AIMC Topic: Computational Biology

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GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data.

BMC bioinformatics Apr 18, 2025
BACKGROUND: A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. The reconstruction of GRNs can help investigate cellular dynamics, drug d...
Humans Machine Learning Algorithms Computational Biology Sequence Analysis, RNA Deep Learning Single-Cell Analysis RNA-Seq Gene Regulatory Networks Single-Cell Gene Expression Analysis
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BL-FlowSOM: Consistent and Highly Accelerated FlowSOM Based on Parallelized Batch Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology Apr 17, 2025
The recent increase in the dimensionality of cytometry data has led to the development of various computational analysis methods. FlowSOM is one of the best-performing clustering methods but has room for improvement in terms of the consistency and sp...
Principal Component Analysis Humans Algorithms Machine Learning Flow Cytometry Computational Biology Software Cluster Analysis
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Identification of key therapeutic targets in nicotine-induced intracranial aneurysm through integrated bioinformatics and machine learning approaches.

BMC pharmacology & toxicology Apr 17, 2025
BACKGROUND: Intracranial aneurysm (IA) is a critical cerebrovascular condition, and nicotine exposure is a known risk factor. This study delves into the toxicological mechanisms of nicotine in IA, aiming to identify key biomarkers and therapeutic tar...
Nicotine Intracranial Aneurysm Gene Regulatory Networks Humans Computational Biology Machine Learning Animals Molecular Docking Simulation
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Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in intracranial aneurysma.

Frontiers in immunology Apr 17, 2025
BACKGROUND: Increasing evidence indicates a connection between intracranial aneurysm (intracranial aneurysm, IA) and autoimmune diseases. However, the molecular mechanisms from a genetic perspective remain unclear. This study aims to elucidate the po...
Gene Regulatory Networks Computational Biology Databases, Genetic MicroRNAs Machine Learning Humans Autoimmunity Gene Expression Profiling Neural Networks, Computer Intracranial Aneurysm Transcriptome
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The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.

Frontiers in immunology Apr 17, 2025
OBJECTIVE: Lung adenocarcinoma (LUAD) continues to be a primary cause of cancer-related mortality globally, highlighting the urgent need for novel insights finto its molecular mechanisms. This study aims to investigate the relationship between gene e...
Adenocarcinoma of Lung Biomarkers, Tumor Mitophagy Gene Expression Profiling Gene Regulatory Networks Databases, Genetic Humans Autophagy Machine Learning Lung Neoplasms Mitochondria Computational Biology Gene Expression Regulation, Neoplastic
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An integrated bioinformatics and machine learning approach to identifying biomarkers connecting parkinson's disease with purine metabolism-related genes.

BMC neurology Apr 16, 2025
BACKGROUND: Parkinson's disease (PD), a prevalent neurodegenerative disorder in the aging population, poses significant challenges in unraveling its pathogenesis and progression. A key area of investigation is the disruption of oncological metabolic ...
Purines Humans Machine Learning Male Biomarkers Computational Biology Female Parkinson Disease
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TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data.

Nature communications Apr 16, 2025
It is challenging to identify regulatory transcriptional regulators (TRs), which control gene expression via regulatory elements and epigenomic signals, in context-specific studies on the onset and progression of diseases. The use of large-scale mult...
Deep Learning Computational Biology Epigenomics Humans Regulatory Elements, Transcriptional Chromatin Transcription Factors Gene Expression Regulation Binding Sites
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Emerging frontiers in protein structure prediction following the AlphaFold revolution.

Journal of the Royal Society, Interface Apr 16, 2025
Models of protein structures enable molecular understanding of biological processes. Current protein structure prediction tools lie at the interface of biology, chemistry and computer science. Millions of protein structure models have been generated ...
Deep Learning Models, Molecular Protein Conformation Protein Folding Computational Biology Proteins
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Optimizing lipocalin sequence classification with ensemble deep learning models.

PloS one Apr 16, 2025
Deep learning (DL) has become a powerful tool for the recognition and classification of biological sequences. However, conventional single-architecture models often struggle with suboptimal predictive performance and high computational costs. To addr...
Computational Biology Amino Acid Sequence Lipocalins Humans Neural Networks, Computer Deep Learning
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Prediction and Evaluation of Coronavirus and Human Protein-Protein Interactions Integrating Five Different Computational Methods.

Proteins Apr 15, 2025
The high lethality and infectiousness of coronaviruses, particularly SARS-Cov-2, pose a significant threat to human society. Understanding coronaviruses, especially the interactions between these viruses and humans, is crucial for mitigating the coro...
Machine Learning COVID-19 SARS-CoV-2 Humans Viral Proteins Protein Interaction Mapping Protein Interaction Domains and Motifs Protein Interaction Maps Computational Biology Databases, Protein
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