AIMC Topic: Computational Biology

Clear Filters Showing 1381 to 1390 of 4583 articles

Integrated Bioinformatics and Validation Reveal and Its Related Molecules as Potential Identifying Genes in Liver Cirrhosis.

Biomolecules
Liver cirrhosis remains a significant global public health concern, with liver transplantation standing as the foremost effective treatment currently available. Therefore, investigating the pathogenesis of liver cirrhosis and developing novel therapi...

Drug Repositioning Based on Deep Sparse Autoencoder and Drug-Disease Similarity.

Interdisciplinary sciences, computational life sciences
Drug repositioning is critical to drug development. Previous drug repositioning methods mainly constructed drug-disease heterogeneous networks to extract drug-disease features. However, these methods faced difficulty when we are using structurally si...

Hessian Regularized -Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.

Interdisciplinary sciences, computational life sciences
Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate...

Bonds and bytes: The odyssey of structural biology.

Current opinion in structural biology
Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of structural biology, no single method comprehensively r...

DeepCompoundNet: enhancing compound-protein interaction prediction with multimodal convolutional neural networks.

Journal of biomolecular structure & dynamics
Virtual screening has emerged as a valuable computational tool for predicting compound-protein interactions, offering a cost-effective and rapid approach to identifying potential candidate drug molecules. Current machine learning-based methods rely o...

Data science in drug discovery safety: Challenges and opportunities.

Experimental biology and medicine (Maywood, N.J.)
Early de-risking of drug targets and chemistry is essential to provide drug projects with the best chance of success. Target safety assessments (TSAs) use target biology, gene and protein expression data, genetic information from humans and animals, ...

AttCON: With better MSAs and attention mechanism for accurate protein contact map prediction.

Computers in biology and medicine
Protein contact map prediction is a critical and vital step in protein structure prediction, and its accuracy is highly contingent upon the feature representations of protein sequence information and the efficacy of deep learning models. In this pape...

Large AI Models in Health Informatics: Applications, Challenges, and the Future.

IEEE journal of biomedical and health informatics
Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in vario...

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks.

PLoS computational biology
We present the Fast Greedy Equivalence Search (FGES)-Merge, a new method for learning the structure of gene regulatory networks via merging locally learned Bayesian networks, based on the fast greedy equivalent search algorithm. The method is competi...

How is Big Data reshaping preclinical aging research?

Lab animal
The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning...