Detection of candidate tumor driver genes using a fully integrated Bayesian approach

J Yang, X Wang, M Kim, Y Xie, G Xiao - Statistics in medicine, 2014 - Wiley Online Library
DNA copy number alterations (CNAs), including amplifications and deletions, can result in
significant changes in gene expression and are closely related to the development and …

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 …

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 …

Driver gene detection through Bayesian network integration of mutation and expression profiles

Z Chen, Y Lu, B Cao, W Zhang, A Edwards… - …, 2022 - academic.oup.com
Motivation The identification of mutated driver genes and the corresponding pathways is one
of the primary goals in understanding tumorigenesis at the patient level. Integration of multi …

A Bayesian framework for integrating copy number and gene expression data

Y Ji, F Trentini, P Mueller - Advances in Statistical Bioinformatics …, 2013 - books.google.com
Diverse types of cancer genomics data are being collected widely and rapidly with the aim to
systemically examine the origin and dynamics of different diseases. An important premise is …

Modeling the altered expression levels of genes on signaling pathways in tumors as causal bayesian networks

R Neapolitan, D Xue, X Jiang - Cancer informatics, 2014 - journals.sagepub.com
This paper concerns a study indicating that the expression levels of genes in signaling
pathways can be modeled using a causal Bayesian network (BN) that is altered in tumorous …

Non-linear Bayesian framework to determine the transcriptional effects of cancer-associated genomic aberrations

A Razi, N Banerjee, N Dimitrova… - 2015 37th Annual …, 2015 - ieeexplore.ieee.org
While the tumorigenic effects of specific recurrent mutations in known cancer driver-genes is
well-characterized, not much is known about the functional relevance of the vast majority of …

[HTML][HTML] A Novel Bayesian Framework Infers Driver Activation States and Reveals Pathway-Oriented Molecular Subtypes in Head and Neck Cancer

Z Liu, C Cai, X Ma, J Liu, L Chen, VWY Lui, GF Cooper… - Cancers, 2022 - mdpi.com
Simple Summary Numerous factors, such as genomic mutations, chromosomal changes,
transcriptional controls, phosphorylation, and protein–protein interactions, among others …

[HTML][HTML] Pan-cancer and single-cell modeling of genomic alterations through gene expression

D Mercatelli, F Ray, FM Giorgi - Frontiers in genetics, 2019 - frontiersin.org
Cancer is a disease often characterized by the presence of multiple genomic alterations,
which trigger altered transcriptional patterns and gene expression, which in turn sustain the …

Tumor-specific causal inference (tci): A bayesian method for identifying causative genome alterations within individual tumors

G Cooper, C Cai, X Lu - bioRxiv, 2017 - biorxiv.org
Precision medicine for cancer involves identifying and targeting the somatic genome
alterations (SGAs) that drive the development of an individual tumor. Much of current efforts …