Machine learning approaches for predicting genotype from phenotype and a novel clustering technique for subgenotype discovery: an application to inherited deafness

• Main Author: Taylor, Kyle Ross
• Other Authors: Casavant, Thomas L.
• Format: Others
• Language: English
• Published: University of Iowa 2014
• Subjects: Bioinformatics; Hearing Loss; Machine Learning; Electrical and Computer Engineering
• Online Access: https://ir.uiowa.edu/etd/1404; https://ir.uiowa.edu/cgi/viewcontent.cgi?article=5443&context=etd

This thesis describes a method, software tool, and web-based service called AudioGene, which can be used to predict genotype from phenotype in patients with inherited forms of hearing loss. To enhance the effectiveness of this prediction facility, a novel clustering technique was developed called Hierarchal Surface Clustering (HSC), which allows existing phenotype data to drive the discovery of new disease subtypes and their genotypes. The accuracy of AudioGene for predicting the top three candidate loci was 68% when using a multi-instance support vector machine, compared to 44% using a Majority classifier for Autosomal Dominant Non-syndromic Hearing loss (ADNSHL). The method was extended to predict the mutation type for patients with mutations in the Autosomal Recessive Non-syndromic Hearing Loss locus DFNB1, and had an accuracy of 83% compared to 50% for a Majority classifier. Along with HSC, a novel visualization technique was developed to plot the progression of the hearing loss with age in 3D as surfaces. Simulated datasets were used along with actual clinical data to evaluate the performance of HSC and compare it to other clustering techniques. When evaluating using the clinical data, HSC had the highest Adjusted Rand Index with a value of 0.459 compared to 0.187 for spectral clustering and 0.103 for K-means clustering.