Conference proceeding
Inferring Functional Groups from Microbial Gene Catalogue with Probabilistic Topic Models
2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM 2011), pp 3-9
01 Jan 2011
Abstract
In this paper, based on the functional elements derived from non-redundant CDs catalogue, we show that the configuration of functional groups in meta-genome samples can be inferred by probabilistic topic modeling. The probabilistic topic modeling is a Bayesian method that is able to extract useful topical information from unlabeled data. When used to study microbial samples (assuming that relative abundance of functional elements is already obtained by a homology-based approach), each sample can be considered as a 'document', which has a mixture of functional groups, while each functional group (also known as a 'latent topic') is a weight mixture of functional elements (including taxonomic levels, and indicators of gene orthologous groups and KEGG pathway mappings). The functional elements bear an analogy with 'words'. Estimating the probabilistic topic model can uncover the configuration of functional groups (the latent topic) in each sample. The experimental results demonstrate the effectiveness of our proposed method.
Metrics
Details
- Title
- Inferring Functional Groups from Microbial Gene Catalogue with Probabilistic Topic Models
- Creators
- Xin Chen - Drexel University, Radiation Oncology (and Nuclear Medicine)TingTing He - Central China Normal UniversityXiaohua Hu - Drexel University, Information ScienceYuan An - Drexel University, Information ScienceXindong Wu - Univ Vermont, Dept Comp Sci, Burlington, VT USA
- Publication Details
- 2011 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM 2011), pp 3-9
- Series
- IEEE International Conference on Bioinformatics and Biomedicine-BIBM
- Publisher
- IEEE
- Number of pages
- 7
- Grant note
- 90920005 / NSFC; National Natural Science Foundation of China (NSFC)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science; Radiation Oncology (and Nuclear Medicine)
- Web of Science ID
- WOS:000411330600001
- Scopus ID
- 2-s2.0-84862969635
- Other Identifier
- 991019167330604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Medical Informatics