A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

By Emidio Capriotti, Russ B. Altman. 2011

86 citations recorded by the source corpus.

High-throughput genotyping and sequencing techniques are rapidly and inexpensively providing large amounts of human genetic variation data. Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability and have been implicated in several human diseases, including cancer. Amino acid mutations resulting from non-synonymous SNPs in coding regions may generate protein functional changes that affect cell proliferation. In this study, we developed a machine learning approach to predict cancer-causing missense variants. We present a Support Vector Machine (SVM) classifier trained on a set of 3163 cancer-causing variants and an equal number of neutral polymorphisms. The method achieve 93% overall accuracy, a correlation coefficient of 0.86, and area under ROC curve of 0.98. When compared with other previously developed algorithms such as SIFT and CHASM our method results in higher prediction accuracy and correlation coefficient in identifying cancer-causing variants.

Subfields: Cancer Research, Genetics

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Citations in the corpus

Collective judgment predicts disease-associated single nucleotide variants (2013, background): “In the last decade, several algorithms have been developed to predict disease-related and functionally deleterious variants [11-21].”
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation (2013, methodology): “These methods are mainly based on statistical and/or machine learning approaches that take as input information from protein sequence [18,19,23,24,26,28,29], structure [20,25] and function [19,21,27].”
Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models (2012, methodology): “The performances observed within Align-GVGD (gene-speciﬁc alignments) suggest that there may be some beneﬁt in incorporating disease-speciﬁc weightings into our algorithm, for example, cancer-speciﬁc weightings similar to those employed by Capriotti and Altman (2011).”