Self-organizing neural networks to support the discovery of DNA-binding motifs.
Identification of the short DNA sequence motifs that serve as binding targets for transcription factors is an important challenge in bioinformatics. Unsupervised techniques from the statistical learning theory literature have often been applied to motif discovery, but effective solutions for large genomic datasets have yet to be found. We present here three self-organizing neural networks that have applicability to the motif-finding problem. The core system in this study is a previously described SOM-based motif-finder named SOMBRERO. The motif-finder is integrated in this work with a SOM-based method that automatically constructs generalized models for structurally related motifs and initializes SOMBRERO with relevant biological knowledge. A self-organizing tree method that displays the relationships between various motifs is also presented, and it is shown that such a method can act as an effective structural classifier of novel motifs. The performance of the three self-organizing neural networks is evaluated here using various datasets.
Mahony
Shaun
S
Department of Computational Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA. shaun.mahony@ccbb.pitt.edu
Benos
Panayiotis V
PV
Smith
Terry J
TJ
Golden
Aaron
A
D000465:Algorithms; D020816:Amino Acid Motifs / Q000502:physiology*; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology; D004247:DNA / Q000378:metabolism*; D030541:Databases, Genetic; D006801:Humans; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D017434:Protein Structure, Tertiary; D017422:Sequence Analysis, DNA
D004247:DNA