AIMC Topic: DNA

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Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces.

Methods in molecular biology (Clifton, N.J.)
The increasing number of protein structures with uncharacterized function necessitates the development of in silico prediction methods for functional annotations on proteins. In this chapter, different kinds of computational approaches are briefly in...

DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding.

Bioinformatics (Oxford, England)
MOTIVATION: Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of ...

DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes.

Bioinformatics (Oxford, England)
MOTIVATION: 5-Methylcytosine and 5-Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High-throughput assays to detect these modifications are expensive, labor-intensive, unfeasible...

High-speed all-optical DNA local sequence alignment based on a three-dimensional artificial neural network.

Journal of the Optical Society of America. A, Optics, image science, and vision
This paper presents an optical processing approach for exploring a large number of genome sequences. Specifically, we propose an optical correlator for global alignment and an extended moiré matching technique for local analysis of spatially coded DN...

DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signa...

Primer on the Gene Ontology.

Methods in molecular biology (Clifton, N.J.)
The Gene Ontology (GO) project is the largest resource for cataloguing gene function. The combination of solid conceptual underpinnings and a practical set of features have made the GO a widely adopted resource in the research community and an essent...

The Gene Ontology and the Meaning of Biological Function.

Methods in molecular biology (Clifton, N.J.)
The Gene Ontology (GO) provides a framework and set of concepts for describing the functions of gene products from all organisms. It is specifically designed for supporting the computational representation of biological systems. A GO annotation is an...

Convolutional neural network architectures for predicting DNA-protein binding.

Bioinformatics (Oxford, England)
MOTIVATION: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA-protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth...