Prediction of Transcription Factor Binding Sites of SP1 on Human Chromosome1

Article Biochemical Research Methods

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Chen Chen et al.

Summary: DeepGRN, utilizing a combination of single attention module and pairwise attention module, can accurately predict transcription factor binding sites and outperforms other methods in the ENCODE-DREAM challenge dataset. The attention weights learned by the model are correlated with informative inputs, providing possible explanations for the predictive improvements in DeepGRN.

BMC BIOINFORMATICS (2021)

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Article Biochemical Research Methods

An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning

Fang Jing et al.

Summary: The study focuses on predicting transcription factor binding sites using convolutional neural networks and explores the role of histone modifications and chromatin accessibility in this process. By evaluating different network structures and sample lengths, it is found that the contributions from these three types of data are complementary. The integrative CNN framework outperforms traditional machine learning methods.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2021)

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Article Biochemical Research Methods