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DNA

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GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues.

Nucleic acids research
Knowledge of the interactions between proteins and nucleic acids is the basis of understanding various biological activities and designing new drugs. How to accurately identify the nucleic-acid-binding residues remains a challenging task. In this pap...

Interpretation of deep learning in genomics and epigenomics.

Briefings in bioinformatics
Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies tow...

DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences.

Briefings in bioinformatics
Quantifying DNA properties is a challenging task in the broad field of human genomics. Since the vast majority of non-coding DNA is still poorly understood in terms of function, this task is particularly important to have enormous benefit for biology...

DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites.

Briefings in bioinformatics
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...

Prediction of bio-sequence modifications and the associations with diseases.

Briefings in functional genomics
Modifications of protein, RNA and DNA play an important role in many biological processes and are related to some diseases. Therefore, accurate identification and comprehensive understanding of protein, RNA and DNA modification sites can promote rese...

Deep learning for nanopore ionic current blockades.

The Journal of chemical physics
DNA molecules can electrophoretically be driven through a nanoscale opening in a material, giving rise to rich and measurable ionic current blockades. In this work, we train machine learning models on experimental ionic blockade data from DNA nucleot...

[Identification of nucleosome positioning using support vector machine method based on comprehensive DNA sequence feature].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
In this article, based on z-curve theory and position weight matrix (PWM), a model for nucleosome sequences was constructed. Nucleosome sequence dataset was transformed into three-dimensional coordinates, PWM of the nucleosome sequences was calculate...

DNA4mC-LIP: a linear integration method to identify N4-methylcytosine site in multiple species.

Bioinformatics (Oxford, England)
MOTIVATION: DNA N4-methylcytosine (4mC) is a crucial epigenetic modification. However, the knowledge about its biological functions is limited. Effective and accurate identification of 4mC sites will be helpful to reveal its biological functions and ...

iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

Briefings in bioinformatics
With the explosive growth of biological sequences generated in the post-genomic era, one of the most challenging problems in bioinformatics and computational biology is to computationally characterize sequences, structures and functions in an efficie...

Identifying enhancer-promoter interactions with neural network based on pre-trained DNA vectors and attention mechanism.

Bioinformatics (Oxford, England)
MOTIVATION: Identification of enhancer-promoter interactions (EPIs) is of great significance to human development. However, experimental methods to identify EPIs cost too much in terms of time, manpower and money. Therefore, more and more research ef...