Deep Learning Methods in Predicting Gene Expression Levels for the Malaria Parasite

Abstract

Malaria is a mosquito-borne disease caused by single-celled blood parasites of the genus Plasmodium. The most severe cases of this disease are caused by the Plasmodium species, Falciparum. Once infected, a human host experiences symptoms of recurrent and intermittent fevers occurring over a time-frame of 48 hours, attributed to the synchronized developmental cycle of the parasite during the blood stage. To understand the regulated periodicity of Plasmodium falciparum transcription, this paper forecast and predict the P. falciparum gene transcription during its blood stage life cycle implementing a well-tuned recurrent neural network with gated recurrent units. Additionally, we also employ a spiking neural network to predict the expression levels of the P. falciparum gene. We provide results of this prediction on multiple genes including potential genes that express possible drug target enzymes. Our results show a high level of accuracy in being able to predict and forecast the expression levels of the different genes.