AIMC Topic: RNA-Seq

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Method for Identifying Cancer-Related Genes Using Gene Similarity-Based Collaborative Filtering.

Journal of computational biology : a journal of computational molecular cell biology May 22, 2019
Genes, Neoplasm Humans Machine Learning Female RNA-Seq Breast Neoplasms Biomarkers, Tumor Models, Genetic
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Selecting precise reference normal tissue samples for cancer research using a deep learning approach.

BMC medical genomics Jan 31, 2019
BACKGROUND: Normal tissue samples are often employed as a control for understanding disease mechanisms, however, collecting matched normal tissues from patients is difficult in many instances. In cancer research, for example, the open cancer resource...
Deep Learning RNA-Seq Humans Reference Values Neural Networks, Computer Neoplasms
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Identification of proliferating neural progenitors in the adult human hippocampus.

Science (New York, N.Y.) Jul 3, 2025
Continuous adult hippocampal neurogenesis is involved in memory formation and mood regulation but is challenging to study in humans. Difficulties finding proliferating progenitor cells called into question whether and how new neurons may be generated...
Child, Preschool Child Single-Cell Analysis RNA-Seq Infant Transcriptome Adolescent Ki-67 Antigen Neurogenesis Dentate Gyrus Neural Stem Cells Sequence Analysis, RNA Cell Proliferation Hippocampus Humans Adult Female Machine Learning Infant, Newborn Male
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Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling.

Scientific reports Jul 2, 2025
In drug discovery, different data modalities (chemical structure, cell biology, quantum mechanics, etc.) are abundant, and their integration can help with understanding aspects of chemistry, biology, and their interactions. Within cell biology, cell ...
Cluster Analysis Gene Expression Profiling Humans Transcriptome Machine Learning RNA-Seq Drug Discovery
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Differentiable graph clustering with structural grouping for single-cell RNA-seq data.

Bioinformatics (Oxford, England) Jul 1, 2025
MOTIVATION: Clustering cells into subpopulations is one of the most crucial tasks in single-cell RNA sequencing (scRNA-seq) data analysis, which provides support for biological research at cellular level. With the development of graph neural networks...
Humans Neural Networks, Computer Single-Cell Gene Expression Analysis Computational Biology Sequence Analysis, RNA Cluster Analysis Algorithms Single-Cell Analysis RNA-Seq
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Identification of potential biomarkers in cardiovascular calcification based on bioinformatics combined with single-cell RNA-seq and multiple machine learning analysis.

Cellular signalling Jul 1, 2025
BACKGROUND: The molecular and genetic mechanisms underlying vascular calcification remain unclear. This study aimed to determine the differences in calcification marker-related gene expression in macrophages.
Biomarkers Computational Biology Humans Animals Vascular Calcification Machine Learning RNA-Seq Single-Cell Analysis Single-Cell Gene Expression Analysis Protein Interaction Maps Mice
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Integrating bulk RNA-seq and scRNA-seq analyses with machine learning to predict platinum response and prognosis in ovarian cancer.

Scientific reports May 31, 2025
Platinum-based therapy is an integral part of the standard treatment for ovarian cancer. However, despite extensive research spanning several decades, the identification of dependable predictive biomarkers for platinum response in clinical practice h...
Platinum Drug Resistance, Neoplasm Single-Cell Gene Expression Analysis Humans Gene Expression Regulation, Neoplastic Machine Learning Ovarian Neoplasms Prognosis Transcriptome Single-Cell Analysis Antineoplastic Agents RNA-Seq Gene Expression Profiling Female Biomarkers, Tumor
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Identification of novel therapeutic targets in hepatitis-B virus-associated membranous nephropathy using scRNA-seq and machine learning.

Scientific reports May 29, 2025
Hepatitis B Virus-associated membranous nephropathy (HBV-MN) significantly impacts renal health, particularly in areas with high HBV prevalence. Understanding the molecular mechanisms underlying HBV-MN is crucial for developing effective therapeutic ...
Machine Learning Hepatitis B virus Humans Single-Cell Gene Expression Analysis MicroRNAs Glomerulonephritis, Membranous Podocytes Hepatitis B Single-Cell Analysis RNA-Seq
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CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders.

Nucleic acids research May 22, 2025
Correcting for confounding variables is often overlooked when computing RNA-RNA correlations, even though it can profoundly affect results. We introduce CorrAdjust, a method for identifying and correcting such hidden confounders. CorrAdjust selects a...
Sequence Analysis, RNA MicroRNAs RNA-Seq Computational Biology RNA, Messenger Humans Gene Expression Profiling Transcriptome Machine Learning Software
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Machine learning using scRNA-seq Combined with bulk-seq to identify lactylation-related hub genes in carotid arteriosclerosis.

Scientific reports May 22, 2025
Atherosclerosis is a chronic inflammatory disease, this study aims to investigate the immune landscape in carotid atherosclerotic plaque formation and explore diagnostic biomarkers of lactylation-associated genes, so as to gain new insights into unde...
Single-Cell Gene Expression Analysis Sequence Analysis, RNA Plaque, Atherosclerotic Carotid Artery Diseases Single-Cell Analysis RNA-Seq Gene Regulatory Networks Humans Biomarkers Machine Learning Gene Expression Profiling Transcriptome
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