ArticlePloS one2013
Network and data integration for biomarker signature discovery via network smoothed T-statistics.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed.
- Identification of Prognostic Biomarkers in Gene Expression Profile of Neuroblastoma Via Machine Learning.Pediatric discovery · 2025Article
- Potential Mechanisms Underlying the Minimal Impact of Cry1Ab1 Protein onInternational journal of molecular sciences · 2025Article
- CTRR-ncRNA: A Knowledgebase for Cancer Therapy Resistance and Recurrence Associated Non-coding RNAs.Genomics, proteomics & bioinformatics · 2023Article
- Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma.Scientific reports · 2022Article
- Article
- Feature selection and classification over the network with missing node observations.Statistics in medicine · 2022Article
- EARN: an ensemble machine learning algorithm to predict driver genes in metastatic breast cancer.BMC medical genomics · 2021Article
- The effect of statistical normalization on network propagation scores.Bioinformatics (Oxford, England) · 2021Article
- Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.Frontiers in microbiology · 2021Review
- Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach.Scientific reports · 2020Article
- On fusion methods for knowledge discovery from multi-omics datasets.Computational and structural biotechnology journal · 2020Review
- Network Diffusion Promotes the Integrative Analysis of Multiple Omics.Frontiers in genetics · 2020Review
- DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays.Bioinformatics (Oxford, England) · 2019Article
- A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.PLoS computational biology · 2019Article
- IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species.Nucleic acids research · 2019Article
- Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data.Frontiers in genetics · 2019Article
- EMT network-based feature selection improves prognosis prediction in lung adenocarcinoma.PloS one · 2019Article
- Evaluation of Pathway Activation for a Single Sample Toward Inflammatory Bowel Disease Classification.Frontiers in genetics · 2019Article
- Pathway-based subnetworks enable cross-disease biomarker discovery.Nature communications · 2018Article
- Integration of gene interaction information into a reweighted random survival forest approach for accurate survival prediction and survival biomarker discovery.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predictive, stable and interpretable gene signatures are generally seen as an important step towards a better personalized medicine. During the last decade various methods have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinics is the typical low reproducibility of signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. We here propose a technique that integrates network information as well as different kinds of experimental data (here exemplified by mRNA and miRNA expression) into one classifier. This is done by smoothing t-statistics of individual genes or miRNAs over the structure of a combined protein-protein interaction (PPI) and miRNA-target gene network. A permutation test is conducted to select features in a highly consistent manner, and subsequently a Support Vector Machine (SVM) classifier is trained. Compared to several other competing methods our algorithm reveals an overall better prediction performance for early versus late disease relapse and a higher signature stability. Moreover, obtained gene lists can be clearly associated to biological knowledge, such as known disease genes and KEGG pathways. We demonstrate that our data integration strategy can improve classification performance compared to using a single data source only. Our method, called stSVM, is available in R-package netClass on CRAN (http://cran.r-project.org).
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Registered trials
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 OpenQuestion graph.