AIMC Topic: Machine Learning

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Current status and future perspectives of computational studies on human-virus protein-protein interactions.

Briefings in bioinformatics
The protein-protein interactions (PPIs) between human and viruses mediate viral infection and host immunity processes. Therefore, the study of human-virus PPIs can help us understand the principles of human-virus relationships and can thus guide the ...

A comprehensive overview and critical evaluation of gene regulatory network inference technologies.

Briefings in bioinformatics
Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to un...

Machine learning application for patient stratification and phenotype/genotype investigation in a rare disease.

Briefings in bioinformatics
Alkaptonuria (AKU, OMIM: 203500) is an autosomal recessive disorder caused by mutations in the Homogentisate 1,2-dioxygenase (HGD) gene. A lack of standardized data, information and methodologies to assess disease severity and progression represents ...

Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation.

Briefings in bioinformatics
As an essential task in protein structure and function prediction, protein fold recognition has attracted increasing attention. The majority of the existing machine learning-based protein fold recognition approaches strongly rely on handcrafted featu...

jSRC: a flexible and accurate joint learning algorithm for clustering of single-cell RNA-sequencing data.

Briefings in bioinformatics
Single-cell RNA-sequencing (scRNA-seq) explores the transcriptome of genes at cell level, which sheds light on revealing the heterogeneity and dynamics of cell populations. Advances in biotechnologies make it possible to generate scRNA-seq profiles f...

An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction.

Briefings in bioinformatics
Accurately identifying potential drug-target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been carried out for identifying DTIs in the past few decades, the biological experiment-based DTI ident...

XGSEA: CROSS-species gene set enrichment analysis via domain adaptation.

Briefings in bioinformatics
MOTIVATION: Gene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. Howeve...

Hypergraph-based persistent cohomology (HPC) for molecular representations in drug design.

Briefings in bioinformatics
Artificial intelligence (AI) based drug design has demonstrated great potential to fundamentally change the pharmaceutical industries. Currently, a key issue in AI-based drug design is efficient transferable molecular descriptors or fingerprints. Her...

MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data.

Briefings in bioinformatics
MOTIVATION: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues a...

Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening?

Briefings in bioinformatics
Machine-learning (ML)-based scoring functions (MLSFs) have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and structure-based virtual screening. However, clouds of doubts have still been raised against the...