AIMC Topic: Machine Learning

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Modern semiempirical electronic structure methods and machine learning potentials for drug discovery: Conformers, tautomers, and protonation states.

The Journal of chemical physics
Modern semiempirical electronic structure methods have considerable promise in drug discovery as universal "force fields" that can reliably model biological and drug-like molecules, including alternative tautomers and protonation states. Herein, we c...

Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning.

Nucleic acids research
It has been known for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synt...

Machine learning on protein-protein interaction prediction: models, challenges and trends.

Briefings in bioinformatics
Protein-protein interactions (PPIs) carry out the cellular processes of all living organisms. Experimental methods for PPI detection suffer from high cost and false-positive rate, hence efficient computational methods are highly desirable for facilit...

Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information.

Briefings in bioinformatics
Emerging studies have shown that circular RNAs (circRNAs) are involved in a variety of biological processes and play a key role in disease diagnosing, treating and inferring. Although many methods, including traditional machine learning and deep lear...

MSGCL: inferring miRNA-disease associations based on multi-view self-supervised graph structure contrastive learning.

Briefings in bioinformatics
Potential miRNA-disease associations (MDA) play an important role in the discovery of complex human disease etiology. Therefore, MDA prediction is an attractive research topic in the field of biomedical machine learning. Recently, several models have...

Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences.

Briefings in bioinformatics
N6-methyladinosine (m6A) modification is the most abundant co-transcriptional modification in eukaryotic RNA and plays important roles in cellular regulation. Traditional high-throughput sequencing experiments used to explore functional mechanisms ar...

SGPPI: structure-aware prediction of protein-protein interactions in rigorous conditions with graph convolutional network.

Briefings in bioinformatics
While deep learning (DL)-based models have emerged as powerful approaches to predict protein-protein interactions (PPIs), the reliance on explicit similarity measures (e.g. sequence similarity and network neighborhood) to known interacting proteins m...

Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategy.

Briefings in bioinformatics
Due to the lack of a method to efficiently represent the multimodal information of a protein, including its structure and sequence information, predicting compound-protein binding affinity (CPA) still suffers from low accuracy when applying machine-l...