AIMC Topic: Transcriptome
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Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis.
Nature communications
Jul 2, 2025
Approximately 40% of patients with rheumatoid arthritis do not respond to individual biologic therapies, while biomarkers predictive of treatment response are lacking. Here we analyse RNA-sequencing (RNA-Seq) of pre-treatment synovial tissue from the...
Prioritizing perturbation-responsive gene patterns using interpretable deep learning.
Nature communications
Jul 2, 2025
Spatially resolved transcriptomics enables mapping of multiplexed gene expression within tissue contexts. While existing methods prioritize spatially variable genes within a single slice, few address identifying genes with differential spatial expres...
Cross modality learning of cell painting and transcriptomics data improves mechanism of action clustering and bioactivity modelling.
Scientific reports
Jul 2, 2025
In drug discovery, different data modalities (chemical structure, cell biology, quantum mechanics, etc.) are abundant, and their integration can help with understanding aspects of chemistry, biology, and their interactions. Within cell biology, cell ...
Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation.
Scientific reports
Jul 2, 2025
Lung adenocarcinoma (LUAD) is a major challenge in oncology due to its complex molecular structure and generally poor prognosis. The aim of this study was to find diagnostic markers and therapeutic targets for LUAD by integrating differential gene ex...
Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers.
Scientific reports
Jul 2, 2025
PANoptosis is closely associated with tumorigenesis and therapeutic response, yet its role in multiple myeloma (MM) remains unclear. This study analyzed bulk transcriptomic and clinical data from the TCGA and GEO databases to identify seven PANoptosi...
A Machine Learning Approach to Molecular Initiating Event Prediction Using High-Throughput Transcriptomic Chemical Screening Data.
Journal of chemical information and modeling
Jul 1, 2025
Improved scalability of high-throughput RNA-sequencing technologies has contributed to their proposed use in regulatory contexts for chemical hazard identification. However, the high dimensionality and size of these transcriptomic data sets present a...
stGRL: spatial domain identification, denoising, and imputation algorithm for spatial transcriptome data based on multi-task graph contrastive representation learning.
BMC biology
Jul 1, 2025
BACKGROUND: Spatial transcriptomics now enables sequencing while preserving the spatial location of cells. This significantly enhances researchers' understanding of cellular and tissue functions in their spatial context. However, due to current techn...
Lactylation associated biomarkers and immune infiltration in aortic dissection.
Scientific reports
Jul 1, 2025
Protein lactylation, a novel post-translational modification (PTM), has emerged as a critical factor in disease processes related to glycolysis and immune responses. However, its role in aortic dissection (AD) has yet to be thoroughly investigated. T...
Validation of an AI-enabled exome/transcriptome liquid biopsy platform for early detection, MRD, disease monitoring, and therapy selection for solid tumors.
Scientific reports
Jul 1, 2025
Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring of disease burden. Caris Assure is a multifunctional blood-based assay that couples whole exome and w...
Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis.
Scientific reports
Jul 1, 2025
Here we propose CovSF, a deep learning model designed to track and forecast short-term severity progression of COVID-19 patients using longitudinal clinical records. The motivation stems from the need for timely medical resource allocation, improved ...
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