Enhanced position weight matrices using mixture models
نویسندگان
چکیده
منابع مشابه
Enhanced position weight matrices using mixture models
MOTIVATION Positional weight matrix (PWM) is derived from a set of experimentally determined binding sites. Here we explore whether there exist subclasses of binding sites and if the mixture of these subclass-PWMs can improve the binding site prediction. Intuitively, the subclasses correspond to either distinct binding preference of the same transcription factor in different contexts or distinc...
متن کاملConstruction of microRNA functional families by a mixture model of position weight matrices
MicroRNAs (miRNAs) are small regulatory molecules that repress the translational processes of their target genes by binding to their 3' untranslated regions (3' UTRs). Because the target genes are predominantly determined by their sequence complementarity to the miRNA seed regions (nucleotides 2-7) which are evolutionarily conserved, it is inferred that the target relationships and functions of...
متن کاملLarge Scale Matching for Position Weight Matrices
This paper addresses the problem of multiple pattern matching for motifs encoded by Position Weight Matrices. We first present an algorithm that uses a multi-index table to preprocess the set of motifs, allowing a dramatically decrease of computation time. We then show how to take benefit from simlar motifs to prevent useless computations.
متن کاملDiscriminative mixture weight estimation for large Gaussian mixture models
This paper describes a new approach to acoustic mod-eling for large vocabulary continuous speech recognition (LVCSR) systems. Each phone is modeled with a large Gaussian mixture model (GMM) whose context-dependent mixture weights are estimated with a sentence-level discrim-inative training criterion. The estimation problem is casted in a neural network framework, which enables the incorporation...
متن کاملContext-specific independence mixture modeling for positional weight matrices
MOTIVATION A positional weight matrix (PWM) is a statistical representation of the binding pattern of a transcription factor estimated from known binding site sequences. Previous studies showed that for factors which bind to divergent binding sites, mixtures of multiple PWMs increase performance. However, estimating a conventional mixture distribution for each position will in many cases cause ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2005
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/bti1001