Open AccessApplications of probability and statistics in cancer genomics
Author(s) -
Ma Xiaotu,
Arunachalam Sasi,
Liu Yanling
Publication year - 2020
Publication title -
quantitative biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.707
H-Index - 15
eISSN - 2095-4697
pISSN - 2095-4689
DOI - 10.1007/s40484-020-0203-8
Subject(s) - genomics , personalized medicine , computational biology , probabilistic logic , genome , epigenomics , cancer , biology , bioinformatics , computer science , genetics , gene , artificial intelligence , gene expression , dna methylation
Background The past decade has witnessed a rapid progress in our understanding of the genetics of cancer and its progression. Probabilistic and statistical modeling played a pivotal role in the discovery of general patterns from cancer genomics datasets and continue to be of central importance for personalized medicine. Results In this review we introduce cancer genomics from a probabilistic and statistical perspective. We start from (1) functional classification of genes into oncogenes and tumor suppressor genes, then (2) demonstrate the importance of comprehensive analysis of different mutation types for individual cancer genomes, followed by (3) tumor purity analysis, which in turn leads to (4) the concept of ploidy and clonality, that is next connected to (5) tumor evolution under treatment pressure, which yields insights into cancer drug resistance. We also discuss future challenges including the non‐coding genomic regions, integrative analysis of genomics and epigenomics, as well as early cancer detection. Conclusion We believe probabilistic and statistical modeling will continue to play important roles for novel discoveries in the field of cancer genomics and personalized medicine.