ArticleBMC bioinformatics2016
LNDriver: identifying driver genes by integrating mutation and expression data based on gene-gene interaction network.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 39 citations in OpenAlex.
- Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.PLoS computational biology · 2025Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network.BMC bioinformatics · 2022Article
- Pan-Cancer Bioinformatics Analysis of Gene UBE2C.Frontiers in genetics · 2022Article
- Identification of driver genes based on gene mutational effects and network centrality.BMC bioinformatics · 2021Article
- A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs.BMC bioinformatics · 2021Article
- Ranking cancer drivers via betweenness-based outlier detection and random walks.BMC bioinformatics · 2021Article
- A Novel Computational Framework to Predict Disease-Related Copy Number Variations by Integrating Multiple Data Sources.Frontiers in genetics · 2021Article
- Developing Computational Model to Predict Protein-Protein Interaction Sites Based on the XGBoost Algorithm.International journal of molecular sciences · 2020Article
- MECoRank: cancer driver genes discovery simultaneously evaluating the impact of SNVs and differential expression on transcriptional networks.BMC medical genomics · 2019Article
- Identifying driver genes involving gene dysregulated expression, tissue-specific expression and gene-gene network.BMC medical genomics · 2019Article
- Semi-supervised prediction of protein interaction sites from unlabeled sample information.BMC bioinformatics · 2019Article
- LBMC bioinformatics · 2019Article
- A random walk-based method to identify driver genes by integrating the subcellular localization and variation frequency into bipartite graph.BMC bioinformatics · 2019Article
- Article
- Driver pattern identification over the gene co-expression of drug response in ovarian cancer by integrating high throughput genomics data.Scientific reports · 2017Article
- Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer is a complex disease which is characterized by the accumulation of genetic alterations during the patient's lifetime. With the development of the next-generation sequencing technology, multiple omics data, such as cancer genomic, epigenomic and transcriptomic data etc., can be measured from each individual. Correspondingly, one of the key challenges is to pinpoint functional driver mutations or pathways, which contributes to tumorigenesis, from millions of functional neutral passenger mutations.
resultsIn this paper, in order to identify driver genes effectively, we applied a generalized additive model to mutation profiles to filter genes with long length and constructed a new gene-gene interaction network. Then we integrated the mutation data and expression data into the gene-gene interaction network. Lastly, greedy algorithm was used to prioritize candidate driver genes from the integrated data. We named the proposed method Length-Net-Driver (LNDriver).
conclusionsExperiments on three TCGA datasets, i.e., head and neck squamous cell carcinoma, kidney renal clear cell carcinoma and thyroid carcinoma, demonstrated that the proposed method was effective. Also, it can identify not only frequently mutated drivers, but also rare candidate driver genes.
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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 Socratic graph.