A machine learning framework for trait based genomics

Wei Zhang; Erliang Zeng; Dan Liu; S Jones; S Emrich

doi:10.1109/ICCABS.2012.6182648

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Conference proceeding

A machine learning framework for trait based genomics

Wei Zhang, Erliang Zeng, Dan Liu, S Jones and S Emrich

2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS), pp.1-6

02/2012

DOI: 10.1109/ICCABS.2012.6182648

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Abstract

Microbial communities perform many important ecological functions across a wide range of natural and man-made environments. Recently, the utility of trait based approaches for microbial communities has been identified. Increasing availability of whole genome sequences provide the opportunity to explore the genetic foundations of a variety of functional traits. In this paper, we proposed a machine learning framework to quantitatively link the genotype with functional traits. Genes from bacteria genomes belonging to different functional trait groups were grouped to Cluster of Orthologs (COGs), and were used as features. Then, TF-IDF technique from the text mining domain was applied to transform the data to accommodate the abundance and importance of each COG. After TF-IDF processing, COGs were ranked using feature selection methods to identify their relevance to the functional trait of interest. We focused on a binary functional trait in this paper, but plan to extend our approach to continuous functional traits in the future. Experimental results demonstrated that functional trait related genes can be detected using our method.

Bioinformatics

Ecology

Genomics

Machine Learning

Functional Trait

Communities

Microbial communities

Ortholog

Support vector machines

Feature Selection

Microorganisms

Accuracy

Sequencing

Details

Title: Subtitle: A machine learning framework for trait based genomics
Creators: Wei Zhang - Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, South Bend, IN, USA
Erliang Zeng - Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, South Bend, IN, USA
Dan Liu - Dept. of Biol. Sci., Univ. of Notre Dame, South Bend, IN, USA
S Jones - Dept. of Biol. Sci., Univ. of Notre Dame, South Bend, IN, USA
S Emrich - Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, South Bend, IN, USA
Resource Type: Conference proceeding
Publication Details: 2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS), pp.1-6
DOI: 10.1109/ICCABS.2012.6182648
Publisher: IEEE
Language: English
Date published: 02/2012
Academic Unit: Preventive and Community Dentistry; Roy J. Carver Department of Biomedical Engineering; Iowa Neuroscience Institute; Biostatistics; Dental Research
Record Identifier: 9984065373402771

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