Sentence examples for binding data for multiple from inspiring English sources

(2011) reported a comprehensive transcriptome analysis of human hematopoiesis, combined with sophisticated bioinformatics analysis and high-throughput DNA binding data for multiple transcription factors.

Publicly available data sets of peptide-MHC binding data for multiple HLAs, were used as training sets.

In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes.

Next we trained the machine learning classifier on binding data for DRB1*0101 and made predictions for peptide binding to multiple dissimilar MHC allotypes in order to test the transferability of the prediction method.

The competitive binding data for each ligand was tested for both one and two -site binding.

These observations have spurred the development of approaches that integrate data for multiple types of chromatin features to improve the accuracy of TF binding site predictions.

For the illustrations, we require data for multiple node densities.

Multiple entries represent data for multiple transcripts on the array.

We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types.

The particular allotypes used for testing were chosen for two reasons: (1) adequate peptide binding data was available for evaluating the prediction results and (2) they have very different peptide binding specificities so that the results reflect the generality of the prediction method for multiple MHC allotypes.

CREs typically consist of closely linked clusters of binding sites for multiple TFs [1], [2].