AIMC Topic: Plasmodium falciparum

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Machine learning enables de novo multiepitope design of circumsporozoite protein to target trimeric L9 antibody.

Proceedings of the National Academy of Sciences of the United States of America
Currently approved vaccines for the prevention of malaria provide only partial protection against disease due to high variability in the quality of induced antibodies. These vaccines present the unstructured central repeat region, as well as the C-te...

DANet a lightweight dilated attention network for malaria parasite detection.

Scientific reports
Malaria remains a critical global health challenge, requiring accurate and efficient diagnostic tools, particularly in developing countries with limited medical expertise. Detecting malaria parasites from red blood cell (RBC) blood smear images is ch...

How to use learning curves to evaluate the sample size for malaria prediction models developed using machine learning algorithms.

Malaria journal
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
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
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
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
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
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
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
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...