AIMC Topic: Computational Biology

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scRSSL: Residual semi-supervised learning with deep generative models to automatically identify cell types.

IET systems biology Apr 22, 2025
Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell datasets often fa...
Single-Cell Analysis Computational Biology Supervised Machine Learning Humans Neural Networks, Computer Deep Learning
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The prediction of RNA-small molecule binding sites in RNA structures based on geometric deep learning.

International journal of biological macromolecules Apr 21, 2025
Biological interactions between RNA and small-molecule ligands play a crucial role in determining the specific functions of RNA, such as catalysis and folding, and are essential for guiding drug design in the medical field. Accurately predicting the ...
Deep Learning Computational Biology Models, Molecular Binding Sites RNA Small Molecule Libraries Ligands Nucleic Acid Conformation
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Bioinformatics Approach to Identifying Molecular Targets of Isoliquiritigenin Affecting Chronic Obstructive Pulmonary Disease: A Machine Learning Pharmacology Study.

International journal of molecular sciences Apr 21, 2025
To identify the molecular targets and possible mechanisms of isoliquiritigenin (ISO) in affecting chronic obstructive pulmonary disease (COPD) by regulating the glycolysis and phagocytosis of alveolar macrophages (AM). Datasets GSE130928 and GSE13896...
Phagocytosis Chalcones Macrophages, Alveolar Gene Expression Profiling Humans Glycolysis Protein Interaction Maps Computational Biology Gene Regulatory Networks Machine Learning Databases, Genetic Pulmonary Disease, Chronic Obstructive
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Transfer learning of multicellular organization via single-cell and spatial transcriptomics.

PLoS computational biology Apr 21, 2025
Biological tissues exhibit complex gene expression and multicellular patterns that are valuable to dissect. Single-cell RNA sequencing (scRNA-seq) provides full coverages of genes, but lacks spatial information, whereas spatial transcriptomics (ST) m...
Humans Neural Networks, Computer Animals Machine Learning Single-Cell Analysis Sequence Analysis, RNA Gene Expression Profiling Transcriptome Mice Drosophila Computational Biology
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Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder.

Scientific reports Apr 19, 2025
Major depressive disorder (MDD) is a multifactorial disorder involving genetic and environmental factors, with unclear pathogenesis. This study aims to explore the pathogenic pathway of MDD and its relationship with immune responses and to discover i...
Machine Learning Signal Transduction Algorithms Databases, Genetic Female Humans Metabolic Networks and Pathways Depressive Disorder, Major Gene Expression Profiling Computational Biology
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A random forest-based predictive model for classifying BRCA1 missense variants: a novel approach for evaluating the missense mutations effect.

Journal of human genetics Apr 18, 2025
The right classification of variants is the key to pre-symptomatic detection of disease and conducting preventive actions. Since BRCA1 has a high incidence and penetrance in breast and ovarian cancers, a high-performance predictive tool can be employ...
Female Breast Neoplasms ROC Curve Humans Machine Learning Random Forest Mutation, Missense BRCA1 Protein Genetic Predisposition to Disease Computational Biology Ovarian Neoplasms
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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...
Sequence Analysis, RNA Deep Learning Single-Cell Analysis RNA-Seq Gene Regulatory Networks Single-Cell Gene Expression Analysis Computational Biology Humans Machine Learning Algorithms
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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...
Flow Cytometry Machine Learning Software Algorithms Computational Biology Humans Cluster Analysis Principal Component 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...
Animals Molecular Docking Simulation Gene Regulatory Networks Humans Machine Learning Computational Biology Nicotine Intracranial Aneurysm
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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 Autoimmunity Humans Intracranial Aneurysm Gene Expression Profiling Neural Networks, Computer Transcriptome
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