ArticleBMC bioinformatics2018
Discovering mutated driver genes through a robust and sparse co-regularized matrix factorization framework with prior information from mRNA expression patterns and interaction network.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 30 citations in OpenAlex.
- Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification.BioData mining · 2024Article
- Identification of cancer driver genes based on hierarchical weak consensus model.Health information science and systems · 2024Article
- Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives.Healthcare (Basel, Switzerland) · 2023Review
- TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain.BMC bioinformatics · 2021Article
- Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning.Scientific reports · 2021Article
- GRMT: Generative Reconstruction of Mutation Tree From Scratch Using Single-Cell Sequencing Data.Frontiers in genetics · 2021Article
- Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer.Frontiers in genetics · 2021Article
- Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU.Scientific reports · 2020Article
- An Effective Graph Clustering Method to Identify Cancer Driver Modules.Frontiers in bioengineering and biotechnology · 2020Article
- Differentiating between cancer and normal tissue samples using multi-hit combinations of genetic mutations.Scientific reports · 2019Article
- Gene Expression-Based Predictive Markers for Paclitaxel Treatment in ER+ and ER- Breast Cancer.Frontiers in genetics · 2019Article
- Feature related multi-view nonnegative matrix factorization for identifying conserved functional modules in multiple biological networks.BMC bioinformatics · 2018Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDiscovery of mutated driver genes is one of the primary objective for studying tumorigenesis. To discover some relatively low frequently mutated driver genes from somatic mutation data, many existing methods incorporate interaction network as prior information. However, the prior information of mRNA expression patterns are not exploited by these existing network-based methods, which is also proven to be highly informative of cancer progressions.
resultsTo incorporate prior information from both interaction network and mRNA expressions, we propose a robust and sparse co-regularized nonnegative matrix factorization to discover driver genes from mutation data. Furthermore, our framework also conducts Frobenius norm regularization to overcome overfitting issue. Sparsity-inducing penalty is employed to obtain sparse scores in gene representations, of which the top scored genes are selected as driver candidates. Evaluation experiments by known benchmarking genes indicate that the performance of our method benefits from the two type of prior information. Our method also outperforms the existing network-based methods, and detect some driver genes that are not predicted by the competing methods.
conclusionsIn summary, our proposed method can improve the performance of driver gene discovery by effectively incorporating prior information from interaction network and mRNA expression patterns into a robust and sparse co-regularized matrix factorization framework.
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