AIMC Topic: Computational Biology

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Hub biomarkers and their clinical relevance in glycometabolic disorders: A comprehensive bioinformatics and machine learning approach.

Chinese medical journal
BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical found...

Novel natural vector with asymmetric covariance for classifying biological sequences.

Gene
The genome sequences of organisms form a large and complex landscape, presenting a significant challenge in bioinformatics: how to utilize mathematical tools to describe and analyze this space effectively. The ability to compare relationships between...

Identification of differentially co-expressed genes with lipid metabolism in Parkinson's disease by bioinformatics analysis.

Neuroscience
There was increasing evidence that lipid metabolism disorders played a significant part in the maturation of Parkinson's disease (PD). The purpose of the article was to investigate a significance of lipid metabolism-related genes (LMRGs) in the matur...

protPheMut: An Interpretable Machine Learning Tool for Classification of Cancer and Neurodevelopmental Disorders in Human Missense Mutations.

Journal of chemical information and modeling
Recent advances in human genomics have revealed that missense mutations in a single protein can lead to distinctly different phenotypes. In particular, some mutations in oncoproteins like MEK1, MEK2, PI3Kα, PTEN, SHAP2, and RAS are linked various can...

Transfer Learning for Predicting ncRNA-Protein Interactions.

Journal of chemical information and modeling
Noncoding RNAs (ncRNAs) interact with proteins, playing a crucial role in regulating gene expression and cellular functions. Accurate prediction of these interactions is essential for understanding biological processes and developing novel therapeuti...

Integrative multi-omics analysis reveals BEST1 as a potential tumor-associated gene in gliomas.

Neuroscience
BACKGROUND: The newest glioma classification in WHO 2021 emphasizes the importance of gene mutations in the glioma molecular pathogenesis. Our research aims to look for new glioma-related genes that have the potential to be therapeutic targets.

Integration of machine learning in biomarker discovery for esophageal squamous cell carcinoma: Applications and future directions.

Pathology, research and practice
PURPOSE: Recent advancements in sequencing technologies and bioinformatics algorithms have facilitated significant breakthroughs in both fundamental and clinical tumor research. Nevertheless, the processing and utilization of large-scale data continu...

Predicting metabolite-disease associations based on dynamic adaptive feature learning architecture.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, the association between metabolites and complex human diseases has increasingly been recognized as a major research focus. Traditional wet-lab experiments are considered time-consuming and labor-intensive, w...

Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.

Molecular diversity
MicroRNAs (miRNAs) are evolutionarily conserved small regulatory elements that are ubiquitous in cells and are found to be abnormally expressed during the onset and progression of several human diseases. miRNAs are increasingly recognized as potentia...

Multi-positive contrastive learning-based cross-attention model for T cell receptor-antigen binding prediction.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: T cells play a vital role in the immune system by recognizing and eliminating infected or cancerous cells, thus driving adaptive immune responses. Their activation is triggered by the binding of T cell receptors (TCRs) to ep...