Add to ORKGAbstract: The prediction of transcription factor binding sites is an important problem, since it reveals information about the transcriptional regulation of genes. A commonly used representation of these sites are position specific weight matrices which show weak predictive power. We introduce a feature-based modelling approach, which is able to deal with various kind of biological properties of binding sites and models them via Bayesian belief networks. The presented results imply higher model accuracy in contrast to the PSSM approach
Information about the binding preferences of many transcription factors is known and characterized b...
An important part of gene regulation is mediated by specific proteins, called transcription factors,...
Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regu...
Abstract: The prediction of transcription factor binding sites is an important problem, since it rev...
The availability of whole genome sequences and high-throughput genomic assays opens the door for in ...
In this paper we apply machine learning to the task of predicting transcription factor binding sites...
We propose a novel method for locating transcription factors binding sites in upstream regions, by...
Motivation: The position-specific weight matrix (PWM) model, which assumes that each position in the...
We propose a novel method for locating tran-scription factors binding sites in upstream regions, by ...
BACKGROUND: Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data...
Identifying transcription factor binding sites with experimental methods is often expensive and time...
Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational infere...
82 data subsets are shown as 82 different horizontal lines. Each data subset contains TFBSs correspo...
To try to increase the accuracy of transcription factor binding site prediction we propose a new app...
Reconstructing transcriptional regulatory networks from experimental data is one of the corner-stone...
Information about the binding preferences of many transcription factors is known and characterized b...
An important part of gene regulation is mediated by specific proteins, called transcription factors,...
Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regu...
Abstract: The prediction of transcription factor binding sites is an important problem, since it rev...
The availability of whole genome sequences and high-throughput genomic assays opens the door for in ...
In this paper we apply machine learning to the task of predicting transcription factor binding sites...
We propose a novel method for locating transcription factors binding sites in upstream regions, by...
Motivation: The position-specific weight matrix (PWM) model, which assumes that each position in the...
We propose a novel method for locating tran-scription factors binding sites in upstream regions, by ...
BACKGROUND: Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data...
Identifying transcription factor binding sites with experimental methods is often expensive and time...
Identifying transcription factor (TF) binding sites (TFBSs) is important in the computational infere...
82 data subsets are shown as 82 different horizontal lines. Each data subset contains TFBSs correspo...
To try to increase the accuracy of transcription factor binding site prediction we propose a new app...
Reconstructing transcriptional regulatory networks from experimental data is one of the corner-stone...
Information about the binding preferences of many transcription factors is known and characterized b...
An important part of gene regulation is mediated by specific proteins, called transcription factors,...
Finding where transcription factors (TFs) bind to the DNA is of key importance to decipher gene regu...