AIMC Topic: Transcriptome

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COME: contrastive mapping learning for spatial reconstruction of single-cell RNA sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive d...

Machine Learning-Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma.

Journal of cellular and molecular medicine
Using machine learning approaches, we developed and validated a novel prognostic model for oesophageal squamous cell carcinoma (ESCC) based on glycolipid metabolism-related genes. Through integrated analysis of TCGA and GEO datasets, we established a...

Enhancing single-cell classification accuracy using image conversion and deep learning.

Yi chuan = Hereditas
Single-cell transcriptome sequencing (scRNA-seq) is widely used in the fields of animal and plant developmental biology and important trait analysis by obtaining single-cell transcript abundance data in high throughput, which can deeply reveal cell t...

stAI: a deep learning-based model for missing gene imputation and cell-type annotation of spatial transcriptomics.

Nucleic acids research
Spatial transcriptomics technology has revolutionized our understanding of cellular systems by capturing RNA transcript levels in their original spatial context. Single-cell spatial transcriptomics (scST) offers single-cell resolution expression leve...

Predicting Fitness-Related Traits Using Gene Expression and Machine Learning.

Genome biology and evolution
Evolution by natural selection occurs at its most basic through the change in frequencies of alleles; connecting those genomic targets to phenotypic selection is an important goal for evolutionary biology in the genomics era. The relative abundance o...

Identification and Validation of Biomarkers in Metabolic Dysfunction-Associated Steatohepatitis Using Machine Learning and Bioinformatics.

Molecular genetics & genomic medicine
BACKGROUND: The incidence of metabolic dysfunction-associated steatohepatitis (MASH) is increasing annually. MASH can progress to cirrhosis and hepatocellular carcinoma. However, the early diagnosis of MASH is challenging.

scTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies.

Nucleic acids research
Single-cell transcriptome-wide association studies (scTWAS) is a new method for conducting TWAS analysis at the cellular level to identify gene-trait associations with higher precision. This approach helps overcome the challenge of interpreting cell-...

CircaKB: a comprehensive knowledgebase of circadian genes across multiple species.

Nucleic acids research
Circadian rhythms, which are the natural cycles that dictate various physiological processes over a 24-h period, have been increasingly recognized as important in the management and treatment of various human diseases. However, the lack of sufficient...

Bioinformatics meets machine learning: identifying circulating biomarkers for vitiligo across blood and tissues.

Frontiers in immunology
BACKGROUND: Vitiligo is a skin disorder characterized by the progressive loss of pigmentation in the skin and mucous membranes. The exact aetiology and pathogenesis of vitiligo remain incompletely understood.