NEM-Tar: A Probabilistic Graphical Model for Cancer Regulatory Network Inference and Prioritization of Potential Therapeutic Targets From Multi-Omics Data
Targeted therapy has been widely adopted as an effective treatment strategy to battle against cancer. However, cancers are not single disease entities, but comprising multiple molecularly distinct subtypes, and the heterogeneity nature prevents precise selection of patients for optimized therapy. Di...
Main Authors: | Yuchen Zhang, Lina Zhu, Xin Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-04-01
|
Series: | Frontiers in Genetics |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.608042/full |
Similar Items
-
Integrated regulatory models for inference of subtype‐specific susceptibilities in glioblastoma
by: Yunpeng Liu, et al.
Published: (2020-09-01) -
GraphPPL.jl: A Probabilistic Programming Language for Graphical Models
by: Wouter W. L. Nuijten, et al.
Published: (2024-10-01) -
A study of the HIV-1 regulatory genes using the polymerase chain reaction
by: K. A. Ryzhov, et al.
Published: (2015-06-01) -
Using empirical biological knowledge to infer regulatory networks from multi-omics data
by: Anna Pačínková, et al.
Published: (2022-08-01) -
Regulatory perspectives of combination products
by: Jiaxin Tian, et al.
Published: (2022-04-01)