International Journal of Science, Engineering and TechnologyAuthors: Gurram Lavanya, Ch.Naveen
Abstract: It is critical to investigate the link between treatment efficacy and sensitivity to mutational patterns in order to successfully treat complex diseases such as cancer. Specifically, cancer drugs may lose some of their efficacy over time as a result of new mutations, and cancer cells themselves are continually changing as a result of ongoing mutations. The main purpose of this research is to analyse the relationship between medications, disorders, and genes using statistical methods. To further anticipate the drug sensitivity of cancer cells based on genomic changes, a generic processing pipeline and machine learning models were developed. This was accomplished by comparing an improved database to four well-known open-source databases: one that had information on drug sensitivity in cancer cell lines, one that contained resources for somatic mutation data, and the other that contained information on gene-drug interactions. Text encoding, filtering, and optimisation were among the preprocessing procedures used to provide a fresh, enhanced dataset for use in machine learning and statistical analysis. Statistics were run on the supplemented database to find out how much of an impact gene-drug interactions had on drug sensitivity. On the other hand, machine learning algorithms that have been trained on datasets of drug interactions or somatic mutations may be used to forecast drug sensitivity. Incorporating feature significance and ablation studies, the research aims to provide a thorough analysis of gene and pharmaceutical sensitivity. A constant R2 score of 0.73 across several data sources, on top of an R2 score of 0.91 in early testing, demonstrated good generalizability for the built pipeline. Ablation studies, statistical analysis, and machine learning all provide new perspectives to the field of drug sensitivity prediction.
DOI: http://doi.org/10.5281/zenodo.20281858
