AI Medical Compendium Topic:
Computational Biology

Clear Filters Showing 461 to 470 of 4036 articles

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes.

Scientific reports
Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, represents a continuum that may escalate from mild systemic infection to severe sepsis, potentially resulting in septic shock and multiple organ dysfunc...

Deep multiple instance learning on heterogeneous graph for drug-disease association prediction.

Computers in biology and medicine
Drug repositioning offers promising prospects for accelerating drug discovery by identifying potential drug-disease associations (DDAs) for existing drugs and diseases. Previous methods have generated meta-path-augmented node or graph embeddings for ...

Accurate RNA 3D structure prediction using a language model-based deep learning approach.

Nature methods
Accurate prediction of RNA three-dimensional (3D) structures remains an unsolved challenge. Determining RNA 3D structures is crucial for understanding their functions and informing RNA-targeting drug development and synthetic biology design. The stru...

Leveraging Bioinformatics and Machine Learning for Identifying Prognostic Biomarkers and Predicting Clinical Outcomes in Lung Adenocarcinoma.

Genes
There exist significant challenges for lung adenocarcinoma (LUAD) due to its poor prognosis and limited treatment options, particularly in the advanced stages. It is crucial to identify genetic biomarkers for improving outcome predictions and guidin...

Deep neural networks for endemic measles dynamics: Comparative analysis and integration with mechanistic models.

PLoS computational biology
Measles is an important infectious disease system both for its burden on public health and as an opportunity for studying nonlinear spatio-temporal disease dynamics. Traditional mechanistic models often struggle to fully capture the complex nonlinear...

A combined deep learning framework for mammalian m6A site prediction.

Cell genomics
N-methyladenosine (m6A) is the most prevalent chemical modification in eukaryotic mRNAs and plays key roles in diverse cellular processes. Precise localization of m6A sites is thus critical for characterizing the functional roles of m6A in various co...

Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.

PLoS computational biology
The ability to predict HIV-1 resistance to broadly neutralizing antibodies (bnAbs) will increase bnAb therapeutic benefits. Machine learning is a powerful approach for such prediction. One challenge is that some HIV-1 subtypes in currently available ...

A multi-perspective deep learning framework for enhancer characterization and identification.

Computational biology and chemistry
Enhancers are vital elements in the genome that boost the transcriptional activity of neighboring genes and are essential in regulating cell-specific gene expression. Therefore, accurately identifying and characterizing enhancers is essential for com...

PredIDR: Accurate prediction of protein intrinsic disorder regions using deep convolutional neural network.

International journal of biological macromolecules
The involvement of protein intrinsic disorder in essential biological processes, it is well known in structural biology. However, experimental methods for detecting intrinsic structural disorder and directly measuring highly dynamic behavior of prote...

DeepKlapred: A deep learning framework for identifying protein lysine lactylation sites via multi-view feature fusion.

International journal of biological macromolecules
Lysine lactylation (Kla) is a post-translational modification (PTM) that holds significant importance in the regulation of various biological processes. While traditional experimental methods are highly accurate for identifying Kla sites, they are bo...