Machine learning applications for transcription level and phenotype predictions
3
Issued Date
2022-12-01
Resource Type
ISSN
15216543
eISSN
15216551
DOI
Scopus ID
2-s2.0-85142270263
Pubmed ID
36345613
Journal Title
IUBMB Life
Volume
74
Issue
12
Start Page
1273
End Page
1287
Rights Holder(s)
SCOPUS
Bibliographic Citation
IUBMB Life Vol.74 No.12 (2022) , 1273-1287
Suggested Citation
Chantaraamporn J., Phumikhet P., Nguantad S., Techo T., Charoensawan V. Machine learning applications for transcription level and phenotype predictions. IUBMB Life Vol.74 No.12 (2022) , 1273-1287. 1287. doi:10.1002/iub.2693 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/87112
Title
Machine learning applications for transcription level and phenotype predictions
Author's Affiliation
Other Contributor(s)
Abstract
Predicting phenotypes and complex traits from genomic variations has always been a big challenge in molecular biology, at least in part because the task is often complicated by the influences of external stimuli and the environment on regulation of gene expression. With today's abundance of omic data and advances in high-throughput computing and machine learning (ML), we now have an unprecedented opportunity to uncover the missing links and molecular mechanisms that control gene expression and phenotypes. To empower molecular biologists and researchers in related fields to start using ML for in-depth analyses of their large-scale data, here we provide a summary of fundamental concepts of machine learning, and describe a wide range of research questions and scenarios in molecular biology where ML has been implemented. Due to the abundance of data, reproducibility, and genome-wide coverage, we focus on transcriptomics, and two ML tasks involving it: (a) predicting of transcriptomic profiles or transcription levels from genomic variations in DNA, and (b) predicting phenotypes of interest from transcriptomic profiles or transcription levels. Similar approaches can also be applied to more complex data such as those in multi-omic studies. We envisage that the concepts and examples described here will raise awareness and promote the application of ML among molecular biologists, and eventually help improve a framework for systematic design and predictions of gene expression and phenotypes for synthetic biology applications.