AIMC Topic: Computational Biology

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Deep learning for healthcare: review, opportunities and challenges.

Briefings in bioinformatics
Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electron...

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

Bioinformatics (Oxford, England)
MOTIVATION: MicroRNAs (miRNAs) are small non-coding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRN...

Perspectives and applications of machine learning for evolutionary developmental biology.

Molecular omics
Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand how development was modulated by the evolutionary process. In this sense, "omic" studies emerged as a powerful ally to unravel the molecular mechanisms u...

Gene multifunctionality scoring using gene ontology.

Journal of bioinformatics and computational biology
Multifunctional genes are important genes because of their essential roles in human cells. Studying and analyzing multifunctional genes can help understand disease mechanisms and drug discovery. We propose a computational method for scoring gene mult...

Protein secondary structure prediction improved by recurrent neural networks integrated with two-dimensional convolutional neural networks.

Journal of bioinformatics and computational biology
Protein secondary structure prediction (PSSP) is an important research field in bioinformatics. The representation of protein sequence features could be treated as a matrix, which includes the amino-acid residue (time-step) dimension and the feature ...

A systematic exploration of [Formula: see text] cutoff ranges in machine learning models for protein mutation stability prediction.

Journal of bioinformatics and computational biology
Discerning how a mutation affects the stability of a protein is central to the study of a wide range of diseases. Mutagenesis experiments on physical proteins provide precise insights about the effects of amino acid substitutions, but such studies ar...

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features.

Bioinformatics (Oxford, England)
MOTIVATION: In addition to substitution frequency data from protein sequence alignments, many state-of-the-art methods for contact prediction rely on additional sources of information, or features, of protein sequences in order to predict residue-res...

The anatomy of phenotype ontologies: principles, properties and applications.

Briefings in bioinformatics
The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large...

A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential.

Nucleic acids research
The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, featu...