Machine Learning in Biology

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅或者付费存取

详细

The increase of experimental data at the beginning of the XXI century coincided with the revolution in machine learning methods, which began to be actively used for solving biological problems. The main advantage of machine learning is the ability to formulate and test many hypotheses based on large datasets from modern experiments. The article discusses several machine learning algorithms and examples of their practical application in genetics and genomics.

作者简介

A. Taskina

Institute of Cytology and Genetics, Siberian Branch of RAS; Novosibirsk State University

Novosibirsk, Russia

A. Muravyova

Institute of Cytology and Genetics, Siberian Branch of RAS; Novosibirsk State University

Novosibirsk, Russia

A. Elsukova

Institute of Cytology and Genetics, Siberian Branch of RAS; Novosibirsk State University

Novosibirsk, Russia

V. Fishman

Institute of Cytology and Genetics, Siberian Branch of RAS; Novosibirsk State University

Email: minja-f@ya.ru
Novosibirsk, Russia

参考

  1. Perez-Riverol Y., Zorin A., Dass G. et al. Quantifying the impact of public omics data. Nat. Commun. 2019; 10(1): 1-10. doi: 10.1038/s41467-019-n461-w.
  2. Karczewski K.J., Snyder M.P. Integrative omics for health and disease. Nat. Rev. Genet. 2018; 19(5): 299-310. doi: 10.1038/nrg.2018.4.
  3. Huber D., Voith von Voithenberg L., Kaigala G.V. Fluorescence in situ hybridization (FISH): History, limitations and what to expect from micro-scale FISH? Micro Nano Eng. 2018; 1: 15-24. D0I:10.1016/j.mne.2018.10.006.
  4. Cremer T., Cremer M. Chromosome territories. Cold Spring Harb Perspect Biol. 2010; 2(3). D0I:10.1101/cshperspect.a003889.
  5. Wit E. de, Laat W. de A decade of 3C technologies: Insights into nuclear organization. Genes Dev. 2012; 26(1): 11-24. D0I:10.1101/gad.179804.111.
  6. Denker A., Laat W. de The second decade of 3C technologies: Detailed insights into nuclear organization. Genes Dev. 2016; 30(12): 1357-1382. doi: 10.1101/gad.281964.116.
  7. Fishman V.S., Salnikov P.A., Battulin N.R. Interpreting chromosomal rearrangements in the context of 3-dimentional genome organization: a practical guide for medical genetics. Biochemistry (Moscow). 2018; 83(4): 393-401. D0I:10.1134/S0006297918040107
  8. Belokopytova P.S., Nuriddinov M.A., Mozheiko E.A. et al. Quantitative prediction of enhancer-promoter interactions. Genome Research. 2020; 30(1): 72-84. D0I:10.1134/S1022795419100089.
  9. Mozheiko E.A., Fishman V.S. Detection of point mutations and chromosomal translocations based on massive parallel sequencing of enriched 3C libraries. Russ. J. Genet. 2019; 55(10): 1273-1281. D0I:10.1134/S1022795419100089.
  10. Zhou J., Theesfeld C.L., Yao K. et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat. Genet. 2018; 50(8): 1171-1179. D0I:10.1038/s41588-018-0160-6.
  11. Kelley D.R., Reshef Y.A., Bileschi M. et al. Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Res. 2018; 28(5): 739-750. doi: 10.1101/gr.227819.117.
  12. Zhou J., Troyanskaya O.G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods. 2015; 12(10): 931-934. doi: 10.1038/nmeth.3547.
  13. Rentzsch P., Witten D., Cooper G.M. et al. CADD: Predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 2019; 47(D1): D886-D894. doi: 10.1093/nar/gky1016.
  14. Herrero J, Muffato M., Beal K. et al. Ensembl comparative genomics resources. Database. 2016; 2016: baw053. doi: 10.1093/database/baw053.
  15. Tolles J., Meurer W.J. Logistic regression: Relating patient characteristics to outcomes. JAMA. 2016; 316(5): 533-534. doi: 10.1001/jama.2016.7653.
  16. Shen M.W., Arbab M., Hsu J.Y. et al. Predictable and precise template-free CRISPR editing of pathogenic variants. Nature. 2018; 563(7733): 646-651. doi: 10.1038/s41586-018-0686-x.
  17. Butler A., Hoffman P., Smibert P. et al. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 2018; 36(5): 411-420. doi: 10.1038/nbt.4096.
  18. Clark S.J., Argelaguet R., Kapourani C.A. et al. ScNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 2018; 9(1): 781. doi: 10.1038/s41467-018-03149-4.
  19. Mahmoudi S., Mancini E., Xu L. et al. Heterogeneity in old fibroblasts is linked to variability in reprogramming and wound healing. Nature. 2019; 574(7779): 553-558. doi: 10.1038/s41586-019-1658-5.
  20. Bzdok D., Altman N., Krzywinski M. Points of significance: statistics versus machine learning. Nat. Methods. 2018; 15(4): 233-234. doi: 10.1038/nmeth.4642.
  21. Cao J., Spielmann M., Qiu X. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019; 566(7745): 496-502. Doi doi: 10.1038/s41586-019-0969-x.
  22. Badsha M.B., Li R., Liu B. et al. Imputation of single-cell gene expression with an autoencoder neural network. bioRxiv. December 2018: 504977. doi: 10.1101/504977.
  23. Talwar D., Mongia A., Sengupta D., Majumdar A. AutoImpute: Autoencoder based imputation of single-cell RNA-seq data. Sci. Rep. 2018; 8(1): 16329. doi: 10.1038/s41598-018-34688-x.
  24. Wang D., Gu J. VASC: Dimension reduction and visualization of single-cell RNA-seq data by deep variational autoencoder. Genomics, Proteomics Bioinforma. 2018; 16(5): 320-331. D0I:10.1016/j.gpb.2018.08.003.
  25. Symeonidis P. Content-based dimensionality reduction for recommender systems. Studies in Classification, Data Analysis, and Knowledge Organization. 2008; 619-626. doi: 10.1007/978-3-540-78246-9_73.
  26. Plasschaert L.W., D ilionis R., Choo-Wing R. et al. A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature. 2018; 560(7718): 377-381. doi: 10.1038/s41586-018-0394-6.
  27. Clunes M.T., Boucher R.C. Cystic fibrosis: the mechanisms of pathogenesis of an inherited lung disorder. Drug Discov. Today Dis. Mech. 2007; 4(2): 63-72. D0I:10.1016/j.ddmec.2007.09.001.

补充文件

附件文件
动作
1. JATS XML
下载

版权所有 © Издательство «Наука», 2020

##common.cookie##