AIMC Topic: Transcriptome
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Enhancing single-cell classification accuracy using image conversion and deep learning.
Yi chuan = Hereditas
Mar 1, 2025
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...
Machine learning-driven immunophenotypic stratification of mixed connective tissue disease, corroborating the clinical heterogeneity.
Rheumatology (Oxford, England)
Mar 1, 2025
OBJECTIVE: The objective of this study was to stratify patients with MCTD, based on their immunophenotype.
stAI: a deep learning-based model for missing gene imputation and cell-type annotation of spatial transcriptomics.
Nucleic acids research
Feb 27, 2025
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
Feb 3, 2025
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
Feb 1, 2025
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
Jan 6, 2025
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
Jan 6, 2025
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
Jan 1, 2025
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.
Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-negative breast cancer.
Frontiers in immunology
Jan 1, 2025
BACKGROUND: Triple-negative breast cancer (TNBC) is a highly aggressive subtype with limited therapeutic targets and poor immunotherapy outcomes. The tumor microenvironment (TME) plays a key role in cancer progression. Advances in single-cell transcr...
Tumor-infiltrating immune cell signature score reveals prognostic biomarkers and therapeutic targets for colorectal cancer.
Frontiers in immunology
Jan 1, 2025
BACKGROUND: Colorectal cancer (CRC) is one of the leading contributors to cancer-related deaths worldwide, with more than 900,000 new diagnoses and related deaths each year. This study aims to explore the prognostic value of tumor-infiltrating immune...
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