AI Medical Compendium Topic:
Biomarkers, Tumor

Clear Filters Showing 931 to 940 of 1023 articles

Artificial intelligence-powered optimization of KI-67 assessment in breast cancer: enhancing precision and workflow efficiency. a literature review.

JPMA. The Journal of the Pakistan Medical Association
Breast Cancer (BC) has evolved from traditional morphological analysis to molecular profiling, identifying new subtypes. Ki-67, a prognostic biomarker, helps classify subtypes and guide chemotherapy decisions. This review explores how artificial inte...

[Identification of Protein-Coding Gene Markers in Breast Invasive Carcinoma Based on Machine Learning].

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae
Objective To screen out the biomarkers linked to prognosis of breast invasive carcinoma based on the analysis of transcriptome data by random forest (RF),extreme gradient boosting (XGBoost),light gradient boosting machine (LightGBM),and categorical b...

Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.

Cancer medicine
BACKGROUND: Ovarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literatu...

Construction and validation of artificial intelligence pathomics models for predicting pathological staging in colorectal cancer: Using multimodal data and clinical variables.

Cancer medicine
OBJECTIVE: This retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin-Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pa...

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Briefings in bioinformatics
Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations betwee...

A deep-learning workflow to predict upper tract urothelial carcinoma protein-based subtypes from H&E slides supporting the prioritization of patients for molecular testing.

The journal of pathology. Clinical research
Upper tract urothelial carcinoma (UTUC) is a rare and aggressive, yet understudied, urothelial carcinoma (UC). The more frequent UC of the bladder comprises several molecular subtypes, associated with different targeted therapies and overlapping with...

HRGCNLDA: Forecasting of lncRNA-disease association based on hierarchical refinement graph convolutional neural network.

Mathematical biosciences and engineering : MBE
Long non-coding RNA (lncRNA) is considered to be a crucial regulator involved in various human biological processes, including the regulation of tumor immune checkpoint proteins. It has great potential as both a cancer biomolecular biomarker and ther...

Predicting TNFRSF4 expression and prognosis in head and neck squamous cell carcinoma tissue: a pathological image analysis approach.

Polish journal of pathology : official journal of the Polish Society of Pathologists
Head and neck squamous cell carcinoma (HNSCC) exhibits a poor 5-year survival rate. TNFRSF4 is gaining attention in tumor therapy. The objective of this study was to forecast the expression of TNFRSF4 in HNSCC tissue using analysis of pathological im...

Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.

Technology in cancer research & treatment
Clear cell renal cell carcinoma (ccRCC) is a highly lethal urinary malignancy with poor overall survival (OS) rates. Integrating computer vision and machine learning in pathomics analysis offers potential for enhancing classification, prognosis, and ...