ArticleBioinformatics (Oxford, England)2017
JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.Nature communications · 2023Article
- Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications.Cancers · 2023Review
- Using machine learning to identify gene interaction networks associated with breast cancer.BMC cancer · 2022Article
- Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity.Biostatistics (Oxford, England) · 2022Article
- Integrated Multi-Omics Analysis Model to Identify Biomarkers Associated With Prognosis of Breast Cancer.Frontiers in oncology · 2022Article
- Brain connectivity alteration detection via matrix-variate differential network model.Biometrics · 2021Article
- Disparity-filtered differential correlation network analysis: a case study on CRC metabolomics.Journal of integrative bioinformatics · 2021Article
- SeqNet: An R Package for Generating Gene-Gene Networks and Simulating RNA-Seq Data.Journal of statistical software · 2021Article
- Article
- Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.International journal of molecular sciences · 2020Review
- DNF: A differential network flow method to identify rewiring drivers for gene regulatory networks.Neurocomputing · 2020Article
- Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches.Briefings in bioinformatics · 2019Review
- Integrating gene regulatory pathways into differential network analysis of gene expression data.Scientific reports · 2019Article
- A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction.Frontiers in neurology · 2019Article
- A new insight into underlying disease mechanism through semi-parametric latent differential network model.BMC bioinformatics · 2018Article
- Article
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Authors and funding
6 authors.
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Abstract
motivationA complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application.
resultsWe propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data. AVAILABILITY AND IMPLEMENTATION: R scripts available at https://github.com/jijiadong/JDINAC. CONTACT: lxie@iscb.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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