ArticleScientific reports2015
Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 26 citations in OpenAlex.
- Gene co-expression network analysis revealed novel biomarkers for ovarian cancer.Frontiers in genetics · 2022Article
- EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer.BMC medical genomics · 2021Article
- Identifying and ranking potential driver genes of Alzheimer's disease using multiview evidence aggregation.Bioinformatics (Oxford, England) · 2019Article
- DNArCdb: A database of cancer biomarkers in DNA repair genes that includes variants related to multiple cancer phenotypes.DNA repair · 2018Article
- Integrating omics data and protein interaction networks to prioritize driver genes in cancer.Oncotarget · 2017Article
- Differential analysis between somatic mutation and germline variation profiles reveals cancer-related genes.Genome medicine · 2017Article
- Utilizing somatic mutation data from numerous studies for cancer research: proof of concept and applications.Oncogene · 2017Article
- NCG 5.0: updates of a manually curated repository of cancer genes and associated properties from cancer mutational screenings.Nucleic acids research · 2016Article
- Mutated Pathways as a Guide to Adjuvant Therapy Treatments for Breast Cancer.Molecular cancer therapeutics · 2016Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
The number of mutated genes in cancer cells is far larger than the number of mutations that drive cancer. The difficulty this creates for identifying relevant alterations has stimulated the development of various computational approaches to distinguishing drivers from bystanders. We develop and apply an ensemble classifier (EC) machine learning method, which integrates 10 classifiers that are publically available, and apply it to breast and ovarian cancer. In particular we find the following: (1) Using both standard and non-standard metrics, EC almost always outperforms single method classifiers, often by wide margins. (2) Of the 50 highest ranked genes for breast (ovarian) cancer, 34 (30) are associated with other cancers in either the OMIM, CGC or NCG database (P < 10(-22)). (3) Another 10, for both breast and ovarian cancer, have been identified by GWAS studies. (4) Several of the remaining genes--including a protein kinase that regulates the Fra-1 transcription factor which is overexpressed in ER negative breast cancer cells; and Fyn, which is overexpressed in pancreatic and prostate cancer, among others--are biologically plausible. Biological implications are briefly discussed. Source codes and detailed results are available at http://www.visantnet.org/misi/driver_integration.zip.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.