AI Medical Compendium Journal:
BMC bioinformatics

Showing 51 to 60 of 772 articles

Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.

BMC bioinformatics
Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and ...

Graph-based machine learning model for weight prediction in protein-protein networks.

BMC bioinformatics
Proteins interact with each other in complex ways to perform significant biological functions. These interactions, known as protein-protein interactions (PPIs), can be depicted as a graph where proteins are nodes and their interactions are edges. The...

Prediction of antibody-antigen interaction based on backbone aware with invariant point attention.

BMC bioinformatics
BACKGROUND: Antibodies play a crucial role in disease treatment, leveraging their ability to selectively interact with the specific antigen. However, screening antibody gene sequences for target antigens via biological experiments is extremely time-c...

Rapid bacterial identification through volatile organic compound analysis and deep learning.

BMC bioinformatics
BACKGROUND: The increasing antimicrobial resistance caused by the improper use of antibiotics poses a significant challenge to humanity. Rapid and accurate identification of microbial species in clinical settings is crucial for precise medication and...

Improving crop production using an agro-deep learning framework in precision agriculture.

BMC bioinformatics
BACKGROUND: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors i...

Predicting RNA sequence-structure likelihood via structure-aware deep learning.

BMC bioinformatics
BACKGROUND: The active functionalities of RNA are recognized to be heavily dependent on the structure and sequence. Therefore, a model that can accurately evaluate a design by giving RNA sequence-structure pairs would be a valuable tool for many rese...

Utilization of a natural language processing-based approach to determine the composition of artifact residues.

BMC bioinformatics
BACKGROUND: Determining the composition of artifact residues is a central problem in ancient residue metabolomics. This is done by comparing mass spectral features in common with an experimental artifact and an ancient artifact (standard method). Whi...

Mild cognitive impairment prediction based on multi-stream convolutional neural networks.

BMC bioinformatics
BACKGROUND: Mild cognitive impairment (MCI) is the transition stage between the cognitive decline expected in normal aging and more severe cognitive decline such as dementia. The early diagnosis of MCI plays an important role in human healthcare. Cur...

PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.

BMC bioinformatics
BACKGROUND: Recently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of o...

PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features.

BMC bioinformatics
Post-translational modifications (PTMs) are fundamental to essential biological processes, exerting significant influence over gene expression, protein localization, stability, and genome replication. Sumoylation, a PTM involving the covalent additio...