AIMC Topic: Transcriptome

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CanCellCap: robust cancer cell capture across tissue types on single-cell RNA-seq data by multi-domain learning.

BMC biology
BACKGROUND: The advent of single-cell RNA sequencing (scRNA-seq) has provided unprecedented insights into cancer cellular diversity, enabling a comprehensive understanding of cancer at the single-cell level. However, identifying cancer cells remains ...

CYCLONE: recycle contrastive learning for integrating single-cell gene expression data.

BMC bioinformatics
BACKGROUND: Combining single-cell transcriptome sequencing results from several batches reduces batch effect, which improves our understanding of cellular identity and function.

SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics.

Genome biology
Spatially resolved transcriptomics (SRT) for characterizing spatial cellular heterogeneities in tissue environments requires systematic analytical approaches to elucidate gene expression variations within their physiological context. Here, we introdu...

Integrative transcriptomics and metabolomics reveal neuroendocrine-lipid crosstalk and adenosine signaling in broiler under heat stress.

BMC genomics
BACKGROUND: Heat stress (HS) is a significant challenge in poultry, negatively impacting feed efficiency and survival. These adaptive responses could lead to disrupted lipid metabolism, impaired immunity, and neural damage. We hypothesized that the n...

An autoencoder learning method for predicting breast cancer subtypes.

PloS one
Heterogeneity of breast cancer poses several challenges for detection and treatment. With next-generation sequencing, we can now map the transcriptional profile of each patient's breast tissue, which has the potential for identifying and characterizi...

Integrating transcriptomics, network analysis, and single-cell RNA sequencing to identify and validate key target genes of gynostemma in the treatment of non-alcoholic fatty liver disease.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
This study explores the therapeutic targets and mechanisms of Gynostemma pentaphyllum in non-alcoholic fatty liver disease (NAFLD). Using network analysis and bioinformatics, we identified target genes of Gynostemma's active metabolites in NAFLD thro...

Prediction of hub genes in pulpal inflammation and regeneration using autoencoders and a generative AI approach.

Scientific reports
Pulpal inflammation and regeneration are crucial for enhancing endodontic treatment outcomes. Transcriptomic studies highlight the involvement of proinflammatory cytokines, NF-κB signaling, and stem cell activity. This study employs a generative AI a...

STHD: probabilistic cell typing of single spots in whole transcriptome spatial data with high definition.

Genome biology
Recent advances in spatial transcriptomics technologies have enabled gene expression profiling across the transcriptome in spots with subcellular resolution, but high sparsity and dimensionality present significant computational challenges. We presen...

Using machine learning to discover DNA metabolism biomarkers that direct prostate cancer treatment.

Scientific reports
DNA metabolism genes play pivotal roles in the regulation of cellular processes that contribute to cancer progression, immune modulation, and therapeutic response in prostate cancer (PC). Understanding the mechanisms by which these genes influence th...