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| http://dx.doi.org/10.1016/j.jacadv.2023.100552 | DOI Listing |
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Development and experimental validation of a machine learning model for the prediction of new antimalarials.
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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 PDFMiCML: a causal machine learning cloud platform for the analysis of treatment effects using microbiome profiles.
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Department of Applied Mathematics and Statistics, The State University of New York, Korea, Incheon, South Korea.
Background: The treatment effects are heterogenous across patients due to the differences in their microbiomes, which in turn implies that we can enhance the treatment effect by manipulating the patient's microbiome profile. Then, the coadministration of microbiome-based dietary supplements/therapeutics along with the primary treatment has been the subject of intensive investigation. However, for this, we first need to comprehend which microbes help (or prevent) the treatment to cure the patient's disease.
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.
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Department of Anaesthesiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
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Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy.
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Jiaxing Maternity and Child Health Care Hospital, Jiaxing, Zhejiang, 314001, China.
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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.
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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.
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