Literature DB >> 28113329

The Discovery of Mutated Driver Pathways in Cancer: Models and Algorithms.

Junhua Zhang, Shihua Zhang.   

Abstract

The pathogenesis of cancer in human is still poorly understood. With the rapid development of high-throughput sequencing technologies, huge volumes of cancer genomics data have been generated. Deciphering that data poses great opportunities and challenges to computational biologists. One of such key challenges is to distinguish driver mutations, genes as well as pathways from passenger ones. Mutual exclusivity of gene mutations (each patient has no more than one mutation in the gene set) has been observed in various cancer types and thus has been used as an important property of a driver gene set or pathway. In this article, we aim to review the recent development of computational models and algorithms for discovering driver pathways or modules in cancer with the focus on mutual exclusivity-based ones.

Entities:  

Mesh:

Year:  2016        PMID: 28113329     DOI: 10.1109/TCBB.2016.2640963

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  13 in total

1.  Discovery of cancer common and specific driver gene sets.

Authors:  Junhua Zhang; Shihua Zhang
Journal:  Nucleic Acids Res       Date:  2017-06-02       Impact factor: 16.971

2.  De novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet.

Authors:  Sebastian Winkler; Ivana Winkler; Mirjam Figaschewski; Thorsten Tiede; Alfred Nordheim; Oliver Kohlbacher
Journal:  BMC Bioinformatics       Date:  2022-04-19       Impact factor: 3.307

3.  Identification of driver modules in pan-cancer via coordinating coverage and exclusivity.

Authors:  Bo Gao; Guojun Li; Juntao Liu; Yang Li; Xiuzhen Huang
Journal:  Oncotarget       Date:  2017-05-30

4.  Identifying Cancer Specific Driver Modules Using a Network-Based Method.

Authors:  Feng Li; Lin Gao; Peizhuo Wang; Yuxuan Hu
Journal:  Molecules       Date:  2018-05-08       Impact factor: 4.411

5.  Adaptively Weighted and Robust Mathematical Programming for the Discovery of Driver Gene Sets in Cancers.

Authors:  Xiaolu Xu; Pan Qin; Hong Gu; Jia Wang; Yang Wang
Journal:  Sci Rep       Date:  2019-04-11       Impact factor: 4.379

6.  Identifying Mutually Exclusive Gene Sets with Prognostic Value and Novel Potential Driver Genes in Patients with Glioblastoma.

Authors:  Qian Gao; Yan Cui; Yanan Shen; Yanyan Li; Xue Gao; Yanfeng Xi; Tong Wang
Journal:  Biomed Res Int       Date:  2019-11-05       Impact factor: 3.411

7.  An Effective Graph Clustering Method to Identify Cancer Driver Modules.

Authors:  Wei Zhang; Yifu Zeng; Lei Wang; Yue Liu; Yi-Nan Cheng
Journal:  Front Bioeng Biotechnol       Date:  2020-04-07

8.  Ranking cancer drivers via betweenness-based outlier detection and random walks.

Authors:  Cesim Erten; Aissa Houdjedj; Hilal Kazan
Journal:  BMC Bioinformatics       Date:  2021-02-10       Impact factor: 3.169

9.  Prioritizing Cancer Genes Based on an Improved Random Walk Method.

Authors:  Pi-Jing Wei; Fang-Xiang Wu; Junfeng Xia; Yansen Su; Jing Wang; Chun-Hou Zheng
Journal:  Front Genet       Date:  2020-04-28       Impact factor: 4.599

10.  MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks.

Authors:  Ying Hui; Pi-Jing Wei; Junfeng Xia; Yu-Tian Wang; Chun-Hou Zheng
Journal:  BMC Med Genomics       Date:  2019-12-30       Impact factor: 3.063

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.