AIMC Topic: Computational Biology

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Protein-RNA interaction prediction with deep learning: structure matters.

Briefings in bioinformatics
Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lac...

OPUS-Rota4: a gradient-based protein side-chain modeling framework assisted by deep learning-based predictors.

Briefings in bioinformatics
Accurate protein side-chain modeling is crucial for protein folding and protein design. In the past decades, many successful methods have been proposed to address this issue. However, most of them depend on the discrete samples from the rotamer libra...

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning.

Briefings in bioinformatics
Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts...

HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks.

Briefings in bioinformatics
Identifying new indications for drugs plays an essential role at many phases of drug research and development. Computational methods are regarded as an effective way to associate drugs with new indications. However, most of them complete their tasks ...

LR-GNN: a graph neural network based on link representation for predicting molecular associations.

Briefings in bioinformatics
In biomedical networks, molecular associations are important to understand biological processes and functions. Many computational methods, such as link prediction methods based on graph neural networks (GNNs), have been successfully applied in discov...

SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations.

Briefings in bioinformatics
MiRNAs are a class of small non-coding RNA molecules that play an important role in many biological processes, and determining miRNA-disease associations can benefit drug development and clinical diagnosis. Although great efforts have been made to de...

NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences.

Briefings in bioinformatics
2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety...

BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution.

Briefings in bioinformatics
MOTIVATION: Understanding chemical-gene interactions (CGIs) is crucial for screening drugs. Wet experiments are usually costly and laborious, which limits relevant studies to a small scale. On the contrary, computational studies enable efficient in-s...

Machine learning meets omics: applications and perspectives.

Briefings in bioinformatics
The innovation of biotechnologies has allowed the accumulation of omics data at an alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge from various omics data remains a daunting problem in bioinformatics. Bet...

A similarity-based deep learning approach for determining the frequencies of drug side effects.

Briefings in bioinformatics
The side effects of drugs present growing concern attention in the healthcare system. Accurately identifying the side effects of drugs is very important for drug development and risk assessment. Some computational models have been developed to predic...