Fig. 1

Data analysis work flow for predicting active compounds against malaria. The computational approach benefits from both transcriptional drug repositioning and machine learning. a The transcriptional drug repositioning approach imports gene expression data from GEO dataset (GDS4259) and compares patients with severe malaria vs patients with mild malaria to get gene expression profile of malaria. Transcriptional drug repositioning approach developed in this work is then used to predict potentially active single agents. b The machine learning part is trained on a dataset of activity of 1540 compound combinations applied on three different malaria P. falciparum strains. Target prediction and pathway annotation is used to define the features. c All combinations of potentially active single agents were annotated with targets and pathways and used as a test set input to the machine learning model built. d Activity of all possible combinations were predicted and computationally validated. All possible combinations were also experimentally validated and full accuracy of the algorithm in practice was evaluated