AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Single-Cell Analysis

Showing 161 to 170 of 479 articles

Clear Filters

A self-supervised embedding of cell migration features for behavior discovery over cell populations.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Recent studies point out that the dynamics and interaction of cell populations within their environment are related to several biological processes in immunology. Hence, single-cell analysis in immunology now relies on spati...

Novel AT2 Cell Subpopulations and Diagnostic Biomarkers in IPF: Integrating Machine Learning with Single-Cell Analysis.

International journal of molecular sciences
Idiopathic pulmonary fibrosis (IPF) is a long-term condition with an unidentified cause, and currently there are no specific treatment options available. Alveolar epithelial type II cells (AT2) constitute a heterogeneous population crucial for secret...

T-distributed Stochastic Neighbor Network for unsupervised representation learning.

Neural networks : the official journal of the International Neural Network Society
Unsupervised representation learning (URL) is still lack of a reasonable operator (e.g. convolution kernel) for exploring meaningful structural information from generic data including vector, image and tabular data. In this paper, we propose a simple...

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis.

ACS nano
Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights in...

Fragment-Fusion Transformer: Deep Learning-Based Discretization Method for Continuous Single-Cell Raman Spectral Analysis.

ACS sensors
Raman spectroscopy has become an important single-cell analysis tool for monitoring biochemical changes at the cellular level. However, Raman spectral data, typically presented as continuous data with high-dimensional characteristics, is distinct fro...

scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis.

Genome biology
Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organization at single-cell resolution. Here we introduce scHolography, a machine learning-based method desig...

Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment.

Frontiers in immunology
Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immunotherapy of intracranial aneurysms (IAs). The objective of this study was to identify monocyte/macrop...

NNICE: a deep quantile neural network algorithm for expression deconvolution.

Scientific reports
The composition of cell-type is a key indicator of health. Advancements in bulk gene expression data curation, single cell RNA-sequencing technologies, and computational deconvolution approaches offer a new perspective to learn about the composition ...

Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX.

Nature communications
The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (...

Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction.

World journal of surgical oncology
BACKGROUND: Non-small cell lung cancer (NSCLC) is a prevalent and heterogeneous disease with significant genomic variations between the early and advanced stages. The identification of key genes and pathways driving NSCLC tumor progression is critica...