K-Profiles: A Nonlinear Clustering Method for Pattern Detection in High Dimensional Data.

ABSTRACT: With modern technologies such as microarray, deep sequencing, and liquid chromatography-mass spectrometry (LC-MS), it is possible to measure the expression levels of thousands of genes/proteins simultaneously to unravel important biological processes. A very first step towards elucidating hidden patterns and understanding the massive data is the application of clustering techniques. Nonlinear relations, which were mostly unutilized in contrast to linear correlations, are prevalent in high-throughput data. In many cases, nonlinear relations can model the biological relationship more precisely and reflect critical patterns in the biological systems. Using the general dependency measure, Distance Based on Conditional Ordered List (DCOL) that we introduced before, we designed the nonlinear K-p

SUBMITTER: Wang K 

PROVIDER: S-EPMC4538770 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature