Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites.

CONCLUSION: Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases. PMID: 29081688 [PubMed]
Source: Current Genomics - Category: Genetics & Stem Cells Tags: Curr Genomics Source Type: research