Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.

ABSTRACT:

Motivation

Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic modifications and genetic variations. Computational modeling, as an essential research method, has generated promising testable quantitative models that represent complex interplay among different gene regulatory mechanisms based on these data in many biological systems. However, given the dynamic changes of interactome in chaotic systems such as cancers, and the dramatic growth of heterogeneous data on this topic, such promise has encountered unprecedented challenges in terms of model complexity and scalability. In this study, we introdu

SUBMITTER: Dogan H 

PROVIDER: S-EPMC8574978 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature