AIMC Topic: Single-Cell Analysis

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A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia.

Cancer research
UNLABELLED: Combination therapies are one potential approach to improve the outcomes of patients with relapsed/refractory (R/R) disease. However, comprehensive testing in scarce primary patient material is hampered by the many drug combination possib...

Label-free single-cell phenotyping to determine tumor cell heterogeneity in pancreatic cancer in real time.

JCI insight
Resistance to chemotherapy of pancreatic ductal adenocarcinoma (PDAC) is largely driven by intratumoral heterogeneity (ITH) due to tumor cell plasticity and clonal diversity. To develop alternative strategies to overcome this defined mechanism of res...

Iterative clustering algorithm G-DESC-E and pan-cancer key gene analysis based on single-cell sequencing data.

Briefings in bioinformatics
Single-cell sequencing technology has profoundly revolutionized the field of cancer genomics, enabling researchers to explore gene expression profiles at the resolution of individual cells. Despite its extensive applications in the study of cancer ge...

Differentiable graph clustering with structural grouping for single-cell RNA-seq data.

Bioinformatics (Oxford, England)
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...

Identification of potential biomarkers in cardiovascular calcification based on bioinformatics combined with single-cell RNA-seq and multiple machine learning analysis.

Cellular signalling
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.

Artificial intelligence approaches for tumor phenotype stratification from single-cell transcriptomic data.

eLife
Single-cell RNA-sequencing (scRNA-seq) coupled with robust computational analysis facilitates the characterization of phenotypic heterogeneity within tumors. Current scRNA-seq analysis pipelines are capable of identifying a myriad of malignant and no...

Segment-Weighting Similarity-Based Fragment-Learning Model for Single-Cell Raman Spectral Analysis.

Analytical chemistry
Raman spectroscopy provides intrinsic biochemical profiles of all cellular biomolecules in a segmented manner, promising nondestructive and label-free phenotyping at the single-cell level. However, current analytical methods rarely utilize spectral b...

Machine Learning-Assisted Analysis of the Oral Cancer Immune Microenvironment: From Single-Cell Level to Prognostic Model Construction.

Journal of cellular and molecular medicine
Oral cancer is among the most prevalent malignant tumours worldwide; prognosis can be affected by several factors, including molecular subtypes, immune microenvironment and clinical characteristics. In this study, we aimed to apply machine learning m...

Single-cell analyses unravel ecosystem dynamics and intercellular crosstalk during gallbladder cancer malignant transformation.

Hepatology communications
BACKGROUND: Gallbladder cancer (GBC) is a rare but aggressive malignancy, often detected late due to early asymptomatic stages. Understanding cellular and molecular changes from normal tissue to high-grade intraepithelial neoplasia (HGIN) and invasiv...

GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data.

Computers in biology and medicine
OBJECTIVE: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) by leveraging single-cell RNA sequencing (scRNA-seq) data from peripheral blood mononuclear cells (PBMCs) and advanced deep learning techniques.