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Computational Biology

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KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics.

Journal of genetics and genomics = Yi chuan xue bao
KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics (ST) data. ST technologies provide insights into gene...

GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks.

International journal of molecular sciences
Phosphorylation is one of the most common protein post-translational modifications. The identification of phosphorylation sites serves as the cornerstone for protein-phosphorylation-related research. This paper proposes a protein-phosphorylation site...

Identification of potential biomarkers for 2022 Mpox virus infection: a transcriptomic network analysis and machine learning approach.

Scientific reports
Monkeypox virus (MPXV), a zoonotic pathogen, re-emerged in 2022 with the Clade IIb variant, raising global health concerns due to its unprecedented spread in non-endemic regions. Recent studies have shown that Clade IIb (2022 MPXV) is marked by uniqu...

Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning.

Frontiers in immunology
BACKGROUND: The etiology of interstitial cystitis/painful bladder syndrome (IC/BPS) remains elusive, presenting significant challenges in both diagnosis and treatment. To address these challenges, we employed a comprehensive approach aimed at identif...

The tumour histopathology "glossary" for AI developers.

PLoS computational biology
The applications of artificial intelligence (AI) and deep learning (DL) are leading to significant advances in cancer research, particularly in analysing histopathology images for prognostic and treatment-predictive insights. However, effective trans...

DisDock: A Deep Learning Method for Metal Ion-Protein Redocking.

Proteins
The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a met...

Identification of lipid metabolism-related gene markers and construction of a diagnostic model for multiple sclerosis: An integrated analysis by bioinformatics and machine learning.

Analytical biochemistry
BACKGROUND: Multiple sclerosis (MS) is an autoimmune inflammatory disorder that causes neurological disability. Dysregulated lipid metabolism contributes to the pathogenesis of MS. This study aimed to identify lipid metabolism-related gene markers an...

Deep learning methods for proteome-scale interaction prediction.

Current opinion in structural biology
Proteome-scale interaction prediction is essential for understanding protein functions and disease mechanisms. Traditional experimental methods are often limited by scale and complexity, driving the need for computational approaches. Deep learning ha...

Joint embedding-classifier learning for interpretable collaborative filtering.

BMC bioinformatics
BACKGROUND: Interpretability is a topical question in recommender systems, especially in healthcare applications. An interpretable classifier quantifies the importance of each input feature for the predicted item-user association in a non-ambiguous f...

MVGNN-PPIS: A novel multi-view graph neural network for protein-protein interaction sites prediction based on Alphafold3-predicted structures and transfer learning.

International journal of biological macromolecules
Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction sites is essential for biomedical research and targeted drug development. Compared to experimental metho...