risk scores orkamani, A., Wineinger, N.E. & Topol, E.J. Nat Rev Genet 19, 581–590 (2018). https://doi.org/10.1038/s41576-018-0018-x
- Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype Yin B, Balvert M, van der Spek R A A, et al. Bioinformatics, 2019, 35(14): i538-i547.
- GraBLD: an R based software package that makes polygenic traits prediction using gradient boosted and LD adjusted gene score weights
- ldpred: a Python based software package that adjusts GWAS summary statistics for the effects of linkage disequilibrium (LD)
- AnnoPred: Genetic risk prediction integrating LD and functional annotations
- The accessible chromatin landscape of the human genome Thurman, R., Rynes, E., Humbert, R. et al. Nature 489, 75–82 (2012). https://doi.org/10.1038/nature11232
- Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning Coudray N, Ocampo P S, Sakellaropoulos T, et al. Nature medicine, 2018, 24(10): 1559-1567.
- Deep learning-based survival prediction for multiple cancer types using histopathology images[J]. Wulczyn E, Steiner D F, Xu Z, et al. arXiv preprint arXiv:1912.07354, 2019.
- Development and application of a machine learning approach to assess short-term mortality risk among patients with cancer starting chemotherapy Elfiky A A, Pany M J, Parikh R B, et al. JAMA network open, 2018, 1(3): e180926-e180926.