Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

Overview

Journal Front Oncol

Specialty Oncology

Date 2020 Nov 6

PMID 33154949

Citations 40

Authors

Nupur Biswas

Saikat Chakrabarti

Affiliations

Soon will be listed here.

Abstract

Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of "big"-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.

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References

Zhang J, Baran J, Cros A, Guberman J, Haider S, Hsu J . International Cancer Genome Consortium Data Portal--a one-stop shop for cancer genomics data. Database (Oxford). 2011; 2011:bar026. PMC: 3263593. DOI: 10.1093/database/bar026. View

Wong D, Yip S . Machine learning classifies cancer. Nature. 2020; 555(7697):446-447. DOI: 10.1038/d41586-018-02881-7. View

Chaudhary K, Poirion O, Lu L, Garmire L . Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin Cancer Res. 2017; 24(6):1248-1259. PMC: 6050171. DOI: 10.1158/1078-0432.CCR-17-0853. View

Ghandi M, Huang F, Jane-Valbuena J, Kryukov G, Lo C, McDonald 3rd E . Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019; 569(7757):503-508. PMC: 6697103. DOI: 10.1038/s41586-019-1186-3. View

Liu Z, Ma A, Mathe E, Merling M, Ma Q, Liu B . Network analyses in microbiome based on high-throughput multi-omics data. Brief Bioinform. 2020; 22(2):1639-1655. PMC: 7986608. DOI: 10.1093/bib/bbaa005. View

Hasin Y, Seldin M, Lusis A . Multi-omics approaches to disease. Genome Biol. 2017; 18(1):83. PMC: 5418815. DOI: 10.1186/s13059-017-1215-1. View

Cohen I, Efroni S . The Immune System Computes the State of the Body: Crowd Wisdom, Machine Learning, and Immune Cell Reference Repertoires Help Manage Inflammation. Front Immunol. 2019; 10:10. PMC: 6349705. DOI: 10.3389/fimmu.2019.00010. View

Ai L, Tian H, Chen Z, Chen H, Xu J, Fang J . Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer. Oncotarget. 2017; 8(6):9546-9556. PMC: 5354752. DOI: 10.18632/oncotarget.14488. View

Hollingsworth S . Precision medicine in oncology drug development: a pharma perspective. Drug Discov Today. 2015; 20(12):1455-63. DOI: 10.1016/j.drudis.2015.10.005. View

10.

Adir O, Poley M, Chen G, Froim S, Krinsky N, Shklover J . Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine. Adv Mater. 2019; 32(13):e1901989. PMC: 7124889. DOI: 10.1002/adma.201901989. View

11.

Chervova O, Conde L, Guerra-Assuncao J, Moghul I, Webster A, Berner A . The Personal Genome Project-UK, an open access resource of human multi-omics data. Sci Data. 2019; 6(1):257. PMC: 6823446. DOI: 10.1038/s41597-019-0205-4. View

12.

Collins F, Varmus H . A new initiative on precision medicine. N Engl J Med. 2015; 372(9):793-5. PMC: 5101938. DOI: 10.1056/NEJMp1500523. View

13.

Friedman A, Letai A, Fisher D, Flaherty K . Precision medicine for cancer with next-generation functional diagnostics. Nat Rev Cancer. 2015; 15(12):747-56. PMC: 4970460. DOI: 10.1038/nrc4015. View

14.

Lyons Y, Wu S, Overwijk W, Baggerly K, Sood A . Immune cell profiling in cancer: molecular approaches to cell-specific identification. NPJ Precis Oncol. 2018; 1(1):26. PMC: 5871917. DOI: 10.1038/s41698-017-0031-0. View

15.

Hanahan D, Weinberg R . Hallmarks of cancer: the next generation. Cell. 2011; 144(5):646-74. DOI: 10.1016/j.cell.2011.02.013. View

16.

. The Integrative Human Microbiome Project. Nature. 2019; 569(7758):641-648. PMC: 6784865. DOI: 10.1038/s41586-019-1238-8. View

17.

Xu J, Wu P, Chen Y, Meng Q, Dawood H, Dawood H . A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data. BMC Bioinformatics. 2019; 20(1):527. PMC: 6819613. DOI: 10.1186/s12859-019-3116-7. View

18.

Mariette J, Villa-Vialaneix N . Unsupervised multiple kernel learning for heterogeneous data integration. Bioinformatics. 2017; 34(6):1009-1015. DOI: 10.1093/bioinformatics/btx682. View

19.

Ali M, Aittokallio T . Machine learning and feature selection for drug response prediction in precision oncology applications. Biophys Rev. 2018; 11(1):31-39. PMC: 6381361. DOI: 10.1007/s12551-018-0446-z. View

20.

Gonzalez H, Hagerling C, Werb Z . Roles of the immune system in cancer: from tumor initiation to metastatic progression. Genes Dev. 2018; 32(19-20):1267-1284. PMC: 6169832. DOI: 10.1101/gad.314617.118. View