AIMC Topic: Tumor Suppressor Protein p53

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Artificial Intelligence Based Study Association between p53 Gene Polymorphism and Endometriosis: A Systematic Review and Meta-analysis.

Computational intelligence and neuroscience
BACKGROUND: The P53 gene is critical to the onset and progression of cancers. Currently, relevant study findings indicate that the p53 gene may have a strong association with the risk of endometriosis, but these findings have not been united. To gath...

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study.

European radiology
OBJECTIVES: To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.

A deep learning method for predicting molecular properties and compound-protein interactions.

Journal of molecular graphics & modelling
Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep l...

A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy.

Bioorganic & medicinal chemistry
Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully trans...

Biologically informed deep neural network for prostate cancer discovery.

Nature
The determination of molecular features that mediate clinically aggressive phenotypes in prostate cancer remains a major biological and clinical challenge. Recent advances in interpretability of machine learning modelsĀ as applied to biomedical proble...

Combined Spiral Transformation and Model-Driven Multi-Modal Deep Learning Scheme for Automatic Prediction of TP53 Mutation in Pancreatic Cancer.

IEEE transactions on medical imaging
Pancreatic cancer is a malignant form of cancer with one of the worst prognoses. The poor prognosis and resistance to therapeutic modalities have been linked to TP53 mutation. Pathological examinations, such as biopsies, cannot be frequently performe...

Radiogenomics for predicting p53 status, PD-L1 expression, and prognosis with machine learning in pancreatic cancer.

British journal of cancer
BACKGROUND: Radiogenomics is an emerging field that integrates "Radiomics" and "Genomics". In the current study, we aimed to predict the genetic information of pancreatic tumours in a simple, inexpensive, and non-invasive manner, using cancer imaging...

In vitro and in silico genetic toxicity screening of flavor compounds and other ingredients in tobacco products with emphasis on ENDS.

Journal of applied toxicology : JAT
Electronic nicotine delivery systems (ENDS) are regulated tobacco products and often contain flavor compounds. Given the concern of increased use and the appeal of ENDS by young people, evaluating the potential of flavors to induce DNA damage is impo...