MADGiC: a model-based approach for identifying driver genes in cancer

KD Korthauer, C Kendziorski - Bioinformatics, 2015 - academic.oup.com
Motivation: Identifying and prioritizing somatic mutations is an important and challenging
area of cancer research that can provide new insights into gene function as well as new …

[HTML][HTML] Comprehensive assessment of computational algorithms in predicting cancer driver mutations

H Chen, J Li, Y Wang, PKS Ng, YH Tsang, KR Shaw… - Genome biology, 2020 - Springer
Background The initiation and subsequent evolution of cancer are largely driven by a
relatively small number of somatic mutations with critical functional impacts, so-called driver …

[HTML][HTML] Finding driver mutations in cancer: Elucidating the role of background mutational processes

AL Brown, M Li, A Goncearenco… - PLoS computational …, 2019 - journals.plos.org
Identifying driver mutations in cancer is notoriously difficult. To date, recurrence of a mutation
in patients remains one of the most reliable markers of mutation driver status. However …

Evaluating the evaluation of cancer driver genes

CJ Tokheim, N Papadopoulos… - Proceedings of the …, 2016 - National Acad Sciences
Sequencing has identified millions of somatic mutations in human cancers, but
distinguishing cancer driver genes remains a major challenge. Numerous methods have …

Comprehensive evaluation of computational methods for predicting cancer driver genes

X Shi, H Teng, L Shi, W Bi, W Wei… - Briefings in …, 2022 - academic.oup.com
Optimal methods could effectively improve the accuracy of predicting and identifying
candidate driver genes. Various computational methods based on mutational frequency …

Machine learning methods for prediction of cancer driver genes: a survey paper

R Andrades… - Briefings in …, 2022 - academic.oup.com
Identifying the genes and mutations that drive the emergence of tumors is a critical step to
improving our understanding of cancer and identifying new directions for disease diagnosis …

[HTML][HTML] An evolutionary approach for identifying driver mutations in colorectal cancer

J Foo, LL Liu, K Leder, M Riester, Y Iwasa… - PLoS computational …, 2015 - journals.plos.org
The traditional view of cancer as a genetic disease that can successfully be treated with
drugs targeting mutant onco-proteins has motivated whole-genome sequencing efforts in …

[HTML][HTML] A new machine learning method for cancer mutation analysis

M Habibi, G Taheri - PLoS computational biology, 2022 - journals.plos.org
It is complicated to identify cancer-causing mutations. The recurrence of a mutation in
patients remains one of the most reliable features of mutation driver status. However, some …

Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework

H Yang, Q Wei, X Zhong, H Yang, B Li - Bioinformatics, 2017 - academic.oup.com
Motivation Comprehensive catalogue of genes that drive tumor initiation and progression in
cancer is key to advancing diagnostics, therapeutics and treatment. Given the complexity of …

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

M Nourbakhsh, K Degn, A Saksager… - Briefings in …, 2024 - academic.oup.com
The vast amount of available sequencing data allows the scientific community to explore
different genetic alterations that may drive cancer or favor cancer progression. Software …