AIMC Topic: Transcriptome
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Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators.
Cell metabolism
Apr 10, 2024
Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately ident...
Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis.
Computers in biology and medicine
Apr 9, 2024
Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly spec...
A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.
International journal of surgery (London, England)
Apr 1, 2024
BACKGROUND: Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), ...
Evaluation of Clinically Significant miRNAs Level by Machine Learning Approaches Utilizing Total Transcriptome Data.
Doklady. Biochemistry and biophysics
Mar 27, 2024
Analysis of the mechanisms underlying the occurrence and progression of cancer represents a key objective in contemporary clinical bioinformatics and molecular biology. Utilizing omics data, particularly transcriptomes, enables a detailed characteriz...
TENET: Triple-enhancement based graph neural network for cell-cell interaction network reconstruction from spatial transcriptomics.
Journal of molecular biology
Mar 18, 2024
Cellular communication relies on the intricate interplay of signaling molecules, forming the Cell-cell Interaction network (CCI) that coordinates tissue behavior. Researchers have shown the capability of shallow neural networks in reconstructing CCI,...
Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning.
Biochemical genetics
Feb 21, 2024
Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate th...
Deep learning-based quantification and transcriptomic profiling reveal a methyl jasmonate-mediated glandular trichome formation pathway in Cannabis sativa.
The Plant journal : for cell and molecular biology
Feb 8, 2024
Cannabis glandular trichomes (GTs) are economically and biotechnologically important structures that have a remarkable morphology and capacity to produce, store, and secrete diverse classes of secondary metabolites. However, our understanding of the ...
Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base.
BMC bioinformatics
Feb 7, 2024
BACKGROUND: Recent developments in the domain of biomedical knowledge bases (KBs) open up new ways to exploit biomedical knowledge that is available in the form of KBs. Significant work has been done in the direction of biomedical KB creation and KB ...
CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Feb 2, 2024
Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficu...
Compound Activity Prediction with Dose-Dependent Transcriptomic Profiles and Deep Learning.
Journal of chemical information and modeling
Jan 31, 2024
Predicting compound activity in assays is a long-standing challenge in drug discovery. Computational models based on compound-induced gene expression signatures from a single profiling assay have shown promise toward predicting compound activity in o...
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