Sequential data selection for predicting the pathogenic effects of sequence variation

Mark F. Rogers, Colin Campbell, Hashem A. Shihab, Tom R. Gaunt, Matthew Mort, David N. Cooper

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)
311 Downloads (Pure)

Abstract

Recent improvements in sequencing technologies provide unprecedented opportunities to investigate the role of genetic variation in human disease. In previous work we have proposed a machine learning approach to predicting whether single nucleotide variants (SNVs) are functional or neutral in human disease. Many data sources from the Encyclopaedia of DNA Elements (ENCODE) may be relevant to this problem. To integrate these data sources, we applied integrative multiple kernel learning (MKL) that weights each source according to its relevance. Using an MKL optimization that yields sparse weights, we were able to eliminate the least informative data sources from our model. However, when selecting from a wide assortment of data sources, we have found that MKL may not be an efficient method for eliminating uninformative sources. Many data sources related to the human genome are incomplete: this can reduce dramatically the data available for training and the proportion of novel predictions that exploit all relevant sources. Here we introduce a greedy sequential selection method that assesses data sources in a structured fashion prior to MKL weight optimization. This method allows us to eliminate a majority of uninformative data sources prior to assigning kernel weights. When we use this method with our coding-region predictor, we select just five kernels for our final model, yielding increased accuracy over our previous model. In addition, by reducing the amount of data required for novel predictions, we are able to increase by five fold our model's coverage for new predictions.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages639-644
Number of pages6
ISBN (Print)9781467367981
DOIs
Publication statusPublished - 16 Dec 2015
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: 9 Nov 201512 Nov 2015

Conference

ConferenceIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
CountryUnited States
CityWashington
Period9/11/1512/11/15

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