Genome mining using machine learning techniques

Wlodarczak, Peter and Soar, Jeffrey and Ally, Mustafa (2015) Genome mining using machine learning techniques. In: 13th International Conference on Smart Homes and Health Telematics: Inclusive Smart Cities and e-Health (ICOST 2015), 10-12 June 2015, Geneva, Switzerland.

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Abstract

A major milestone in modern biology was the complete sequencing of the human genome. But it produced a whole set of new challenges in exploring the functions and interactions of different parts of the genome. One application is predicting disorders based on mining the genotype and understanding how the interactions between genetic loci lead to certain human diseases.

However, typically disease phenotypes are genetically complex. They are characterized by large, high-dimensional data sets. Also, usually the sample size is small.

Recently machine learning and predictive modeling approaches have been successfully applied to understand the genotype-phenotype relations and link them to human diseases. They are well suited to overcome the problems of the large data sets produced by the human genome and its high-dimensionality. Machine learning techniques have been applied in virtually all data mining domains and have proven to be effective in BioData mining as well.

This paper describes some of the techniques that have been adopted in recent studies in human genome analysis.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Access to Accepted version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 23 Jun 2016 23:53
Last Modified: 03 Oct 2017 23:40
Uncontrolled Keywords: genome wide prediction; machine learning; cross validation; predictive medicine
Fields of Research : 08 Information and Computing Sciences > 0806 Information Systems > 080699 Information Systems not elsewhere classified
Socio-Economic Objective: C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified
Identification Number or DOI: 10.1007/978-3-319-19312-0_39
URI: http://eprints.usq.edu.au/id/eprint/27425

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