AIMC Topic: Single-Cell Analysis

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MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.

Analytical chemistry
Single-cell mass spectrometry (SCMS) is an emerging tool for studying cell heterogeneity according to variation of molecular species in single cells. Although it has become increasingly common to employ machine learning models in SCMS data analysis, ...

Automated Single Cell Phenotyping of Time-of-Flight Secondary Ion Mass Spectrometry Tissue Images.

Journal of the American Society for Mass Spectrometry
Existing analytical techniques are being improved or applied in new ways to profile the tissue microenvironment (TME) to better understand the role of cells in disease research. Fully understanding the complex interactions between cells of many diffe...

Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment.

Frontiers in immunology
BACKGROUND: Breast cancer, characterized by its heterogeneity, is a leading cause of mortality among women. The study aims to develop a Machine Learning-Derived Liquid-Liquid Phase Separation (MDLS) model to enhance the prognostic accuracy and person...

Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning.

Cancer science
Leukemia is highly heterogeneous, meaning that different types of leukemia require different treatments and have different prognoses. Current clinical diagnostic and typing tests are complex and time-consuming. In particular, all of these tests rely ...

SDC4 protein action and related key genes in nonhealing diabetic foot ulcers based on bioinformatics analysis and machine learning.

International journal of biological macromolecules
Diabetic foot ulcers (DFU) is a complication associated with diabetes characterised by high morbidity, disability, and mortality, involving chronic inflammation and infiltration of multiple immune cells. We aimed to identify the critical genes in non...

A Single-Cell Interrogation System from Scratch: Microfluidics and Deep Learning.

The journal of physical chemistry. B
Live-cell imaging using fluorescence microscopy enables researchers to study cellular processes in unprecedented detail. These techniques are becoming increasingly popular among microbiologists. The emergence of microfluidics and deep learning has si...

Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing.

Nature methods
The rapid growth of single-cell transcriptomic technology has produced an increasing number of datasets for both embryonic development and in vitro pluripotent stem cell-derived models. This avalanche of data surrounding pluripotency and the process ...

SpaInGNN: Enhanced clustering and integration of spatial transcriptomics based on refined graph neural networks.

Methods (San Diego, Calif.)
Recent developments in spatial transcriptomics (ST) technology have markedly enhanced the proposed capacity to comprehensively characterize gene expression patterns within tissue microenvironments while crucially preserving spatial context. However, ...

Artificial Intelligence-Assisted Automatic Raman-Activated Cell Sorting (AI-RACS) System for Mining Specific Functional Microorganisms in the Microbiome.

Analytical chemistry
The microbiome represents the natural presence of microorganisms, and exploring, understanding, and leveraging its functions will bring about significant breakthroughs in life sciences and applications. Raman-activated cell sorting (RACS) enables the...