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Journal article
Class Prediction and Feature Selection with Linear Optimization for Metagenomic Count Data
PloS one, v 8(3), pp e53253-e53253
26 Mar 2013
PMID: 23555553
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The amount of metagenomic data is growing rapidly while the computational methods for metagenome analysis are still in their infancy. It is important to develop novel statistical learning tools for the prediction of associations between bacterial communities and disease phenotypes and for the detection of differentially abundant features. In this study, we presented a novel statistical learning method for simultaneous association prediction and feature selection with metagenomic samples from two or multiple treatment populations on the basis of count data. We developed a linear programming based support vector machine with L-1 and joint L-1,L-infinity penalties for binary and multiclass classifications with metagenomic count data (metalinprog). We evaluated the performance of our method on several real and simulation datasets. The proposed method can simultaneously identify features and predict classes with the metagenomic count data.
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Details
- Title
- Class Prediction and Feature Selection with Linear Optimization for Metagenomic Count Data
- Creators
- Zhenqiu Liu - University of Maryland Marlene and Stewart Greenebaum Cancer CenterDechang Chen - Uniformed Services University of the Health SciencesLi Sheng - Drexel UniversityAmy Y. Liu - Brown University
- Publication Details
- PloS one, v 8(3), pp e53253-e53253
- Publisher
- Public Library Science
- Number of pages
- 7
- Grant note
- 1R03CA133899 / National Cancer Institute; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI) ADT-1220747; CCF-0729080 / National Science Foundation (NSF) R03CA133899 / NATIONAL CANCER INSTITUTE; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000317418500001
- Scopus ID
- 2-s2.0-84875440137
- Other Identifier
- 991019168762704721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Multidisciplinary Sciences