[HTML][HTML] Systematic review on the application of machine learning to quantitative structure–activity relationship modeling against Plasmodium falciparum
Malaria accounts for over two million deaths globally. To flatten this curve, there is a need to
develop new and high potent drugs against Plasmodium falciparum. Some major …
develop new and high potent drugs against Plasmodium falciparum. Some major …
Classification of Malaria Complication Using CART (Classification and Regression Tree) and Naïve Bayes
R Irmanita, SS Prasetiyowati, Y Sibaroni - Jurnal RESTI (Rekayasa …, 2021 - jurnal.iaii.or.id
Malaria is a disease caused by the Plasmodium parasite that transmitted by female
Anopheles mosquitoes. Malaria can become a dangerous disease if late have the medical …
Anopheles mosquitoes. Malaria can become a dangerous disease if late have the medical …
[HTML][HTML] Risk assessment of imported malaria in China: a machine learning perspective
S Yang, R Li, S Yan, H Yang, Z Cao, L Zhang, J Xue… - BMC Public Health, 2024 - Springer
Abstract Background Following China's official designation as malaria-free country by WHO,
the imported malaria has emerged as a significant determinant impacting the malaria …
the imported malaria has emerged as a significant determinant impacting the malaria …
[PDF][PDF] Machine learning for artemisinin resistance in malaria treatment across in vivo-in vitro platforms
Drug resistance has been rapidly evolving with regard to the first-line malaria treatment,
artemisinin-based combination therapies. It has been an open question whether predictive …
artemisinin-based combination therapies. It has been an open question whether predictive …
[HTML][HTML] Modeling plasmodium falciparum diagnostic test sensitivity using machine learning with histidine-rich Protein 2 variants
CT Ford, GS Alemayehu, K Blackburn… - Frontiers in Tropical …, 2021 - frontiersin.org
Malaria, predominantly caused by Plasmodium falciparum, poses one of largest and most
durable health threats in the world. Previously, simplistic regression-based models have …
durable health threats in the world. Previously, simplistic regression-based models have …
Application of Machine Learning for Detection and Prediction of Malaria: A Review
M Apetorgbor, V Wankhede, K Dakhare… - … for Innovation in …, 2024 - ieeexplore.ieee.org
Mosquitoes carries a parasitic infection that affects millions of peoples globally. The initial
and meticulous prediction of outbursts, patient verdict, and treatment enhancement can all …
and meticulous prediction of outbursts, patient verdict, and treatment enhancement can all …
Extreme phenotype sampling improves lasso and random forest marker selection for complex traits
Most attempts to fit a supervised machine learning (ML) model in bioinformatics try to predict
the full range of trait or response values. While such prediction tasks effectively capture the …
the full range of trait or response values. While such prediction tasks effectively capture the …
Machine learning applied to prediction, control and planning from dynamic epidemiological models
O Bent - 2020 - ora.ox.ac.uk
We are ever aware of the global impact of infectious disease transmission in shaping the
reality of the world around us. For much of human existence transmitted infections have …
reality of the world around us. For much of human existence transmitted infections have …
Predicting Dihydroartemisinin Resistance in Plasmodium falciparum using Pathway Activity Inference
Drug resistance threatens the effectiveness of treatments of infectious diseases, particularly
on the global scale where mutation is rapid, mechanisms of resistance are developing or …
on the global scale where mutation is rapid, mechanisms of resistance are developing or …