ArticlePacific Symposium on Biocomputing. Pacific Symposium on Biocomputing2015
T-ReCS: stable selection of dynamically formed groups of features with application to prediction of clinical outcomes.
Article in Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed.
- Causal network perturbation analysis identifies known and novel type-2 diabetes driver genes.bioRxiv : the preprint server for biology · 2024Article
- Extending greedy feature selection algorithms to multiple solutions.Data mining and knowledge discovery · 2021Article
- Causal network perturbations for instance-specific analysis of single cell and disease samples.Bioinformatics (Oxford, England) · 2020Article
- Respiratory Microbiome Profiling for Etiologic Diagnosis of Pneumonia in Mechanically Ventilated Patients.Frontiers in microbiology · 2018Article
- Integrated Theory- and Data-driven Feature Selection in Gene Expression Data Analysis.Proceedings. International Conference on Data Engineering · 2017Article
- Learning mixed graphical models with separate sparsity parameters and stability-based model selection.BMC bioinformatics · 2016Article
- MicroRNA expression profiling predicts clinical outcome of carboplatin/paclitaxel-based therapy in metastatic melanoma treated on the ECOG-ACRIN trial E2603.Clinical epigenetics · 2015Article
- Article
- A Pipeline for Integrated Theory and Data-Driven Modeling of Biomedical Data.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Feature selection is used extensively in biomedical research for biomarker identification and patient classification, both of which are essential steps in developing personalized medicine strategies. However, the structured nature of the biological datasets and high correlation of variables frequently yield multiple equally optimal signatures, thus making traditional feature selection methods unstable. Features selected based on one cohort of patients, may not work as well in another cohort. In addition, biologically important features may be missed due to selection of other co-clustered features We propose a new method, Tree-guided Recursive Cluster Selection (T-ReCS), for efficient selection of grouped features. T-ReCS significantly improves predictive stability while maintains the same level of accuracy. T-ReCS does not require an a priori knowledge of the clusters like group-lasso and also can handle "orphan" features (not belonging to a cluster). T-ReCS can be used with categorical or survival target variables. Tested on simulated and real expression data from breast cancer and lung diseases and survival data, T-ReCS selected stable cluster features without significant loss in classification accuracy.
Indexed as
Identifiers
25592602PMC4299881What OpenQuestion holds
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.