title: Familial or Sporadic Idiopathic Scoliosis - classification based on artificial neural network and GAPDH and ACTB transcription profile.
abstract: BACKGROUND
  Importance of hereditary factors in the etiology of Idiopathic Scoliosis is widely accepted. In clinical practice some of the IS patients present with positive familial history of the deformity and some do not. Traditionally about 90% of patients have been considered as sporadic cases without familial recurrence. However the exact proportion of Familial and Sporadic Idiopathic Scoliosis is still unknown. Housekeeping genes encode proteins that are usually essential for the maintenance of basic cellular functions. ACTB and GAPDH are two housekeeping genes encoding respectively a cytoskeletal protein β-actin, and glyceraldehyde-3-phosphate dehydrogenase, an enzyme of glycolysis. Although their expression levels can fluctuate between different tissues and persons, human housekeeping genes seem to exhibit a preserved tissue-wide expression ranking order. It was hypothesized that expression ranking order of two representative housekeeping genes ACTB and GAPDH might be disturbed in the tissues of patients with Familial Idiopathic Scoliosis (with positive family history of idiopathic scoliosis) opposed to the patients with no family members affected (Sporadic Idiopathic Scoliosis). An artificial neural network (ANN) was developed that could serve to differentiate between familial and sporadic cases of idiopathic scoliosis based on the expression levels of ACTB and GAPDH in different tissues of scoliotic patients. The aim of the study was to investigate whether the expression levels of ACTB and GAPDH in different tissues of idiopathic scoliosis patients could be used as a source of data for specially developed artificial neural network in order to predict the positive family history of index patient.
  
  
  RESULTS
  The comparison of developed models showed, that the most satisfactory classification accuracy was achieved for ANN model with 18 nodes in the first hidden layer and 16 nodes in the second hidden layer. The classification accuracy for positive Idiopathic Scoliosis anamnesis only with the expression measurements of ACTB and GAPDH with the use of ANN based on 6-18-16-1 architecture was 8 of 9 (88%). Only in one case the prediction was ambiguous.
  
  
  CONCLUSIONS
  Specially designed artificial neural network model proved possible association between expression level of ACTB, GAPDH and positive familial history of Idiopathic Scoliosis.
authors.affiliation: Institute of Computer Science, Division of Biomedical Computer Systems, University of Silesia, Katowice, Poland. t.waller@biobyte.pl
authors.forename: Tomasz
authors.initials: T
authors.lastname: Waller
authors.forename: Roman
authors.initials: R
authors.lastname: Nowak
authors.forename: Magdalena
authors.initials: M
authors.lastname: Tkacz
authors.forename: Damian
authors.initials: D
authors.lastname: Zapart
authors.forename: Urszula
authors.initials: U
authors.lastname: Mazurek
mesh_terms: D000199:Actins / Q000235:genetics*; D005069:Evaluation Studies as Topic; D005260:Female; D020043:Genes, Essential; D005987:Glyceraldehyde-3-Phosphate Dehydrogenases / Q000235:genetics*; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D012333:RNA, Messenger / Q000235:genetics; D020133:Reverse Transcriptase Polymerase Chain Reaction; D012600:Scoliosis / Q000145:classification* / Q000175:diagnosis / Q000235:genetics
chemical_list: D000199:Actins; D012333:RNA, Messenger; D005987:Glyceraldehyde-3-Phosphate Dehydrogenases