title: Artificial neural network inference (ANNI): a study on gene-gene interaction for biomarkers in childhood sarcomas.
abstract: OBJECTIVE
  To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI).
  
  
  METHODS
  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.
authors.affiliation: The John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom.
authors.forename: Dong Ling
authors.initials: DL
authors.lastname: Tong
authors.affiliation: The John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom.
authors.forename: David J
authors.initials: DJ
authors.lastname: Boocock
authors.affiliation: Imperial Centre for Translational and Experimental Medicine, National Heart and Lung Institute (NHLI), Imperial College London, London, United Kingdom.
authors.forename: Gopal Krishna R
authors.initials: GK
authors.lastname: Dhondalay
authors.affiliation: Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America.
authors.forename: Christophe
authors.initials: C
authors.lastname: Lemetre
authors.affiliation: The John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom.
authors.forename: Graham R
authors.initials: GR
authors.lastname: Ball
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D002648:Child; D004843:Epistasis, Genetic; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D052138:Genes, Neoplasm; D006801:Humans; D008957:Models, Genetic; D016571:Neural Networks, Computer; D012509:Sarcoma / Q000235:genetics*
chemical_list: D014408:Biomarkers, Tumor