AIMC Topic: Plasmodium falciparum
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How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms.
Malaria journal
Jul 24, 2025
BACKGROUND: Machine learning algorithms have been used to predict malaria risk and severity, identify immunity biomarkers for malaria vaccine candidates, and determine molecular biomarkers of antimalarial drug resistance. Developing these prediction ...
Application of ConvNeXt with transfer learning and data augmentation for malaria parasite detection in resource-limited settings using microscopic images.
PloS one
Jun 4, 2025
Malaria continues to be a severe health problem across the globe, especially within resource-limited areas which lack both skilled diagnostic personnel and diagnostic equipment. This study investigates the use of deep learning diagnosis for malaria t...
Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity.
Molecular diversity
Apr 22, 2025
Malaria is a significant global health challenge, causing high morbidity and mortality. The rise of drug resistance highlights the urgent need for new antimalarial agents. This study focuses on predictive modeling of 104 Plasmodium falciparum protein...
MALDI-TOF mass spectrometry combined with machine learning algorithms to identify protein profiles related to malaria infection in human sera from Côte d'Ivoire.
Malaria journal
Apr 18, 2025
BACKGROUND: In sub-Saharan Africa, Plasmodium falciparum is the most prevalent species of malaria parasites. In endemic areas, malaria is mainly diagnosed using microscopy or rapid diagnostic tests (RDTs), which have limited sensitivity, and microsco...
Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes.
Communications biology
Mar 25, 2025
Continuous high-resolution imaging of the disease-mediating blood stages of the human malaria parasite Plasmodium falciparum faces challenges due to photosensitivity, small parasite size, and the anisotropy and large refractive index of host erythroc...
Deep learning-based malaria parasite detection: convolutional neural networks model for accurate species identification of Plasmodium falciparum and Plasmodium vivax.
Scientific reports
Jan 30, 2025
Accurate malaria diagnosis with precise identification of Plasmodium species is crucial for an effective treatment. While microscopy is still the gold standard in malaria diagnosis, it relies heavily on trained personnel. Artificial intelligence (AI)...
Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.
PloS one
Dec 23, 2024
Essential genes are those whose presence is vital for a cell's survival and growth. Detecting these genes in disease-causing organisms is critical for various biological studies, including understanding microbe metabolism, engineering genetically mod...
Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification.
Computers in biology and medicine
Dec 21, 2024
Over the past few decades, machine learning and deep learning (DL) have incredibly influenced a broader range of scientific disciplines. DL-based strategies have displayed superior performance in image processing compared to conventional standard met...
Diagnosis of infections using artificial intelligence techniques versus standard microscopy in a reference laboratory.
Journal of clinical microbiology
Dec 10, 2024
Diagnosing malaria using standard techniques is time-consuming. With limited staffing in many laboratories, this may lead to delays in reporting. Innovative technologies are changing the diagnostic landscape and may help alleviate staffing shortages....
Novel molecular inhibitor design for Plasmodium falciparum Lactate dehydrogenase enzyme using machine learning generated library of diverse compounds.
Molecular diversity
Aug 20, 2024
Generative machine learning models offer a novel strategy for chemogenomics and de novo drug design, allowing researchers to streamline their exploration of the chemical space and concentrate on specific regions of interest. In cases with limited inh...
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