AIMC Topic: Single-Cell Analysis

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Fuzzy-Based Identification of Transition Cells to Infer Cell Trajectory for Single-Cell Transcriptomics.

Journal of computational biology : a journal of computational molecular cell biology
With the continuous evolution of single-cell RNA sequencing technology, it has become feasible to reconstruct cell development processes using computational methods. Trajectory inference is a crucial downstream analytical task that provides valuable ...

Advanced Spore Identification: Single-Cell Raman Spectroscopy Combined with Self-Attention Mechanism-Guided Deep Learning.

Analytical chemistry
(Nb) has been considered a dangerous pathogen, which can spread rapidly through free spores. Nowadays, pebrine disease caused by Nb spores is a serious threat to silkworms, causing huge economic losses in both the silk industry and agriculture every...

Simple and effective embedding model for single-cell biology built from ChatGPT.

Nature biomedical engineering
Large-scale gene-expression data are being leveraged to pretrain models that implicitly learn gene and cellular functions. However, such models require extensive data curation and training. Here we explore a much simpler alternative: leveraging ChatG...

Harnessing machine learning and multi-omics to explore tumor evolutionary characteristics and the role of AMOTL1 in prostate cancer.

International journal of biological macromolecules
Although recent advancements have shed light on the crucial role of coordinated evolution among cell subpopulations in influencing disease progression, the full potential of these insights has not yet been fully harnessed in the clinical application ...

Intelligent identification of foodborne pathogenic bacteria by self-transfer deep learning and ensemble prediction based on single-cell Raman spectrum.

Talanta
Foodborne pathogenic infections pose a significant threat to human health. Accurate detection of foodborne diseases is essential in preventing disease transmission. This study proposed an AI model for precisely identifying foodborne pathogenic bacter...

Deciphering the impact of senescence in kidney transplant rejection: An integrative machine learning and multi-omics analysis via bulk and single-cell RNA sequencing.

PloS one
BACKGROUND: The demographic shift towards an older population presents significant challenges for kidney transplantation (KTx), particularly due to the vulnerability of aged donor kidneys to ischemic damage, delayed graft function, and reduced graft ...

Machine Learning Integration with Single-Cell Transcriptome Sequencing Datasets Reveals the Impact of Tumor-Associated Neutrophils on the Immune Microenvironment and Immunotherapy Outcomes in Gastric Cancer.

International journal of molecular sciences
The characteristics of neutrophils play a crucial role in defining the tumor inflammatory environment. However, the function of tumor-associated neutrophils (TANs) in tumor immunity and their response to immune checkpoint inhibitors (ICIs) remains in...

Antigen-independent single-cell circulating tumor cell detection using deep-learning-assisted biolasers.

Biosensors & bioelectronics
Circulating tumor cells (CTCs) in the bloodstream are important biomarkers for clinical prognosis of cancers. Current CTC identification methods are based on immuno-labeling, which depends on the differential expression of specific antigens between t...

EfficientNet-resDDSC: A Hybrid Deep Learning Model Integrating Residual Blocks and Dilated Convolutions for Inferring Gene Causality in Single-Cell Data.

Interdisciplinary sciences, computational life sciences
Gene Regulatory Networks (GRNs) reveal complex interactions between genes in organisms, crucial for understanding the life system's operation. The rapid development of biotechnology, especially single-cell RNA sequencing (scRNA-seq), has generated a ...