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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.

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