ArticleScientific reports2018
Recurrent Neural Network for Predicting Transcription Factor Binding Sites.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers, 1 of them a synthesis that pooled it.
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Who cites it
76 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Comprehensive analysis of computational approaches in plant transcription factors binding regions discovery.Heliyon · 2024Article
- CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation.ACS synthetic biology · 2024Article
- A phenotypic drug discovery approach by latent interaction in deep learning.Royal Society open science · 2024Article
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- CRISPR-GEM: A Novel Machine Learning Model for CRISPR Genetic Target Discovery and Evaluation.bioRxiv : the preprint server for biology · 2024Article
- EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction.BMC bioinformatics · 2024Article
- Estimating sliding drop width via side-view features using recurrent neural networks.Scientific reports · 2024Article
- PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.Briefings in bioinformatics · 2024Article
16 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is well known that DNA sequence contains a certain amount of transcription factors (TF) binding sites, and only part of them are identified through biological experiments. However, these experiments are expensive and time-consuming. To overcome these problems, some computational methods, based on k-mer features or convolutional neural networks, have been proposed to identify TF binding sites from DNA sequences. Although these methods have good performance, the context information that relates to TF binding sites is still lacking. Research indicates that standard recurrent neural networks (RNN) and its variants have better performance in time-series data compared with other models. In this study, we propose a model, named KEGRU, to identify TF binding sites by combining Bidirectional Gated Recurrent Unit (GRU) network with k-mer embedding. Firstly, DNA sequences are divided into k-mer sequences with a specified length and stride window. And then, we treat each k-mer as a word and pre-trained word representation model though word2vec algorithm. Thirdly, we construct a deep bidirectional GRU model for feature learning and classification. Experimental results have shown that our method has better performance compared with some state-of-the-art methods. Additional experiments about embedding strategy show that k-mer embedding will be helpful to enhance model performance. The robustness of KEGRU is proved by experiments with different k-mer length, stride window and embedding vector dimension.
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