AIMC Topic: Transcriptome
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An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks.
Scientific reports
May 31, 2025
Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the foundation for predicting patient prognosis, despite variation across staging groups. The consensus m...
Integrated machine learning survival framework develops a prognostic model based on macrophage-related genes and programmed cell death signatures in a multi-sample Kidney renal clear cell carcinoma.
Cell biology and toxicology
May 30, 2025
BACKGROUND: Macrophages are closely associated with the progression of Kidney renal clear cell carcinoma (KIRC) and can influence programmed cell death (PCD) of tumour cells. To identify prognostic biomarkers for KIRC, it is essential to investigate ...
scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links.
Nature communications
May 29, 2025
Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our u...
Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets.
Scientific reports
May 27, 2025
Saliva, a non-invasive, self-collected liquid biopsy, holds promise for early gastric cancer (GC) screening. This study aims to assess the potential of saliva as a proxy for malignant gastric transformation and its diagnostic value through transcript...
Prediction of Drug-Induced Nephrotoxicity Using Chemical Information and Transcriptomics Data.
Journal of chemical information and modeling
May 26, 2025
Prediction of drug-induced nephrotoxicity is an important task in the drug discovery and development pipeline. Chemical information-based machine learning models are used in general for nephrotoxicity prediction as a part of computational modeling. C...
CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders.
Nucleic acids research
May 22, 2025
Correcting for confounding variables is often overlooked when computing RNA-RNA correlations, even though it can profoundly affect results. We introduce CorrAdjust, a method for identifying and correcting such hidden confounders. CorrAdjust selects a...
Machine learning using scRNA-seq Combined with bulk-seq to identify lactylation-related hub genes in carotid arteriosclerosis.
Scientific reports
May 22, 2025
Atherosclerosis is a chronic inflammatory disease, this study aims to investigate the immune landscape in carotid atherosclerotic plaque formation and explore diagnostic biomarkers of lactylation-associated genes, so as to gain new insights into unde...
Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis.
Nature communications
May 22, 2025
Understanding how plants adapt their physiology to overcome severe and often multifactorial stress conditions in nature is vital in light of the climate crisis. This remains a challenge given the complex nature of the underlying molecular mechanisms....
Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.
Scientific reports
May 19, 2025
Osteosarcoma, an aggressive bone malignancy predominantly affecting children and adolescents, is characterized by a poor prognosis and high mortality rates. The development of reliable prognostic tools is critical for advancing personalized treatment...
MixOmics Integration of Biological Datasets Identifies Highly Correlated Variables of COVID-19 Severity.
International journal of molecular sciences
May 15, 2025
Despite several years passing since the COVID-19 pandemic was declared, challenges remain in understanding the factors that can predict the severity of COVID-19 disease and complications of SARS-CoV-2 infection. While many large-scale multi-omic data...
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