15 research outputs found
Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas
Objective: To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI).
Method: To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (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 minimize the risk of over-fitting and to optimize generalization ability of the model.
Results: Strong connection links on 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 signaling pathways, including Wnt, Fas/Rho and intracellular oxygen.
Conclusions: The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas
Summary of the interaction algorithm parameters.
<p>Summary of the interaction algorithm parameters.</p
A simplified interactome network map for the 96 selected genes by ANN network inference algorithm.
<p>The red node is the genes with high expression values in either of the sarcoma cancers. The gray node is the gene with high expression values in more than one cancer groups in which one of these groups is sarcoma cancer. Yellow node is the genes with low expression values in both sarcoma cancers.</p
Diagrammatic representation of the interaction algorithm.
<p>Diagrammatic representation of the interaction algorithm.</p
Association of DACH1 protein with other breast cancer biomarkers.
<p>Association of DACH1 protein with other breast cancer biomarkers.</p
Kaplan-Meier plots modelling DACH1 expression with 5 year post-diagnostic a) specific survival, b) tumour recurrence, and c) distant metastasis.
<p>All were significant at p<0.001.</p
Association of DACH1 expression with clinicopathological factors. N = number of samples. Statistically significant p-values are in bold.
<p>Association of DACH1 expression with clinicopathological factors. N = number of samples. Statistically significant p-values are in bold.</p