AIMC Topic: Algorithms

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Predicting miRNA-disease associations using an ensemble learning framework with resampling method.

Briefings in bioinformatics
MOTIVATION: Accumulating evidences have indicated that microRNA (miRNA) plays a crucial role in the pathogenesis and progression of various complex diseases. Inferring disease-associated miRNAs is significant to explore the etiology, diagnosis and tr...

Artificial intelligence in clinical research of cancers.

Briefings in bioinformatics
Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in th...

Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.

Briefings in bioinformatics
Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant incre...

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...

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...

Evaluating the state of the art in missing data imputation for clinical data.

Briefings in bioinformatics
Clinical data are increasingly being mined to derive new medical knowledge with a goal of enabling greater diagnostic precision, better-personalized therapeutic regimens, improved clinical outcomes and more efficient utilization of health-care resour...

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...

Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets.

Briefings in bioinformatics
MOTIVATION: The development process of a new drug is time-consuming and costly. Thus, identifying new uses for approved drugs, named drug repositioning, is helpful for speeding up the drug development process and reducing development costs. Existing ...

A roadmap for multi-omics data integration using deep learning.

Briefings in bioinformatics
High-throughput next-generation sequencing now makes it possible to generate a vast amount of multi-omics data for various applications. These data have revolutionized biomedical research by providing a more comprehensive understanding of the biologi...