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Comparative analysis of regression algorithms for drug response prediction using GDSC dataset.

BMC Res Notes

January 2025

Department of Computer Engineering, Chungbuk National University, Chungdae-ro 1, Cheongju, 28644, Republic of Korea.

Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient.

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Background: Early Childhood Education and Care (ECEC) centers play an important role in fostering healthy dietary habits. The Nutrition Now project focusing on improving dietary habits during the first 1000 days of life. Central to the project is the implementation of an e-learning resource aimed at promoting feeding practices among staff and healthy dietary behaviours for children aged 0-3 years in ECEC.

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During public health crises such as pandemics, governments must rapidly adopt and implement wide-reaching policies and programs ("public policy interventions"). A key takeaway from the coronavirus disease 2019 (COVID-19) pandemic was that although numerous randomized controlled trials (RCTs) focussed on drugs and vaccines, few policy experiments were conducted to evaluate effects of public policy interventions across various sectors on viral transmission and other consequences. Moreover, many quasi-experimental studies were of spurious quality, thus proving unhelpful for informing public policy.

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Background: Human kinesin family member 11 (KIF11) plays a vital role in regulating the cell cycle and is implicated in the tumorigenesis and progression of various cancers, but its role in endometrial cancer (EC) is still unclear. Our current research explored the prognostic value, biological function and targeting strategy of KIF11 in EC through approaches including bioinformatics, machine learning and experimental studies.

Methods: The GSE17025 dataset from the GEO database was analyzed via the limma package to identify differentially expressed genes (DEGs) in EC.

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Background: Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.

Results: Compared with other methods, regression-based drug target affinity is more representative of the binding ability.

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