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BioMed Research International
Volume 2015, Article ID 234236, 9 pages
http://dx.doi.org/10.1155/2015/234236
Research Article

Automated Training for Algorithms That Learn from Genomic Data

1School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA
2Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA 99164, USA
3Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA 99164, USA

Received 16 August 2014; Revised 9 November 2014; Accepted 21 November 2014

Academic Editor: Hesham H. Ali

Copyright © 2015 Gokcen Cilingir and Shira L. Broschat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Supervised machine learning algorithms are used by life scientists for a variety of objectives. Expert-curated public gene and protein databases are major resources for gathering data to train these algorithms. While these data resources are continuously updated, generally, these updates are not incorporated into published machine learning algorithms which thereby can become outdated soon after their introduction. In this paper, we propose a new model of operation for supervised machine learning algorithms that learn from genomic data. By defining these algorithms in a pipeline in which the training data gathering procedure and the learning process are automated, one can create a system that generates a classifier or predictor using information available from public resources. The proposed model is explained using three case studies on SignalP, MemLoci, and ApicoAP in which existing machine learning models are utilized in pipelines. Given that the vast majority of the procedures described for gathering training data can easily be automated, it is possible to transform valuable machine learning algorithms into self-evolving learners that benefit from the ever-changing data available for gene products and to develop new machine learning algorithms that are similarly capable.