M. Molla, J. Shavlik, T. Albert, T. Richmond & S. Smith (2004).
A Self-Tuning Method for One-Chip SNP Identification. Proceedings of the IEEE Conference on Computational Systems Bioinformatics, pp. 69-79, Stanford, CA.
Slides (PPT).

This publication is available in PDF and available in Microsoft Word.

The slides for this publication are available in Microsoft PowerPoint.

Abstract:

Current methods for interpreting oligonucleotide-based SNP-detection microarrays, SNP chips, are based on statistics and require extensive parameter tuning as well as extremely high-resolution images of the chip being processed. We present a method, based on a simple data-classification technique called nearest-neighbors that, on haploid organisms, produces results comparable to the published results of the leading statistical methods and requires very little in the way of parameter tuning. Furthermore, it can interpret SNP chips using lower-resolution scanners of the type more typically used in current microarray experiments. Along with our algorithm, we present the results of a SNP-detection experiment where, when independently applying this algorithm to six identical SARS SNP chips, we correctly identify all 24 SNPs in a particular strain of the SARS virus, with between 6 and 13 false positives across the six experiments.

Return to the publications of the Univ. of Wisconsin Machine Learning Research Group.