Random partition of the instances in Base into 3 disjoint subsets.

<p>Random partition of the instances in Base into 3 disjoint subsets.</p

Random partition of the instances in Base of Table 3 into 3 disjoint subsets.

<p>Random partition of the instances in Base of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005403#pcbi.1005403.t003" target="_blank">Table 3</a> into 3 disjoint subsets.</p

Imputation for transcription factor binding predictions based on deep learning

<div><p>Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding.</p></div

Subsets of TF-cell line combinations using DNase I hypersensitive sites as background.

<p>Subsets of TF-cell line combinations using DNase I hypersensitive sites as background.</p

The TFImpute model.

<p>Each input is a TF-cell-sequence triple. In the convolution layer, each filter (motif) corresponds to a column. Each filter scans the input sequence and produces one value at each stop. For each filter, the max-pooling layer partitions the signal into three windows and takes the maximum value in each window to obtain three values. The same gate signal operates on the three values, and the gate signal is different for different filters. For each input, the reverse complement of the input sequence together with the TF and cell line is constructed and used as another input for the same network. Therefore, for each input, we obtained two values for forward and reverse strand of the sequence: P1 and P2. The maximum of P1 and P2 is taken as the final prediction. During training, the prediction was compared with the target, and the error was back-propagated to learn the parameters of the whole network.</p

The distributions of the calculated enhancer signature for the top and bottom 100 enhancers.

<p>The p value is calculated using t-test. We would like to emphasize the lack of data of the enhancer reporter assay of GM12878, which is a good control.</p

Subsets of TF-cell line combinations using GC matched negative instances.

<p>Subsets of TF-cell line combinations using GC matched negative instances.</p

Predicted binding affinity change between two alleles of SNP rs12740374 (T/G).

<p>The color in each cell represents the predicted binding affinity of allele G minus that of allele T for the corresponding TF and cell line. The number in each cell of the heatmap is the number of ChIP-seq datasets in the training set for the corresponding TF and cell line. If TFImpute predicted strong binding in the minor allele but no binding in the major allele, the score was 1. If TFImpute predicted no binding difference between the two alleles, the score was 0.</p

The influence curve of steel tube thickness on the ultimate bearing capacity of the confined concrete arch.

<p>The influence curve of steel tube thickness on the ultimate bearing capacity of the confined concrete arch.</p

The cross-section layout of SQCC high-strength support system.

<p>The cross-section layout of SQCC high-strength support system.</p