A Machine Learning and Deep Learning based webapp used to predict multiple diseases.

Source files for building the IDM EMOD disease transmission model.

Detecting Malaria using Deep Learning 🦟🦠

An R interface to open-access malaria data, hosted by the Malaria Atlas Project.

A menu based multiple chronic disease detection system which will detect if a person is suffering from a severe disease by taking an essential input image.

A mobile app that will aid the Volunteer in sustaining life-saving malaria prevention tactics over their 2+ years of service.

Malaria Detection using Deep Learning

dEploid is designed for deconvoluting mixed genomes with unknown proportions. Traditional ‘phasing’ programs are limited to diploid organisms. Our method modifies Li and Stephen’s algorithm with Markov chain Monte Carlo (MCMC) approaches, and builds a generic framework that allows haloptype searches in a multiple infection setting.

Upscaling SV detection to a multi-population level.

Global Disease Database – Android app to gather images for disease detection

WebUI for the Reveal epidemiological surveillance platform

Application d’éducation sur le paludisme et d’accès aux services de santé au Congo. / Application of education on malaria and access to health services in Congo.

MOI and Allele Frequency Recovery from Noisy Polyallelic Genetics Data

Spatial individual-based model of malaria with a focus on drug resistance evolution.

Design and development of Peptide drugs against falciparum Malaria and a Deep learning Web App for Malaria Diagnosis

MicrOscopic VisuAlization of BLood cElls for the Detection of Malaria and CD4+

A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images

A mobile app that will aid the Volunteer in sustaining life-saving malaria prevention tactics over their 2+ years of service.