AIMC Topic: Neural Networks, Computer

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scHyper: reconstructing cell-cell communication through hypergraph neural networks.

Briefings in bioinformatics
Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive g...

DeepIRES: a hybrid deep learning model for accurate identification of internal ribosome entry sites in cellular and viral mRNAs.

Briefings in bioinformatics
The internal ribosome entry site (IRES) is a cis-regulatory element that can initiate translation in a cap-independent manner. It is often related to cellular processes and many diseases. Thus, identifying the IRES is important for understanding its ...

Harnessing large language models' zero-shot and few-shot learning capabilities for regulatory research.

Briefings in bioinformatics
Large language models (LLMs) are sophisticated AI-driven models trained on vast sources of natural language data. They are adept at generating responses that closely mimic human conversational patterns. One of the most notable examples is OpenAI's Ch...

HTINet2: herb-target prediction via knowledge graph embedding and residual-like graph neural network.

Briefings in bioinformatics
Target identification is one of the crucial tasks in drug research and development, as it aids in uncovering the action mechanism of herbs/drugs and discovering new therapeutic targets. Although multiple algorithms of herb target prediction have been...

GR-pKa: a message-passing neural network with retention mechanism for pKa prediction.

Briefings in bioinformatics
During the drug discovery and design process, the acid-base dissociation constant (pKa) of a molecule is critically emphasized due to its crucial role in influencing the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties...

INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction.

Briefings in bioinformatics
An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constraints in time and cost of the associated 'wet lab' experiments. In silico PPI inference methods are ideal tools t...

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...

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...

Machine learning-assisted substrate binding pocket engineering based on structural information.

Briefings in bioinformatics
Engineering enzyme-substrate binding pockets is the most efficient approach for modifying catalytic activity, but is limited if the substrate binding sites are indistinct. Here, we developed a 3D convolutional neural network for predicting protein-li...