Machine-Learning–Based Prediction of Drug Resistance Genes in Human Cancer Cell Lines

Abstract

Background: The problem of drug resistance has continued to be a formidable issue in the treatment of cancer, and in most instances, the patient turns out to fail in therapy and subsequently dies. The latest achievements in machine learning (ML) and high-throughput data in the biological realm have created new opportunities in predicting the drug resistance genes in human cancer cell lines. The combination of computational needs and cancer genomics and multi-omics data will have the potential to improve prediction accuracy and facilitate personalized cancer treatment.

Objective: The overall aim of the conducted research was to investigate the effectiveness of machine learning-based methods in predicting drug resistance genes in human cancer cell lines and study how machine learning familiarity, cancer genomics background, and multi-omics combination impact the predictive results.

Methodology: The research design employed was quantitative, which relied on the use of questionnaire-based data in finding the answer to the research question that involved 210 participants with background data in bioinformatics, computer science, biotechnology, and molecular biology. The statistical tests included a normality test, a reliability and validity test, an Independent Samples t -test, a One-Way ANOVA, a Kruskal-Wallis test, a Chi-Square Test of Independence, a Pearson correlation, and a multiple linear regression test. The data analysis was done to identify relationships, group variations, and predictive effects among major study variables.

Results: The findings were that the data were distributed normally, reliable, and valid. The statistical tests were used to provide an inference that found significant variations in machine learning familiarity between genders, the level of education, and the field of study. The use of human cancer cell line datasets was also significantly related to machine learning familiarity. The results of Pearson correlation showed moderate to strong positive correlations between machine learning familiarity, cancer genomics knowledge, multi-omics integration, and the impact of personalized therapy. The use of regression analysis revealed that all of the independent variables significantly and positively impacted the prediction of drug resistance, with the largest impact being observed in multi-omics integration.

Conclusion: The results of the research paper prove that machine learning-based strategies, to which specialized skills in cancer genomics are added, and multi-omics information sets are integrated, contribute to predicting drug resistance genes in human cancer cells a great deal. Such findings emphasize the relevance of cross-functional learning and elevated computational analysis to promote accuracy in oncology and individualized treatment of cancer.