Now showing items 1-4 of 4
A general approach for discriminative de novo motif discovery from high-throughput data
German conference on bioinformatics 2014
High-throughput techniques like ChIP-seq, ChIP-exo, and protein binding microarrays (PBMs) demand for novel de novo motif discovery approaches that focus on accuracy and runtime on large data sets. While specialized algorithms have been designed for discovering motifs in in-vivo ChIP-seq/ChIP-exo or in in-vitro PBM data, ...
Utilizing promoter pair orientations for HMM-based analysis of ChIP-chip data
German Conference on Bioinformatics
Array-based analysis of chromatin immunoprecipitation data (ChIP-chip) is a powerful technique for identifying DNA target regions of individual transcription factors. Here, we present three approaches, a standard log-fold-change analysis (LFC), a basic method based on a Hidden Markov Model (HMM), and an ex- tension of ...
Supervised posteriors for DNA-motif classification
German conference on bioinformatics – GCB 2007
Markov models have been proposed for the classification of DNA-motifs using generative approaches for parameter learning. Here, we propose to apply the discriminative paradigm for this problem and study two different priors to facilitate parameter estimation using the maximum supervised posterior. Considering seven sets ...
Predicting miRNA targets utilizing an extended profile HMM
German Conference on Bioinformatics 2010
The regulation of many cellular processes is influenced by miRNAs, and bioinformatics approaches for predicting miRNA targets evolve rapidly. Here, we propose conditional profile HMMs that learn rules of miRNA-target site interaction automatically from data. We demonstrate that conditional profile HMMs detect the rules ...