AIMC Topic: Single-Cell Analysis
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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
Jan 23, 2025
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...
Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.
Frontiers in immunology
Jan 23, 2025
BACKGROUND: Diabetic nephropathy (DN) is a complication of systemic microvascular disease in diabetes mellitus. Abnormal glycolysis has emerged as a potential factor for chronic renal dysfunction in DN. The current lack of reliable predictive biomark...
Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach.
Genome research
Jan 22, 2025
Application of single-cell/nucleus genomic sequencing to patient-derived tissues offers potential solutions to delineate disease mechanisms in humans. However, individual cells in patient-derived tissues are in different pathological stages, and henc...
Single-Cell Identification and Characterization of Viable but Nonculturable Using Raman Optical Tweezers and Machine Learning.
Analytical chemistry
Jan 19, 2025
is a leading foodborne pathogen that may enter a viable but nonculturable (VBNC) state to survive under environmental stresses, posing a significant health concern. VBNC cells can evade conventional culture-based detection methods, while viability-b...
Cellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.
International journal of molecular sciences
Jan 17, 2025
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune mic...
SenPred: a single-cell RNA sequencing-based machine learning pipeline to classify deeply senescent dermal fibroblast cells for the detection of an in vivo senescent cell burden.
Genome medicine
Jan 14, 2025
BACKGROUND: Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets h...
Immunometabolic alterations in type 2 diabetes mellitus revealed by single-cell RNA sequencing: insights into subtypes and therapeutic targets.
Frontiers in immunology
Jan 14, 2025
BACKGROUND: Type 2 Diabetes Mellitus (T2DM) represents a major global health challenge, marked by chronic hyperglycemia, insulin resistance, and immune system dysfunction. Immune cells, including T cells and monocytes, play a pivotal role in driving ...
The role of mitophagy-related genes in prognosis and immunotherapy of cutaneous melanoma: a comprehensive analysis based on single-cell RNA sequencing and machine learning.
Immunologic research
Jan 11, 2025
Mitophagy, the selective degradation of mitochondria by autophagy, plays a crucial role in cancer progression and therapy response. This study aims to elucidate the role of mitophagy-related genes (MRGs) in cutaneous melanoma (CM) through single-cell...
Immunotherapy
Sequence Analysis, RNA
Gene Expression Regulation, Neoplastic
Melanoma, Cutaneous Malignant
Mitophagy
Transcriptome
Single-Cell Analysis
Gene Regulatory Networks
Tumor Microenvironment
Biomarkers, Tumor
Gene Expression Profiling
Humans
Melanoma
Skin Neoplasms
Machine Learning
Prognosis
Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma.
Scientific reports
Jan 11, 2025
The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and pro...
Combining machine learning and single-cell sequencing to identify key immune genes in sepsis.
Scientific reports
Jan 10, 2025
This research aimed to identify novel indicators for sepsis by analyzing RNA sequencing data from peripheral blood samples obtained from sepsis patients (n = 23) and healthy controls (n = 10). 5148 differentially expressed genes were identified using...
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