AIMC Topic: Tumor Suppressor Protein p53
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Non-invasive identification of TKI-resistant NSCLC: a multi-model AI approach for predicting EGFR/TP53 co-mutations.
BMC pulmonary medicine
Jul 10, 2025
OBJECTIVES: To investigate the value of multi-model based on preoperative CT scans in predicting EGFR/TP53 co-mutation status.
Combining the NanaPPI Toolbox and AI-Driven Virtual Inhibitor Screening for the p53-MDM2 Interaction.
Analytical chemistry
Jul 7, 2025
High-throughput screening for inhibitors of protein-protein interactions (PPIs) provides vital information for therapeutic intervention in diseases driven by aberrant PPIs. Traditionally, the discovery of PPI inhibitors involves sequential steps: in ...
A radiogenomics study on F-FDG PET/CT in endometrial cancer by a novel deep learning segmentation algorithm.
BMC cancer
Jun 5, 2025
OBJECTIVE: To create an automated PET/CT segmentation method and radiomics model to forecast Mismatch repair (MMR) and TP53 gene expression in endometrial cancer patients, and to examine the effect of gene expression variability on image texture feat...
The Role of Hydrogen Sulfide in the Localization and Structural-Functional Organization of p53 Following Traumatic Brain Injury: Development of a YOLO Model for Detection and Quantification of Apoptotic Nuclei.
International journal of molecular sciences
May 24, 2025
Traumatic brain injury (TBI) triggers a cascade of molecular and cellular disturbances, including apoptosis, inflammation, and destabilization of neuronal connections. The transcription factor p53 plays a pivotal role in regulating cell fate followin...
Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.
Journal of thoracic imaging
May 1, 2025
PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of epidermal growth factor receptor ( EGFR ) and TP53 mutations and to assess the models' capacities t...
Exploring the potential of machine learning in gastric cancer: prognostic biomarkers, subtyping, and stratification.
BMC cancer
Apr 30, 2025
BACKGROUND: Advancements in the management of gastric cancer (GC) and innovative therapeutic approaches highlight the significance of the role of biomarkers in GC prognosis. Machine-learning (ML)-based methods can be applied to identify the most impo...
Interpretable multi-stage attention network to predict cancer subtype, microsatellite instability, TP53 mutation and TMB of endometrial and colorectal cancer.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 30, 2025
Mismatch repair deficiency (dMMR), also known as high-grade microsatellite instability (MSI-H), is a well-established biomarker for predicting the immunotherapy response in endometrial cancer (EC) and colorectal cancer (CRC). Tumor mutational burden ...
Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features.
BMC cancer
Jan 9, 2025
OBJECTIVES: To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).
Novel anoikis-related diagnostic biomarkers for aortic dissection based on machine learning.
Scientific reports
Dec 28, 2024
Aortic dissection (AD) is one of the most dangerous diseases of the cardiovascular system, which is characterized by acute onset and poor prognosis, while the pathogenesis of AD is still unclear and may affect or even delay the diagnosis of AD. Ancho...
Role of artificial intelligence in cancer detection using protein p53: A Review.
Molecular biology reports
Dec 11, 2024
Normal cell development and prevention of tumor formation rely on the tumor-suppressor protein p53. This crucial protein is produced from the Tp53 gene, which encodes the p53 protein. The p53 protein plays a vital role in regulating cell growth, DNA ...
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