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. 2011 Oct;98(4):310-7.
doi: 10.1016/j.ygeno.2011.06.010. Epub 2011 Jul 7.

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

Emidio Capriotti  1 , Russ B Altman
Affiliations

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

Emidio Capriotti et al. Genomics. 2011 Oct.

Abstract

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.

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Figures

Fig. 1
Fig. 1
Performance of SPF-Cancer method. ROC curve of SPF-Cancer method on CNO and CND (panel A) on CNO dataset and Consensus and Not Consensus subsets (panel B).In panels C, ROC curves of SIFT, CHASM, SPF-All and SPF-Cancer on the Synthetic dataset. Plot of the accuracy (Q2), correlation coefficient (C) and percentage of the dataset (DB) as a function of the reliability index (RI) for SPF-Cancer method on CNO dataset (panel D) and Consensus (panel E) and Not Consensus subsets (panel F).
Fig. 2
Fig. 2
Distributions of the Conservation Index and LGO on CNO dataset. Boxplot of the distributions for the Conservation Index (Panel A) and LGO scores (panel B) on CNO and Consensus and NotConsensus subset respectively for cancer-causing (Disease) and neutral variants (Neutral).
Fig. 3
Fig. 3
General and cancer-specific LGO scores. Scatter plot of the generic vs the cancer-specific LGO scores (LGO[All] and LGO[Cancer]) for each GO slim term (panel A). Color scale is related to the value of LGO[Cancer]-LGO[ALL]. In panel B, zoom of the plot in the region of LGO scores between −5 and 5.
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