45 research outputs found
Effects of tadalafil once daily or on demand versus placebo on time to recovery of erectile function in patients after bilateral nerve-sparing radical prostatectomy
TRAIL Score:A Simple Model to Predict Immunochemotherapy Tolerability in Patients With Diffuse Large B-Cell Lymphoma
Prediction of breast cancer by profiling of urinary RNA metabolites using Support Vector Machine-based feature selection
Comparably high retention and low relapse rates in different subpopulations of bipolar patients in a German non-interventional study
Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors
Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy