Gopal Krishna Ramadas Dhondalay PhD

Project | 09

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Artificial neural network inference (ANNI): a study on gene-gene interaction for biomarkers in childhood sarcomas

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To model the potential interaction between previously identified biomarkers in children's sarcomas using artificial neural network inference (ANNI). To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children's small round blue cell tumours (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimise the risk of over-fitting and to optimise the generalisation ability of the model. Strong connections linking certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing's sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signalling pathways, including Wnt, Fas/Rho and intracellular oxygen.

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A publication in the Journal of PLoS ONE was published based on this project.​