Determining Positive Cancer Rescue Mutations in p53 Based Cancers by using Artificial Intelligence

TL;DR

This paper presents an AI-based neural network model to predict positive cancer rescue mutations in p53-related cancers, aiming to improve mutation detection and support medical research.

Contribution

The study introduces a novel neural network approach for identifying beneficial cancer mutations, outperforming existing methods in mutation effect prediction.

Findings

The neural network model achieved higher accuracy than traditional methods.

The approach successfully identified key mutation sites in p53 related cancers.

Results suggest potential for aiding personalized cancer treatment strategies.

Abstract

A mutation in a protein-coding gene in DNA can alter the protein structure coded by the same gene. Structurally altered proteins usually lose their functions and sometimes gain an undesirable function instead. These types of mutations and their effects can result in genetic diseases or antibiotic resistant bacteria, among other health issues. Important curing methods have been developed for detecting mutations against AIDS as well as genetic diseases. Another example is the influenza virus. The reasons why a vaccination developed to fight against influenza does not work the following year are (a) the mutation of its DNA and (b) the outbreak of the virus after it has been mutated especially if it is a virus that escaped the vaccinations target. Due to such reasons, it is highly important to know in advance the location of a potential mutation in a protein as well as the problems it might cause the medical sciences. In this study we have used artificial neural networks, which are one of the latest artificial intelligence technologies, to determine the effects of cancer mutations. The model we developed has given more successful results compared to other methods. We foresee that our model will bring a new dimension to medical research and the medical industry.