Document Type
Article
Publication Date
11-18-2005
Publication Title
BMC Bioinformatics
Department
Geisel School of Medicine
Abstract
The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD to a promoter matrix defined from regulatory DNA sequences, we derived a novel method to predict the critical set of TFBMs.
DOI
10.1186/1471-2105-6-276
Original Citation
Liu Y, Vincenti MP, Yokota H. Principal component analysis for predicting transcription-factor binding motifs from array-derived data. BMC Bioinformatics. 2005 Nov 18;6:276. doi: 10.1186/1471-2105-6-276. PMID: 16297243; PMCID: PMC1316881.
Dartmouth Digital Commons Citation
Liu, Yunlong; Vincenti, Matthew P; and Yokota, Hiroki, "Principal Component Analysis for Predicting Transcription-Factor Binding Motifs from Array-Derived Data" (2005). Dartmouth Scholarship. 573.
https://digitalcommons.dartmouth.edu/facoa/573
