A Primer on Machine Learning Techniques for Genomic Applications

Overview

Journal Comput Struct Biotechnol J

Specialty Biotechnology

Date 2021 Aug 25

PMID 34429852

Citations 8

Authors

Alfonso Monaco

Ester Pantaleo

Nicola Amoroso

Antonio Lacalamita

Claudio Lo Giudice

Adriano Fonzino

Bruno Fosso

Ernesto Picardi

Sabina Tangaro

Graziano Pesole

Roberto Bellotti

Affiliations

Soon will be listed here.

Abstract

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous "omic" data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available.

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References

Aevermann B, Novotny M, Bakken T, Miller J, Diehl A, Osumi-Sutherland D . Cell type discovery using single-cell transcriptomics: implications for ontological representation. Hum Mol Genet. 2018; 27(R1):R40-R47. PMC: 5946857. DOI: 10.1093/hmg/ddy100. View

Reuter J, Spacek D, Snyder M . High-throughput sequencing technologies. Mol Cell. 2015; 58(4):586-97. PMC: 4494749. DOI: 10.1016/j.molcel.2015.05.004. View

Boloc D, Gortat A, Cheng-Zhang J, Garcia-Cerro S, Rodriguez N, Parellada M . Improving pharmacogenetic prediction of extrapyramidal symptoms induced by antipsychotics. Transl Psychiatry. 2018; 8(1):276. PMC: 6293322. DOI: 10.1038/s41398-018-0330-4. View

Barros-Silva D, Marques C, Henrique R, Jeronimo C . Profiling DNA Methylation Based on Next-Generation Sequencing Approaches: New Insights and Clinical Applications. Genes (Basel). 2018; 9(9). PMC: 6162482. DOI: 10.3390/genes9090429. View

Wang Q, Garrity G, Tiedje J, Cole J . Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007; 73(16):5261-7. PMC: 1950982. DOI: 10.1128/AEM.00062-07. View

Kim R, Xu H, Myllykangas S, Ji H . Genetic-based biomarkers and next-generation sequencing: the future of personalized care in colorectal cancer. Per Med. 2013; 8(3):331-345. PMC: 3646399. DOI: 10.2217/pme.11.16. View

Wang J, Yang B, An Y, Marquez-Lago T, Leier A, Wilksch J . Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches. Brief Bioinform. 2017; 20(3):931-951. PMC: 6585386. DOI: 10.1093/bib/bbx164. View

Petegrosso R, Li Z, Kuang R . Machine learning and statistical methods for clustering single-cell RNA-sequencing data. Brief Bioinform. 2019; 21(4):1209-1223. DOI: 10.1093/bib/bbz063. View

Teng S, Yang J, Wang L . Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data. BMC Med Genomics. 2013; 6 Suppl 1:S10. PMC: 3552705. DOI: 10.1186/1755-8794-6-S1-S10. View

10.

Arai F, Stumpf P, Ikushima Y, Hosokawa K, Roch A, Lutolf M . Machine Learning of Hematopoietic Stem Cell Divisions from Paired Daughter Cell Expression Profiles Reveals Effects of Aging on Self-Renewal. Cell Syst. 2020; 11(6):640-652.e5. DOI: 10.1016/j.cels.2020.11.004. View

11.

Lin C, Jain S, Kim H, Bar-Joseph Z . Using neural networks for reducing the dimensions of single-cell RNA-Seq data. Nucleic Acids Res. 2017; 45(17):e156. PMC: 5737331. DOI: 10.1093/nar/gkx681. View

12.

Lee K, Kim B, Choi Y, Kim S, Shin W, Lee S . Deep learning of mutation-gene-drug relations from the literature. BMC Bioinformatics. 2018; 19(1):21. PMC: 5784504. DOI: 10.1186/s12859-018-2029-1. View

13.

Xuan J, Yu Y, Qing T, Guo L, Shi L . Next-generation sequencing in the clinic: promises and challenges. Cancer Lett. 2012; 340(2):284-95. PMC: 5739311. DOI: 10.1016/j.canlet.2012.11.025. View

14.

Cheng X, Cai H, Zhang Y, Xu B, Su W . Optimal combination of feature selection and classification via local hyperplane based learning strategy. BMC Bioinformatics. 2015; 16:219. PMC: 4498526. DOI: 10.1186/s12859-015-0629-6. View

15.

Nyberg K, Bruce S, Nguyen A, Chan C, Gill N, Kim T . Predicting cancer cell invasion by single-cell physical phenotyping. Integr Biol (Camb). 2018; 10(4):218-231. DOI: 10.1039/c7ib00222j. View

16.

Down T, Leong B, Hubbard T . A machine learning strategy to identify candidate binding sites in human protein-coding sequence. BMC Bioinformatics. 2006; 7:419. PMC: 1592515. DOI: 10.1186/1471-2105-7-419. View

17.

Zhu G, Deng W, Hu H, Ma R, Zhang S, Yang J . Reconstructing spatial organizations of chromosomes through manifold learning. Nucleic Acids Res. 2018; 46(8):e50. PMC: 5934626. DOI: 10.1093/nar/gky065. View

18.

Orange D, Agius P, DiCarlo E, Robine N, Geiger H, Szymonifka J . Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data. Arthritis Rheumatol. 2018; 70(5):690-701. PMC: 6336443. DOI: 10.1002/art.40428. View

19.

Saghapour E, Kermani S, Sehhati M . A novel feature ranking method for prediction of cancer stages using proteomics data. PLoS One. 2017; 12(9):e0184203. PMC: 5608217. DOI: 10.1371/journal.pone.0184203. View

20.

Kim S, Park T, Kon M . Cancer survival classification using integrated data sets and intermediate information. Artif Intell Med. 2014; 62(1):23-31. DOI: 10.1016/j.artmed.2014.06.003. View