Year: 2024
Type: article
Abstract: The advent of personalized medicine has highlighted the importance of predicting drug sensitivity in cancer treatment. This paper presents a predictive modeling approach that leverages genomic profiles and ML methods to forecast a sensitivity of cancer cells to various drugs. By analyzing a comprehensive dataset encompassing genomic features, drug characteristics, and response metrics, we develop a robust model to estimate drug effectiveness. Our methodology involves Predicting the natural log of the half-maximal inhibitory concentration (LN_IC50), a crucial indicator of drug sensitivity, requires preprocessing the data, choosing pertinent features, and training several machine learning models . A results demonstrate the potential of integrating genomic information with advanced analytics to enhance the precision of drug response predictions, thereby contributing to the creation of more efficient and customized cancer therapies.
Author: Miss. Neeta Annasaheb Rahane
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409947745