title: ANN-Spec: a method for discovering transcription factor binding sites with improved specificity.
abstract: This work describes ANN-Spec, a machine learning algorithm and its application to discovering un-gapped patterns in DNA sequence. The approach makes use of an Artificial Neural Network and a Gibbs sampling method to define the Specificity of a DNA-binding protein. ANN-Spec searches for the parameters of a simple network (or weight matrix) that will maximize the specificity for binding sequences of a positive set compared to a background sequence set. Binding sites in the positive data set are found with the resulting weight matrix and these sites are then used to define a local multiple sequence alignment. Training complexity is O(lN) where l is the width of the pattern and N is the size of the positive training data. A quantitative comparison of ANN-Spec and a few related programs is presented. The comparison shows that ANN-Spec finds patterns of higher specificity when training with a background data set. The program and documentation are available from the authors for UNIX systems.
authors.affiliation: Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark. workman@cbs.dtu.dk
authors.forename: C T
authors.initials: CT
authors.lastname: Workman
authors.forename: G D
authors.initials: GD
authors.lastname: Stormo
mesh_terms: D000465:Algorithms; D001665:Binding Sites / Q000235:genetics; D003198:Computer Simulation; D004247:DNA / Q000235:genetics / Q000378:metabolism; D005786:Gene Expression Regulation; D008954:Models, Biological; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity; D012984:Software; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors; D004247:DNA