Journal article
DeepTorrent: A deep learning-based approach for predicting DNA N4-methylcytosine sites
Q Liu, J Chen, Y Wang, S Li, C Jia, J Song, F Li
Briefings in Bioinformatics | OXFORD UNIV PRESS | Published : 2021
DOI: 10.1093/bib/bbaa124
Abstract
DNA N4-methylcytosine (4mC) is an important epigenetic modification that plays a vital role in regulating DNA replication and expression. However, it is challenging to detect 4mC sites through experimental methods, which are time-consuming and costly. Thus, computational tools that can identify 4mC sites would be very useful for understanding the mechanism of this important type of DNA modification. Several machine learning-based 4mC predictors have been proposed in the past 3 years, although their performance is unsatisfactory. Deep learning is a promising technique for the development of more accurate 4mC site predictions. In this work, we propose a deep learning-based approach, called Dee..
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Grants
Awarded by National Institute of Allergy and Infectious Diseases
Funding Acknowledgements
National Health and Medical Research Council of Australia (NHMRC) (1092262); Australian Research Council (ARC) (LP110200333 and DP120104460); National Institute of Allergy and Infectious Diseases of the National Institutes of Health (R01 AI111965); Major Inter-Disciplinary Research (IDR) project awarded by Monash University; Collaborative Research Program of Institute for Chemical Research, Kyoto University (2019-32).