Pathway and Network Analysis of Cancer Genomes
By Pau Creixell, Jüri Reimand, Syed Haider, Guanming Wu, T. Shibata, M. Vázquez, Ville Mustonen, Abel Gonzalez-Perez, J. Pearson, Christopher Sander and 10 more. Nature Methods, 2015
319 citations recorded by the source corpus.
Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/f3eafefed88ff646ec6404e77897d3db9a2fa89f
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In Molecular Biology
- Wanding Zhou
- P. Park
- Hui Shen
- Peter W. Laird
- Elaine R. Mardis
- Ilya Shmulevich
- Niranjan Nagarajan
- Katherine A. Hoadley
Citations in the corpus
- Comprehensive Characterization of Cancer Driver Genes and Mutations (2018, background): “…of many bioinformatic algorithms oriented toward discovery, characterization, and prioritization of cellular processes driving cancer based on pathways (Creixell et al., 2015), genes (Ding et al., 2014), or individual variations (Gonzalez-Perez et al., 2013) (Key Resources Table; STAR Methods).”
- g:Profiler—a web server for functional interpretation of gene lists (2016 update) (2016, unknown): “g:Profiler is a recommended tool for interpreting cancer genomes with pathway information (49).”