AI Medical Compendium Journal:
IEEE/ACM transactions on computational biology and bioinformatics

Showing 111 to 120 of 544 articles

promSEMBLE: Hard Pattern Mining and Ensemble Learning for Detecting DNA Promoter Sequences.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of DNA promoter sequences is of crucial importance in unraveling the underlying mechanisms that regulate gene transcription. Initiation of transcription is controlled through regulatory transcription factors binding to promote...

Adaptive Transfer of Graph Neural Networks for Few-Shot Molecular Property Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Few-Shot Molecular Property Prediction (FSMPP) is an improtant task on drug discovery, which aims to learn transferable knowledge from base property prediction tasks with sufficient data for predicting novel properties with few labeled molecules. Its...

T-MGCL: Molecule Graph Contrastive Learning Based on Transformer for Molecular Property Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
In recent years, machine learning has gained increasing traction in the study of molecules, enabling researchers to tackle challenging tasks including molecular property prediction and drug design.Consequently, there remains an open challenge to deve...

Protein-Protein Interaction Site Prediction Based on Attention Mechanism and Convolutional Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Proteins usually perform their cellular functions by interacting with other proteins. Accurate identification of protein-protein interaction sites (PPIs) from sequence is import for designing new drugs and developing novel therapeutics. A lot of comp...

A Unified Multi-Class Feature Selection Framework for Microarray Data.

IEEE/ACM transactions on computational biology and bioinformatics
In feature selection research, simultaneous multi-class feature selection technologies are popular because they simultaneously select informative features for all classes. Recursive feature elimination (RFE) methods are state-of-the-art binary featur...

ProtEC: A Transformer Based Deep Learning System for Accurate Annotation of Enzyme Commission Numbers.

IEEE/ACM transactions on computational biology and bioinformatics
The advancements in next-generation sequencing technologies have given rise to large-scale, open-source protein databases consisting of hundreds of millions of sequences. However, to make these sequences useful in biomedical applications, they need t...

A Deep Learning Approach to the Prediction of Drug Side-Effects on Molecular Graphs.

IEEE/ACM transactions on computational biology and bioinformatics
Predicting drug side effects before they occur is a critical task for keeping the number of drug-related hospitalizations low and for improving drug discovery processes. Automatic predictors of side-effects generally are not able to process the struc...

TransRNAm: Identifying Twelve Types of RNA Modifications by an Interpretable Multi-Label Deep Learning Model Based on Transformer.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of RNA modification sites is of great significance in understanding the functions and regulatory mechanisms of RNAs. Recent advances have shown great promise in applying computational methods based on deep learning for accurat...

Multitype Perception Method for Drug-Target Interaction Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
With the growing popularity of artificial intelligence in drug discovery, many deep-learning technologies have been used to automatically predict unknown drug-target interactions (DTIs). A unique challenge in using these technologies to predict DTI i...

DRLM: A Robust Drug Representation Learning Method and its Applications.

IEEE/ACM transactions on computational biology and bioinformatics
Learning representations from data is a fundamental step for machine learning. High-quality and robust drug representations can broaden the understanding of pharmacology, and improve the modeling of multiple drug-related prediction tasks, which furth...