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Development and experimental validation of a machine learning model for the prediction of new antimalarials.
BMC Chem
January 2025
Department of Pharmacy, Birla Institute of Technology and Science Pilani, Pilani Campus, Vidya Vihar, Pilani, Rajasthan, 333 031, India.
A large set of antimalarial molecules (N ~ 15k) was employed from ChEMBL to build a robust random forest (RF) model for the prediction of antiplasmodial activity. Rather than depending on high throughput screening (HTS) data, molecules tested at multiple doses against blood stages of Plasmodium falciparum were used for model development. The open-access and code-free KNIME platform was used to develop a workflow to train the model on 80% of data (N ~ 12k).
View Article and Find Full Text PDFPostoperative fever following surgery for oral cancer: Incidence, risk factors, and the formulation of a machine learning-based predictive model.
BMC Oral Health
January 2025
Department of Anaesthesiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Background: Postoperative fever (POF) is a common occurrence in patients undergoing major surgery, presenting challenges and burdens for both patients and surgeons yet. This study endeavors to examine the incidence, identify risk factors, and establish a machine learning-based predictive model for POF following surgery of oral cancer.
Methods: A total of seven hundred and twenty-seven consecutive patients undergoing radical resection of oral cancer were retrospectively investigated.
Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy.
BMC Pregnancy Childbirth
January 2025
Jiaxing Maternity and Child Health Care Hospital, Jiaxing, Zhejiang, 314001, China.
Background: Intrahepatic cholestasis of pregnancy (ICP) is a liver disorder that occurs in the second and third trimesters of pregnancy and is associated with a significant risk of fetal complications, including premature birth and fetal death. In clinical practice, the diagnosis of ICP is predominantly based on the presence of pruritus in pregnant women and elevated serum total bile acid. However, this approach may result in missed or delayed diagnoses.
View Article and Find Full Text PDFEnhancing individual glomerular filtration rate assessment: can we trust the equation? Development and validation of machine learning models to assess the trustworthiness of estimated GFR compared to measured GFR.
BMC Nephrol
January 2025
Department of Nephrology-Dialysis-Transplantation, University of Liège, CHU Sart Tilman, Liège, Belgium.
Background: Creatinine-based estimated glomerular filtration rate (eGFR) equations are widely used in clinical practice but exhibit inherent limitations. On the other side, measuring GFR is time consuming and not available in routine clinical practice. We developed and validated machine learning models to assess the trustworthiness (i.
View Article and Find Full Text PDFMachine learning models for water safety enhancement.
Sci Rep
January 2025
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, 4117-13114, Iran.
Humans encounter both natural and artificial radiation sources, including cosmic rays, primordial radionuclides, and radiation generated by human activities. These radionuclides can infiltrate the human body through various pathways, potentially leading to cancer and genetic mutations. A study was conducted using random sampling to assess the concentrations of radioactive isotopes and heavy metals in mineral water from Iran, consumable at Arak City.
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