AIMC Topic: DNA

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Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species.

Methods (San Diego, Calif.)
DNA N6-methyladenine (6mA) is a key DNA modification, which plays versatile roles in the cellular processes, including regulation of gene expression, DNA repair, and DNA replication. DNA 6mA is closely associated with many diseases in the mammals and...

FCNGRU: Locating Transcription Factor Binding Sites by Combing Fully Convolutional Neural Network With Gated Recurrent Unit.

IEEE journal of biomedical and health informatics
Deciphering the relationship between transcription factors (TFs) and DNA sequences is very helpful for computational inference of gene regulation and a comprehensive understanding of gene regulation mechanisms. Transcription factor binding sites (TFB...

DNA Input Classification by a Riboregulator-Based Cell-Free Perceptron.

ACS synthetic biology
The ability to recognize molecular patterns is essential for the continued survival of biological organisms, allowing them to sense and respond to their immediate environment. The design of synthetic gene-based classifiers has been explored previousl...

DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Recent advances in next-generation sequencing technologies have led to the successful insertion of video information into DNA using synthesized oligonucleotides. Several attempts have been made to embed larger data into living organisms. This process...

DeepDNAbP: A deep learning-based hybrid approach to improve the identification of deoxyribonucleic acid-binding proteins.

Computers in biology and medicine
Accurate identification of DNA-binding proteins (DBPs) is critical for both understanding protein function and drug design. DBPs also play essential roles in different kinds of biological activities such as DNA replication, repair, transcription, and...

Predicting residues involved in anti-DNA autoantibodies with limited neural networks.

Medical & biological engineering & computing
Computer-aided rational vaccine design (RVD) and synthetic pharmacology are rapidly developing fields that leverage existing datasets for developing compounds of interest. Computational proteomics utilizes algorithms and models to probe proteins for ...

A sequence-based two-layer predictor for identifying enhancers and their strength through enhanced feature extraction.

Journal of bioinformatics and computational biology
Enhancers are short regulatory DNA fragments that are bound with proteins called activators. They are free-bound and distant elements, which play a vital role in controlling gene expression. It is challenging to identify enhancers and their strength ...

FusionAI, a DNA-sequence-based deep learning protocol reduces the false positives of human fusion gene prediction.

STAR protocols
Even though there were many tool developments of fusion gene prediction from NGS data, too many false positives are still an issue. Wise use of the genomic features around the fusion gene breakpoints will be helpful to identify reliable fusion genes ...

GC6mA-Pred: A deep learning approach to identify DNA N6-methyladenine sites in the rice genome.

Methods (San Diego, Calif.)
MOTIVATION: DNA N6-methyladenine (6mA) is a pivotal DNA modification for various biological processes. More accurate prediction of 6mA methylation sites plays an irreplaceable part in grasping the internal rationale of related biological activities. ...