TY - JOUR
T1 - Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning
AU - Dogan, Haluk
AU - Hakguder, Zeynep
AU - Madadjim, Roland
AU - Scott, Stephen
AU - Pierobon, Massimiliano
AU - Cui, Juan
N1 - Publisher Copyright:
© 2021 The Author(s). Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].
PY - 2021/11/1
Y1 - 2021/11/1
N2 - 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 introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages. Results: In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems. Availability: http://sbbi-panda.unl.edu/pin/
AB - 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 introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages. Results: In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems. Availability: http://sbbi-panda.unl.edu/pin/
KW - Bayesian network
KW - Gaussian process
KW - Markov random field
KW - functional analysis
KW - gene regulatory network
KW - graphical models
KW - miRNA binding
UR - http://www.scopus.com/inward/record.url?scp=85121952216&partnerID=8YFLogxK
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U2 - 10.1093/bib/bbab270
DO - 10.1093/bib/bbab270
M3 - Article
C2 - 34373890
AN - SCOPUS:85121952216
SN - 1467-5463
VL - 22
JO - Briefings in bioinformatics
JF - Briefings in bioinformatics
IS - 6
M1 - bbab270
ER -