ArticleBiostatistics (Oxford, England)2017
Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization.
Article in Biostatistics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Multi-Omics-Guided Design and Safety Engineering of Nucleic Acid Therapeutics: From Molecular Perturbation to Predictive Toxicology and Precision Translation.Chemical biology & drug design · 2026Review
- Metabolic reprogramming in sepsis-associated encephalopathy: emerging mechanisms, candidate biomarkers, and future therapeutic directions.Frontiers in medicine · 2026Review
- Augmented kurtosis-based projection pursuit: a novel, advanced machine learning approach for multi-omics data analysis and integration.Nucleic acids research · 2025Article
- NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction.Briefings in bioinformatics · 2024Article
- A Sparse Mixture-of-Experts Model With Screening of Genetic Associations to Guide Disease Subtyping.Frontiers in genetics · 2022Article
- Vertical integration methods for gene expression data analysis.Briefings in bioinformatics · 2021Review
- Machine learning analysis of TCGA cancer data.PeerJ. Computer science · 2021Article
- Integration of Proteomics and Other Omics Data.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Integrative approaches for analysis of mRNA and microRNA high-throughput data.Computational and structural biotechnology journal · 2021Review
- Article
- Review
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
- CEPICS: A Comparison and Evaluation Platform for Integration Methods in Cancer Subtyping.Frontiers in genetics · 2019Article
- Approaches to the assessment of severe asthma: barriers and strategies.Journal of asthma and allergy · 2019Article
- Integrative Deep Learning for Identifying Differentially Expressed (DE) Biomarkers.Computational and mathematical methods in medicine · 2019Article
- Bayesian integrative model for multi-omics data with missingness.Bioinformatics (Oxford, England) · 2018Article
- Generalized Bayesian Factor Analysis for Integrative Clustering with Applications to Multi-Omics Data.Proceedings of the ... International Conference on Data Science and Advanced Analytics. IEEE International Conference on Data Science and Advanced Analytics · 2018Article
- Precision medicine in COPD: where are we and where do we need to go?European respiratory review : an official journal of the European Respiratory Society · 2018Review
- Node-Structured Integrative Gaussian Graphical Model Guided by Pathway Information.Computational and mathematical methods in medicine · 2017Article
- Meta-analytic support vector machine for integrating multiple omics data.BioData mining · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
With the rapid advances in technologies of microarray and massively parallel sequencing, data of multiple omics sources from a large patient cohort are now frequently seen in many consortium studies. Effective multi-level omics data integration has brought new statistical challenges. One important biological objective of such integrative analysis is to cluster patients in order to identify clinically relevant disease subtypes, which will form basis for tailored treatment and personalized medicine. Several methods have been proposed in the literature for this purpose, including the popular iCluster method used in many cancer applications. When clustering high-dimensional omics data, effective feature selection is critical for better clustering accuracy and biological interpretation. It is also common that a portion of "scattered samples" has patterns distinct from all major clusters and should not be assigned into any cluster as they may represent a rare disease subcategory or be in transition between disease subtypes. In this paper, we firstly propose to improve feature selection of the iCluster factor model by an overlapping sparse group lasso penalty on the omics features using prior knowledge of inter-omics regulatory flows. We then perform regularization over samples to allow clustering with scattered samples and generate tight clusters. The proposed group structured tight iCluster method will be evaluated by two real breast cancer examples and simulations to demonstrate its improved clustering accuracy, biological interpretation, and ability to generate coherent tight clusters.
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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.