Automated Training for Algorithms That Learn from Genomic Data.
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 inc...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 10 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/28/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109273211&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109273211 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/28/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109273211 109273211 109273211 10.1155/2015/234236 109273211 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Automated Training for Algorithms That Learn from Genomic Data. aug: au: Cilingir, Gokcen Broschat, Shira L. affil: School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA sug: subj: Algorithms Genomics Human Research Methodology ab: 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generally, these updates are not incorporated into published machine learning algorithms which thereby can become outdated soon after their introduction.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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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