AIMC Topic: Computational Biology

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Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13).

Proteins
We describe AlphaFold, the protein structure prediction system that was entered by the group A7D in CASP13. Submissions were made by three free-modeling (FM) methods which combine the predictions of three neural networks. All three systems were guide...

ACME: pan-specific peptide-MHC class I binding prediction through attention-based deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Prediction of peptide binding to the major histocompatibility complex (MHC) plays a vital role in the development of therapeutic vaccines for the treatment of cancer. Algorithms with improved correlations between predicted and actual bind...

iRNAD: a computational tool for identifying D modification sites in RNA sequence.

Bioinformatics (Oxford, England)
MOTIVATION: Dihydrouridine (D) is a common RNA post-transcriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased...

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.

Briefings in bioinformatics
As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, com...

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.

Briefings in bioinformatics
Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic id...

Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches.

Briefings in bioinformatics
Metabolomics involves studies of a great number of metabolites, which are small molecules present in biological systems. They play a lot of important functions such as energy transport, signaling, building block of cells and inhibition/catalysis. Und...

ML-Net: multi-label classification of biomedical texts with deep neural networks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods,...

ResPRE: high-accuracy protein contact prediction by coupling precision matrix with deep residual neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Contact-map of a protein sequence dictates the global topology of structural fold. Accurate prediction of the contact-map is thus essential to protein 3D structure prediction, which is particularly useful for the protein sequences that do...

Min3: Predict microRNA target gene using an improved binding-site representation method and support vector machine.

Journal of bioinformatics and computational biology
MicroRNAs are single-stranded noncoding RNAs known to down-regulate target genes at the protein or mRNA level. Computational prediction of targets is essential for elucidating the detailed functions of microRNA. However, prediction specificity and se...