Modeling Plasmodium falciparum Diagnostic Test Sensitivity using Machine Learning with Histidine-Rich Protein 2 Variants

Cheminformatic analysis of 8-AQ fluorescent probes

Binary Classification of images of cells which are either uninfected or parasitized by malaria.

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Implementation of a deep learning model for leukemia classification and malaria detection using CNNs. The model utilizes transfer learning with VGG19 and InceptionV3 architectures, trained on custom datasets. The code includes data preprocessing, model training, evaluation, and visualization of performance metrics. Achieved high accuracy

HackTheBite is a non profit online course platform, that aims to fight malaria.

Complete source code and intermediary files to accompany the manuscript published in Nature Medicine.

iPfal17 genome-scale metabolic reconstruction; lipid metabolism curation

Neural network that recognizes malaria in blood cells by image

Analysis code for malaria

The repository contains the dataset of unknown variables and the script that is used to train this data

Cleaning and preliminary analysis of mosquito/blood data

Greater Mekong Subregion - Malaria Vector Risk Assessment

In this study we aimed to 1) describe malaria transmission through estimation of seroconversion and seroreversion rates using serological and molecular tools and 2) determine factors associated with subpopulations with high malaria exposure in low transmission settings in Indiana and Belen

Team Flask Capstone Project - Hamoye Winter '23 Cohort

This repository contains data and code for our analysis of the genome of a Plasmodium falciparum strain with reduced susceptibility to Artemisinin-Based Combination Therapy (ACT) drugs. We performed whole-genome sequencing of the HSOG3 clinical isolate and compared it with a large dataset of global P. falciparum samples.

Recognize and classify images of cells that are parasitized by malaria using a convolutional neural network.

Image Classification to identify whether Red Blood Cells are infected with Malaria or not.