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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: Obesity is a multifactorial disease reaching pandemic proportions with increasing healthcare costs, advocating the development of better prevention and treatment strategies. Previous research indicates that the gut microbiome plays an important role in metabolic, hormonal, and neuronal cross-talk underlying eating behavior. We therefore aim to examine the effects of prebiotic and neurocognitive behavioral interventions on food decision-making and to assay the underlying mechanisms in a Randomized Controlled Trial (RCT).

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Background: Viruses that infect prokaryotes (phages) constitute the most abundant group of biological agents, playing pivotal roles in microbial systems. They are known to impact microbial community dynamics, microbial ecology, and evolution. Efforts to document the diversity, host range, infection dynamics, and effects of bacteriophage infection on host cell metabolism are extremely underexplored.

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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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Background: The integration of procedure-specific risks into preoperative patient assessment and optimization are crucial aspects of perioperative care. However, data on internists' knowledge of surgical and anesthetic principles and practices are limited. We thus sought to identify internists' knowledge gaps in terms of surgical- and anesthetic-specific risk factors and characteristics.

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