AIMC Topic: DNA

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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...

Deep Learning in the Study of Protein-Related Interactions.

Protein and peptide letters
Protein-related interaction prediction is critical to understanding life processes, biological functions, and mechanisms of drug action. Experimental methods used to determine proteinrelated interactions have always been costly and inefficient. In re...

DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: We expect novel pathogens to arise due to their fast-paced evolution, and new species to be discovered thanks to advances in DNA sequencing and metagenomics. Moreover, recent developments in synthetic biology raise concerns that some stra...

Neural networks with circular filters enable data efficient inference of sequence motifs.

Bioinformatics (Oxford, England)
MOTIVATION: Nucleic acids and proteins often have localized sequence motifs that enable highly specific interactions. Due to the biological relevance of sequence motifs, numerous inference methods have been developed. Recently, convolutional neural n...

HMMRATAC: a Hidden Markov ModeleR for ATAC-seq.

Nucleic acids research
ATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without considering the transposase digested DNA fragm...

Prediction of regulatory motifs from human Chip-sequencing data using a deep learning framework.

Nucleic acids research
The identification of transcription factor binding sites and cis-regulatory motifs is a frontier whereupon the rules governing protein-DNA binding are being revealed. Here, we developed a new method (DEep Sequence and Shape mOtif or DESSO) for cis-re...

Comprehensive evaluation of deep learning architectures for prediction of DNA/RNA sequence binding specificities.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning architectures have recently demonstrated their power in predicting DNA- and RNA-binding specificity. Existing methods fall into three classes: Some are based on convolutional neural networks (CNNs), others use recurrent neur...

Simple tricks of convolutional neural network architectures improve DNA-protein binding prediction.

Bioinformatics (Oxford, England)
MOTIVATION: With the accumulation of DNA sequencing data, convolution neural network (CNN) based methods such as DeepBind and DeepSEA have achieved great success for predicting the function of primary DNA sequences. Previous studies confirm the impor...

Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species.

Bioinformatics (Oxford, England)
MOTIVATION: As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) is recently shown to play crucial roles in restriction-modification systems. For better understanding of their functional mechanisms, it is fundamentally important ...