AI Medical Compendium Topic

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Biomarkers, Tumor

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Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Cancer Detection.

Medicina (Kaunas, Lithuania)
: Breast cancer (BC) is the most common type of cancer in women, accounting for more than 30% of new female cancers each year. Although various treatments are available for BC, most cancer-related deaths are due to incurable metastases. Therefore, th...

Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study.

JCO clinical cancer informatics
PURPOSE: Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic response varies among patients. This study aimed to predict tumor response to nivolumab anti-PD-1 antibody ...

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.

Scientific reports
Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data ...

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.

Molecular cancer
Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern ...

Machine learning technique-based four-autoantibody test for early detection of esophageal squamous cell carcinoma: a multicenter, retrospective study with a nested case-control study.

BMC medicine
BACKGROUND: Autoantibodies represent promising diagnostic blood-based biomarkers that may be generated prior to the first clinically detectable signs of cancers. In present study, we aimed to identify a novel optimized autoantibody panel with high di...

Feature selection for classification based on machine learning algorithms for prostate cancer.

Biomedical physics & engineering express
Microarray technology has transformed the biotechnological research to next level in the recent years. It provides the expression levels of various genes involved in a particular disease. Prostate cancer disease turned into life threatening cancer. T...

Machine Learning-Enhanced Cerebrospinal Fluid N-Glycome for the Diagnosis and Prognosis of Primary Central Nervous System Lymphoma.

Journal of proteome research
The diagnosis and prognosis of Primary Central Nervous System Lymphoma (PCNSL) present significant challenges. In this study, the potential use of machine learning algorithms in diagnosing and predicting the prognosis for PCNSL based on cerebrospinal...

Machine learning-based identification of cuproptosis-related lncRNA biomarkers in diffuse large B-cell lymphoma.

Cell biology and toxicology
Multiple machine learning techniques were employed to identify key long non-coding RNA (lncRNA) biomarkers associated with cuproptosis in Diffuse Large B-Cell Lymphoma (DLBCL). Data from the TCGA and GEO databases facilitated the identification of 12...

Identification of a telomere-related gene signature for the prognostic and immune landscape prediction in head and neck squamous cell carcinoma by integrated analysis of machine learning and Mendelian randomization.

Medicine
Telomere-related genes (TRGs) are vital in diverse tumor types. Nevertheless, there is a notable lack of in-depth research concerning their significance in head and neck squamous cell carcinoma (HNSCC). In this context, the present study aims to asse...