Work

Convex-hull voting method on a large data set

Year: 2015

Type: article

Abstract: Background Genes work in concert as a system, not as independent entities, to mediate disease states. There has been considerable interest in understanding variations in molecular signatures between normal and disease states. The selective-voting convex-hull ensemble procedure accommodates molecular heterogeneity within and between groups and allows retrieval of sample-specific sets and investigation of variations in individual networks relevant to personalized medicine[1]. The work here describes using the convex-hull voting method on a large data set. Using parallelization techniques, we predict that we can execute the convex-hull voting algorithm on the University of Kentucky cluster (DLX) using a dataset much too large to run in a feasible time on a single machine.

Source: BMC Bioinformatics

DOI: 10.1186/1471-2105-16-s15-p2

Authors: Sally R Ellingson, Chi Wang, Radhakrishnan Nagarajan

Institutions: University of Kentucky, Markey Cancer Center

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,990

Funder: University of Kentucky