AIMC Topic: Malaria

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Deep Learning-driven research for drug discovery: Tackling Malaria.

PLoS computational biology
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is ...

A new backpropagation neural network classification model for prediction of incidence of malaria.

Frontiers in bioscience (Landmark edition)
Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classifie...

A novel model for malaria prediction based on ensemble algorithms.

PloS one
BACKGROUND AND OBJECTIVE: Most previous studies adopted single traditional time series models to predict incidences of malaria. A single model cannot effectively capture all the properties of the data structure. However, a stacking architecture can s...

Discovery of potential 1,3,5-Triazine compounds against strains of Plasmodium falciparum using supervised machine learning models.

European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
The Malaria burden was an escalating global encumbrance and need to be addressed with critical care. Anti-malarial drug discovery was integrated with supervised machine learning (ML) models to identify potent thiazolyl-traizine derivatives. This assi...

Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears.

IEEE journal of biomedical and health informatics
OBJECTIVE: This work investigates the possibility of automated malaria parasite detection in thick blood smears with smartphones.

Age grading An. gambiae and An. arabiensis using near infrared spectra and artificial neural networks.

PloS one
BACKGROUND: Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into < or ≥ 7 days old with an average accuracy of 80%, achieved by training a re...

Using mid-infrared spectroscopy and supervised machine-learning to identify vertebrate blood meals in the malaria vector, Anopheles arabiensis.

Malaria journal
BACKGROUND: The propensity of different Anopheles mosquitoes to bite humans instead of other vertebrates influences their capacity to transmit pathogens to humans. Unfortunately, determining proportions of mosquitoes that have fed on humans, i.e. Hum...

Artificial Intelligence for infectious disease Big Data Analytics.

Infection, disease & health
BACKGROUND: Since the beginning of the 21st century, the amount of data obtained from public health surveillance has increased dramatically due to the advancement of information and communications technology and the data collection systems now in pla...

Identification of a Novel Clinical Phenotype of Severe Malaria using a Network-Based Clustering Approach.

Scientific reports
The parasite Plasmodium falciparum is the main cause of severe malaria (SM). Despite treatment with antimalarial drugs, more than 400,000 deaths are reported every year, mainly in African children. The diversity of clinical presentations associated w...

Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells.

Journal of biophotonics
Accurate and immediate diagnosis of malaria is important for medication of the infectious disease. Conventional methods for diagnosing malaria are time consuming and rely on the skill of experts. Therefore, an automatic and simple diagnostic modality...