AIMC Topic: Algorithms

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DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.

Briefings in bioinformatics
Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, ...

HNCGAT: a method for predicting plant metabolite-protein interaction using heterogeneous neighbor contrastive graph attention network.

Briefings in bioinformatics
The prediction of metabolite-protein interactions (MPIs) plays an important role in plant basic life functions. Compared with the traditional experimental methods and the high-throughput genomics methods using statistical correlation, applying hetero...

Heterogeneous biomedical entity representation learning for gene-disease association prediction.

Briefings in bioinformatics
Understanding the genetic basis of disease is a fundamental aspect of medical research, as genes are the classic units of heredity and play a crucial role in biological function. Identifying associations between genes and diseases is critical for dia...

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.

Briefings in bioinformatics
Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (kcat) and Michaelis constant (Km), key kinetic parameter...

BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data.

Briefings in bioinformatics
Navigating the complex landscape of high-dimensional omics data with machine learning models presents a significant challenge. The integration of biological domain knowledge into these models has shown promise in creating more meaningful stratificati...

CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution.

Briefings in bioinformatics
Predicting the strength of promoters and guiding their directed evolution is a crucial task in synthetic biology. This approach significantly reduces the experimental costs in conventional promoter engineering. Previous studies employing machine lear...

DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information.

Briefings in bioinformatics
Bacteriophages are the viruses that infect bacterial cells. They are the most diverse biological entities on earth and play important roles in microbiome. According to the phage lifestyle, phages can be divided into the virulent phages and the temper...

BPP: a platform for automatic biochemical pathway prediction.

Briefings in bioinformatics
A biochemical pathway consists of a series of interconnected biochemical reactions to accomplish specific life activities. The participating reactants and resultant products of a pathway, including gene fragments, proteins, and small molecules, coale...

MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering.

Briefings in bioinformatics
Metabolic processes can transform a drug into metabolites with different properties that may affect its efficacy and safety. Therefore, investigation of the metabolic fate of a drug candidate is of great significance for drug discovery. Computational...

[Advances in using artificial intelligence for predicting protein-ligand binding affinity].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
The binding of proteins and ligands is a crucial aspect of life processes. The calculation of the protein-ligand binding affinity (PLBA) offers valuable insights into protein function, drug screening targets protein receptors, and enzyme modification...