AI Medical Compendium Topic:
Biomarkers, Tumor

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The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood.

Cancer research
UNLABELLED: T cells recognize tumor antigens and initiate an anticancer immune response in the very early stages of tumor development, and the antigen specificity of T cells is determined by the T-cell receptor (TCR). Therefore, monitoring changes in...

Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis.

Neuro-oncology
BACKGROUND: Cellular senescence can have positive and negative effects on the body, including aiding in damage repair and facilitating tumor growth. Adamantinomatous craniopharyngioma (ACP), the most common pediatric sellar/suprasellar brain tumor, p...

Role of Machine Learning in Liquid Biopsy of Brain Tumours.

JPMA. The Journal of the Pakistan Medical Association
Liquid biopsy has multiple benefits and is used extensively in other fields of oncology, but its role in neuro-oncology has been limited so far. Multiple tumour-derived materials like circulating tumour cells (CTCs), tumour-educated platelets (TEPs),...

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

Experimental dermatology
This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE2...

Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma.

Journal of cellular and molecular medicine
Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO...

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data.

Briefings in bioinformatics
The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much rese...

Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning.

Briefings in functional genomics
Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is li...

Omics-based deep learning approaches for lung cancer decision-making and therapeutics development.

Briefings in functional genomics
Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized ...

Prognostic value of CDKN2A in head and neck squamous cell carcinoma via pathomics and machine learning.

Journal of cellular and molecular medicine
This study aims to enhance the prognosis prediction of Head and Neck Squamous Cell Carcinoma (HNSCC) by employing artificial intelligence (AI) to analyse CDKN2A gene expression from pathology images, directly correlating with patient outcomes. Our ap...