ArticleNucleic acids research2013
Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model.
Article in Nucleic acids research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Learning Patient Similarity from Genomics for Precision Oncology.medRxiv : the preprint server for health sciences · 2025Article
- Developing a label propagation approach for cancer subtype classification problem.Turkish journal of biology = Turk biyoloji dergisi · 2022Article
- Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data.Briefings in bioinformatics · 2020Review
- Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data.Bioinformatics (Oxford, England) · 2020Article
- Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.Scientific reports · 2020Article
- Analysis of disease organ as a novel phenotype towards disease genetics understanding.Journal of biomedical informatics · 2019Article
- Enabling Precision Medicine through Integrative Network Models.Journal of molecular biology · 2018Review
- Moving towards a molecular taxonomy of autoimmune rheumatic diseases.Nature reviews. Rheumatology · 2018Review
- More Is Better: Recent Progress in Multi-Omics Data Integration Methods.Frontiers in genetics · 2017Review
- Understanding Genotype-Phenotype Effects in Cancer via Network Approaches.PLoS computational biology · 2016Review
- Patient Similarity: Emerging Concepts in Systems and Precision Medicine.Frontiers in physiology · 2016Article
- Heterogeneous glioblastoma cell cross-talk promotes phenotype alterations and enhanced drug resistance.Oncotarget · 2015Article
- Reassessing the Role of Intra-Arterial Drug Delivery for Glioblastoma Multiforme Treatment.Journal of drug delivery · 2015Review
- Integrative analyses of cancer data: a review from a statistical perspective.Cancer informatics · 2015Review
- Interaction-based discovery of functionally important genes in cancers.Nucleic acids research · 2014Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
One of the obstacles hindering a better understanding of cancer is its heterogeneity. However, computational approaches to model cancer heterogeneity have lagged behind. To bridge this gap, we have developed a new probabilistic approach that models individual cancer cases as mixtures of subtypes. Our approach can be seen as a meta-model that summarizes the results of a large number of alternative models. It does not assume predefined subtypes nor does it assume that such subtypes have to be sharply defined. Instead given a measure of phenotypic similarity between patients and a list of potential explanatory features, such as mutations, copy number variation, microRNA levels, etc., it explains phenotypic similarities with the help of these features. We applied our approach to Glioblastoma Multiforme (GBM). The resulting model Prob_GBM, not only correctly inferred known relationships but also identified new properties underlining phenotypic similarities. The proposed probabilistic framework can be applied to model relations between similarity of gene expression and a broad spectrum of potential genetic causes.
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Registered trials
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