AIMC Topic: Computational Biology

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Dual-use capabilities of concern of biological AI models.

PLoS computational biology
As a result of rapidly accelerating artificial intelligence (AI) capabilities, multiple national governments and multinational bodies have launched efforts to address safety, security and ethics issues related to AI models. One high priority among th...

GKNnet: an relational graph convolutional network-based method with knowledge-augmented activation layer for microbial structural variation detection.

Briefings in bioinformatics
Structural variants (SVs) in microbial genomes play a critical role in phenotypic changes, environmental adaptation, and species evolution, with deletion variations particularly closely linked to phenotypic traits. Therefore, accurate and comprehensi...

DeepRNA-Twist: language-model-guided RNA torsion angle prediction with attention-inception network.

Briefings in bioinformatics
RNA torsion and pseudo-torsion angles are critical in determining the three-dimensional conformation of RNA molecules, which in turn governs their biological functions. However, current methods are limited by RNA's structural complexity as well as fl...

PackPPI: An integrated framework for protein-protein complex side-chain packing and ΔΔG prediction based on diffusion model.

Protein science : a publication of the Protein Society
Deep learning methods have played an increasingly pivotal role in advancing side-chain packing and mutation effect prediction (ΔΔG) for protein complexes. Although these two tasks are inherently closely related, they are typically treated separately ...

Omics data classification using constitutive artificial neural network optimized with single candidate optimizer.

Network (Bristol, England)
Recent technical advancements enable omics-based biological study of molecules with very high throughput and low cost, such as genomic, proteomic, and microbionics'. To overcome this drawback, Omics Data Classification using Constitutive Artificial N...

DTC-m6Am: A Framework for Recognizing N6,2'-O-dimethyladenosine Sites in Unbalanced Classification Patterns Based on DenseNet and Attention Mechanisms.

Frontiers in bioscience (Landmark edition)
BACKGROUND: mAm is a specific RNA modification that plays an important role in regulating mRNA stability, translational efficiency, and cellular stress response. mAm's precise identification is essential to gain insight into its functional mechanisms...

Integrating Bioinformatics and Machine Learning to Identify Glucose Metabolism-Related Biomarkers with Diagnostic and Prognostic Value for Patients with Kidney Renal Clear Cell Carcinoma.

Archivos espanoles de urologia
BACKGROUND: Glucose metabolism plays a critical role in the development and progression of kidney renal clear cell carcinoma (KIRC). This study aimed to identify glucose metabolism-related biomarkers (GRBs) and therapeutic targets for KIRC diagnosis ...

A robust ensemble framework for anticancer peptide classification using multi-model voting approach.

Computers in biology and medicine
Anticancer peptides (ACPs) hold great potential for cancer therapeutics, yet accurately identifying them remains a challenging task due to the complexity of peptide sequences and their interactions with biological systems. In this study, we propose a...

A multi-view graph convolutional network framework based on adaptive adjacency matrix and multi-strategy fusion mechanism for identifying spatial domains.

Bioinformatics (Oxford, England)
MOTIVATION: Spatial transcriptomics (ST) addresses the loss of spatial context in single-cell RNA-sequencing by simultaneously capturing gene expression and spatial location information. A critical task of ST is the identification of spatial domains....

GeOKG: geometry-aware knowledge graph embedding for Gene Ontology and genes.

Bioinformatics (Oxford, England)
MOTIVATION: Leveraging deep learning for the representation learning of Gene Ontology (GO) and Gene Ontology Annotation (GOA) holds significant promise for enhancing downstream biological tasks such as protein-protein interaction prediction. Prior ap...