AIMC Topic: Transcriptome
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A multi-scale expression and regulation knowledge base for Escherichia coli.
Nucleic acids research
Oct 27, 2023
Transcriptomic data is accumulating rapidly; thus, scalable methods for extracting knowledge from this data are critical. Here, we assembled a top-down expression and regulation knowledge base for Escherichia coli. The expression component is a 1035-...
Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning.
Briefings in bioinformatics
May 19, 2023
The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. In recent years, the development of new high-throughput experimental and computational techniques has been a key driving force in d...
Graph deep learning enabled spatial domains identification for spatial transcriptomics.
Briefings in bioinformatics
May 19, 2023
Advancing spatially resolved transcriptomics (ST) technologies help biologists comprehensively understand organ function and tissue microenvironment. Accurate spatial domain identification is the foundation for delineating genome heterogeneity and ce...
Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH.
Cell reports. Medicine
Apr 18, 2023
Nonalcoholic steatohepatitis (NASH) is the most common chronic liver disease globally and a leading cause for liver transplantation in the US. Its pathogenesis remains imprecisely defined. We combined two high-resolution modalities to tissue samples ...
Improved drug response prediction by drug target data integration via network-based profiling.
Briefings in bioinformatics
Mar 19, 2023
Drug response prediction (DRP) is important for precision medicine to predict how a patient would react to a drug before administration. Existing studies take the cell line transcriptome data, and the chemical structure of drugs as input and predict ...
DIST: spatial transcriptomics enhancement using deep learning.
Briefings in bioinformatics
Mar 19, 2023
Spatially resolved transcriptomics technologies enable comprehensive measurement of gene expression patterns in the context of intact tissues. However, existing technologies suffer from either low resolution or shallow sequencing depth. Here, we pres...
The hitchhikers' guide to RNA sequencing and functional analysis.
Briefings in bioinformatics
Jan 19, 2023
DNA and RNA sequencing technologies have revolutionized biology and biomedical sciences, sequencing full genomes and transcriptomes at very high speeds and reasonably low costs. RNA sequencing (RNA-Seq) enables transcript identification and quantific...
DeepST: identifying spatial domains in spatial transcriptomics by deep learning.
Nucleic acids research
Dec 9, 2022
Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expres...
CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data.
Nucleic acids research
Jun 10, 2022
Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distributio...
Breaking the ageing paradigm in endometrium: endometrial gene expression related to cilia and ageing hallmarks in women over 35 years.
Human reproduction (Oxford, England)
Apr 1, 2022
STUDY QUESTION: Does age affect endometrial gene expression?
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