AIMC Topic: Computational Biology

Clear Filters Showing 1231 to 1240 of 4583 articles

CapsNet-TIS: Predicting translation initiation site based on multi-feature fusion and improved capsule network.

Gene
Genes are the basic units of protein synthesis in organisms, and accurately identifying the translation initiation site (TIS) of genes is crucial for understanding the regulation, transcription, and translation processes of genes. However, the existi...

Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis.

BMC musculoskeletal disorders
BACKGROUND: Osteoporosis (OP), the "silent epidemic" of our century, poses a significant challenge to public health, predominantly affecting postmenopausal women and the elderly. It evolves from mild symptoms to pronounced severity, stabilizing event...

Characterization of PANoptosis-related genes in Crohn's disease by integrated bioinformatics, machine learning and experiments.

Scientific reports
Currently, the biological understanding of Crohn's disease (CD) remains limited. PANoptosis is a revolutionary form of cell death reported to participate in numerous diseases, including CD. In our study, we aimed to uncover the roles of PANoptosis in...

BiLSTM- and CNN-Based m6A Modification Prediction Model for circRNAs.

Molecules (Basel, Switzerland)
m6A methylation, a ubiquitous modification on circRNAs, exerts a profound influence on RNA function, intracellular behavior, and diverse biological processes, including disease development. While prediction algorithms exist for mRNA m6A modifications...

Improving plant miRNA-target prediction with self-supervised k-mer embedding and spectral graph convolutional neural network.

PeerJ
Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utiliz...

Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery.

Nutrients
BACKGROUND: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques.

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

PLoS computational biology
The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models des...

An intelligent model for prediction of abiotic stress-responsive microRNAs in plants using statistical moments based features and ensemble approaches.

Methods (San Diego, Calif.)
This study proposed an intelligent model for predicting abiotic stress-responsive microRNAs in plants. MicroRNAs (miRNAs) are short RNA molecules regulates the stress in genes. Experimental methods are costly and time-consuming, as compare to in-sili...

Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets.

RNA biology
2´-O-methylation (Nm) is one of the most abundant modifications found in both mRNAs and noncoding RNAs. It contributes to many biological processes, such as the normal functioning of tRNA, the protection of mRNA against degradation by the decapping a...