Open Access

Familial or Sporadic Idiopathic Scoliosis – classification based on artificial neural network and GAPDH and ACTB transcription profile

  • Tomasz Waller1Email author,
  • Roman Nowak2,
  • Magdalena Tkacz4,
  • Damian Zapart1 and
  • Urszula Mazurek3
Contributed equally
BioMedical Engineering OnLine201312:1

https://doi.org/10.1186/1475-925X-12-1

Received: 9 August 2012

Accepted: 4 December 2012

Published: 4 January 2013

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.

Background

Idiopathic Scoliosis (IS) is the most common structural deformity of the human spine. Although the exact cause or causes of idiopathic scoliosis are still unknown there is convincing evidence supporting a genetic aetiology of this disorder [15]. Importance of hereditary factors in the etiology of IS is underlined by increased risk of scoliosis among the first-degree relatives of index patients. Harrington found scoliosis incidence of 27% among the first degree relatives. [6] Other studies indicate 11% of first degree and 2,4% and 1,4% of second and third degree relatives are affected [7, 8]. Genetic basis of IS is also supported by the results of the twin studies. Inoue and colleagues found the concordance rate of scoliosis of 92,3% in monozygous, decreasing to 62,5% in dizygous twins [9]. Lower concordance rate was found in the study of Kesling and al, 73% among monozygous and 36% among dizygous twins [10]. Recent study based on the Swedish Twin Registry estimates that heritability of this condition is 38% indicating the importance of other still unknown factors in the development of the deformity [11]. Mode of inheritance and genetic basis of the scoliotic phenotype are still not definitively determined. Autosomal dominant mode, X-linked as well as multifactorial inheritance patterns were suggested [37]. According to Miller et al. idiopathic scoliosis is a complex genetic disorder involving one or more genetic loci and complex genetic interactions for disease expression [5]. 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 [1]. However the exact proportion of Familial and Sporadic Idiopathic Scoliosis is still unknown [5]. Ogilvie et al. in the population study based on a large data base from Utah conclude that 97% of their patients with idiopathic scoliosis have familial origins and suggest a presence of one or more major gene defects or single gene defects influenced by other factors [11]. According to Cheng et al. predisposition for IS doesn’t have a specific assigned risk of heritability, but inheritance is based on multiple factors potentially both genetic and environmental, which have yet to be defined [1].

Housekeeping genes encode proteins that are usually essential for the maintenance of basic cellular functions. They are expressed constitutively in every human cell but it appears that their transcriptional level may be influenced by numerous factors [12, 13]. ACTB and GAPDH are two housekeeping genes encoding respectively a cytoskeletal protein β-actin, and glyceraldehyde-3-phosphate dehydrogenase, an enzyme of glycolysis [12]. Based on the assumption of their constant expression in various tissues these genes serve as traditional internal control in a variety of assays in molecular biology [13]. Although their expression levels can fluctuate between different tissues and persons, human housekeeping genes seem to exhibit a preserved tissue-wide expression ranking order [14].

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). In order to recognize potentially sophisticated patterns in the data and because of the tensor structure of the ACTB and GAPDH expression 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 source of data for specially developed artificial neural network in order to predict the positive family history of index patients.

Methods

Patients

Study design was approved by Bioethical Committee Board of Silesian Medical University. Informed, written consent was obtained from each patient participating in the study and if required from their parents. Twenty nine patients (23 females and 6 males) with a definite diagnosis of late onset Idiopathic Scoliosis were included in the study. Thirteen of them (44%) had positive familial history of IS. All of the patients had undergone posterior corrective surgery with segmental spinal instrumentation according to C-D method. The mean age at surgery was 16 years 8 months (range 13,7 – 24 years). Based on Lenke classification 6 curves were of type 1,6 curves of type 2,7 curves of type 3,7 curves of type 4,4 curves of type 5 and 3 of type 6 [15]. Preoperatively the average frontal and sagittal Cobb angles measured on standard p-a and lateral radiograms were 68,8° (range 36°-114°) and 35,4° (range 12°-70°) respectively. The axial plane deformity was measured on CT scans performed at the curve apex by spinal rotation angle relative to sagittal plane RAsag and rib hump index RHi as described by Aaro and Dahlborn [16]. The mean RAsag was 19,3° (range 2,5°-46°) and RHi 0,4 (range 0,03-0,91). During surgery bilateral facet removal was performed in the routine manner and bone specimens from inferior articular spinal processes at the curve apex concavity and convexity were harvested. In the same time bilateral samples of paravertebral muscle tissue at the apical level and 10 ml of patients peripheral blood were collected. Every sample of bone and muscular tissue as well as blood specimens were placed in separate sterile tubes, adequately identified and immediately snap frozen in liquid nitrogen and stored at -80°C until molecular analysis.

Laboratory procedures

Tissues samples were homogenized with the use of Polytron® (Kinematyka AG). Total RNA was isolated from tissue samples with the use of TRIZOL® reagent (Invitrogen Life Technologies, California, USA), according to the manufacturer’s instructions. Extracts of total RNA were treated with DNAase I (Qiagen Gmbh, Hilden, Germany) and purified with the use of RNeasy Mini Spin Kolumn (Qiagen Gmbh, Hilden, Germany), in accordance with manufacturer’s protocol. The quality of RNA was estimated by electrophoresis on a 1% agarose gel stained with ethidium bromide. The RNA concentration was determined by absorbance at 260 nm using a Gene Quant II spectrophotometer (Pharmacia LKB Biochrom Ltd., Cambridge, UK). Total RNA served as a matrix for QRT PCR.

ACTB and GAPDH mRNA quantification in osseous, muscular and blood tissue samples by Quantitative Real Time Reverse Transcription Polymerase Chain Reaction.

The quantitative analysis was carried out with the use of Sequence Detector ABI PRISM™ 7000 (Applied Biosystems, California, USA). The standard curve was appointed for standards of ACTB (TaqMan® DNA Template Reagents Kit, Applied Biosystems, Foster, CA, USA). The ACTB and GAPDH mRNA abundance in all studied tissue specimens were expressed as mRNA copy number per 1 μg of total RNA.

The QRT-PCR reaction mixture of a total volume of 25 μl contained QuantiTect SYBR- Green RT-PCR bufor containing Tris–HCl (NH4)2SO4, 5 mM MgCl2, pH 8,7, dNTP mix fluorescent dye SYBR-Green I, and passive reference dye ROX mixed with 0,5 μl QuantiTect RT mix (Omniscript reverse transcriptase, Sensiscript reverse transcriptase) (QuantiTect SYBR-Green RT-PCR kit; Qiagen) forward and reverse primers each at a final concentration of 0,5 μM mRNA and total RNA 0,25 μg per reaction. Sequence for primers: mRNA for mRNA for ACTB 5’TCACCCACACTGTGCCC ATCTACGA3’(forward primer) 5’CAGCGGAACCGCTCATTGCCAATGG3’ (reverse primer), mRNA for GAPDH 5’GAAGGTGAAGGTCGGAGTC3’(forward primer) 5’GAAGATGG TGATGGGATT 3’(reverse primer). Reverse transcription was carried out at 50°C for 30 min. After activation of the HotStar Taq DNA polymerase and deactivation of reverse transcriptases at 95°C for 15 min, subsequent PCR amplification consisted of denaturation at 94°C for 15 sec, annealing at 60°C for 30 sec and extension at 72°C for 30 sec (40 cycles). Final extension was carried out at 72°C for 10 min. QRT-PCR specificity was assessed by electrophoresis in 6% polyacrylamid gel and melting curves for aplimeres.

Patient data

The results of laboratory procedures and the family anamnesis of 29 patients were used to create dataset consisting of 29 rows. The expression values were transformed to logarithmic scale. One row represented ACTB and GAPDH transcription profile in three kinds of tissue (bone, muscle, and blood) for exactly one patient.

Unfortunately, there were some missing data in our dataset. To face this problem we could either remove incomplete records from the analyzed dataset or use appropriate methodology and tool to preserve and utilize them in the analysis. In data mining and knowledge discovery from data disciplines the problem of missing data is widely discussed [1722]. With the removal of all incomplete records we could risk losing some important information contained in the whole dataset. In effect we decided to preserve all the records and replace missing values by random data from normal distributions similar to the original distributions of the variables. The random values were marked in bold [Tables 1 and 2]. Our decision was supported by the experience of one of the co-authors of this study conducting extensive research in the field of advanced data processing therein in processing incomplete data [2327]. Basing on the mentioned above ANN was chosen as an appropriate method for classification in this case. The dataset was randomly divided into training set (20 rows) [Table 1] and test set (9 rows) [Table 2].
Table 1

Training set

ID

GAPDH bone (concavity)

ACTB bone (concavity)

GAPDH muscle (concavity)

ACTB muscle (concavity)

GAPDH blood

ACTB blood

CLASSIFICATION

0 – Sporadic IS

1 – Familial IS

1

0

3,655234507

5,19447549

5,492043421

0,788036615

7,947055432

1

2

2,260071388

2,681241237

5,492043421

5,19447549

4,351834943

4,379378045

0

3

1,361727836

0

6,883054459

1,932946816

3,543198586

7,878946654

0

4

2,26245109

4,490393961

5,010236335

5,166876908

4,484314078

4,062506775

1

5

0,77815125

4,52146499

5,239966296

4,435937313

1,838849091

5,133344071

1

6

0

4,15192118

5,160648574

4,706444663

2,46686762

4,210666244

1

7

0

1,342422681

2,822821645

3,140193679

2,041392685

3,729488769

0

8

0

1,740362689

8,416773187

1,307737902

1,886490725

3,820595497

0

9

2,7084209

3,68797462

7,906948855

4,842939908

2,283301229

3,062205809

1

10

1,77815125

3,496376054

2,559906625

3,126131407

4,614992076

4,872779577

0

11

0

6,053585081

0

4,704202011

5,753376838

0,8874258

0

12

0

3,331022171

4,2175629

4,527707216

5,322554193

5,961483267

0

13

1,204119983

4,060168812

3,882068944

4,669075022

5,979840083

4,431492425

0

14

10,59979533

0

3,257198426

4,146065989

3,834102656

3,931152639

1

15

0

1,146128036

0,903089987

3,761401557

2,380211242

5,055026472

1

16

0

2,765668555

0

3,709015417

3,087426457

4,830563008

0

17

0

1,792391689

0

4,713734083

3,098643726

5,036968055

0

18

4,443841661

2,615950052

1,505149978

4,414388327

3,128076013

4,702835345

1

19

7,280817804

2,555094449

0

2,029383778

0

2,271841607

0

20

0

0,301029996

1,944482672

4,584489532

3,036628895

4,451325808

1

The random values are marked in bold.

Table 2

Test set and ANN’s prediction

ID

GAPDH bone (concavity)

ACTB bone (concavity)

GAPDH muscle e (concavity)

ACTB muscle (concavity)

GAPDH blood

ACTB blood

CLASSIFICATION

6 - 18–16 - 1

6 - 19–19 - 1

6 - 18–10 - 1

0 – Sporadic IS

ANN’S PREDICTION

ANN’S PREDICTION

ANN’S PREDICTION

1 – Familial IS

21

0

2,999130541

2,086359831

4,54961624

2,595496222

5,315582034

1

 0,8150237

 0,0658464

 0,9729949

22

0

3,325104983

2,981365509

3,940018155

3,253822439

5,188225173

0

 0,1371327

 0,0111759

 0,9755903

23

4,259641653

4,832872801

6,266957346

4,19709909

4,228759555

4,237141427

1

 0,9970080

 0,8874323

 0,9444540

24

1,041392685

3,29136885

6,118608586

5,357498429

1,036299441

3,74587204

1

 0,9984768

 0,9984370

 0,9981375

25

0,84509804

4,850768727

1,176091259

3,542451947

2,305351369

3,743744879

0

 0,0203165

 0,0046074

 0,0195937

26

2,598790507

4,308116016

5,95395578

5,604965452

3,836324116

3,596047008

1

 0,9979908

 0,9941236

 0,9964907

27

0

3,804275767

5,556972498

3,33701319

3,055760465

3,388811413

0

 0,3232977

 0,2974088

 0,0672966

28

0

3,513483957

0,805059074

2,195759967

1,176091259

3,688508808

0

 0,0371065

 0,0023705

 0,0179424

29

0

4,132643851

0,84509804

4,909245708

1,579783597

4,111497749

0

 0,0338294

 0,0341857

 0,0104404

The random values are marked in bold.

Artificial neural network

Artificial neural network is a mathematical model that is inspired by the structure and functional aspects of biological neural networks [28, 29]. ANN can be used to detect sophisticated patterns in data. Several studies have applied neural networks in research and analysis of various diseases (i.e. classification of cardiovascular disease, forecast for bacteria – antibiotic interactions, prediction of colorectal cancer patient survival) [30].

The architecture of the ANN used in this study is the multilayered feed-forward network architecture with four layers (two hidden layers). Multilayer feed-forward neural networks can be used to approximate a nonlinear functions which are applied to describe the complicated relationships in biological data [31]. The schematic representation of the best architecture of artificial neural network for our problem is shown in Figure 1. The number of neurons in the input layer was 6 and it was equal to the number of ACTB and GAPDH expression measurements. The ideal outputs were set at 1 for the positive history of IS in the family and at 0 for absence of IS in the anamnesis. The number of hidden nodes was obtained by trial and error method. We trained 421 neural networks models with different number of hidden nodes using the backpropagation algorithm (activation function: binary sigmoidal function, learning rate: 0,1; momentum rate: 0,01; epochs: 50, 500 and 5000) and the training set. The backpropagation teaching method was chosen because it is the most common method of training multilayered feed-forward neural networks [30]. Initially, 50 training epochs were considered but it did not yield a satisfactory result (Table 3). The mean square error (MSE) was high. This MSE was minimized by increasing the epochs from 50 to 500 and finally from 500 to 5000 [Table 3]. Thereafter, we selected 3 neural networks with the least mean square error (MSE) for training set. To test the classification ability of the ANN approach, we used the selected neural models and test set of data. The ANN model with the best classification accuracy for Idiopathic Scoliosis in the anamnesis with expression measurement of ACTB and GAPDH was chosen as the best.
Figure 1

A schematic representation of one of tested artificial neural networks. Our ANN has input layer, two hidden layers and an output layer. The input layer has 6 neurons, the first hidden layer has 18 neurons, the second hidden layer has 16 neurons and output layer has 1 neuron.

Table 3

Evaluation and selection of multiple network architectures

No.

ANN architecture

MSE 50 epochs

MSE 500 epochs

MSE 5000 epochs

No.

ANN architecture

MSE 50 epochs

MSE 500 epochs

MSE 5000 epochs

No.

ANN architecture

MSE 50 epochs

MSE 500 epochs

MSE 5000 epochs

No.

ANN architecture

MSE 50 epochs

MSE 500 epochs

MSE 5000 epochs

1

6 - 18–16 - 1

0,426

0,058

0,006

57

6 - 16–8 - 1

0,355

0,048

0,008

113

6 - 12–7 - 1

0,432

0,059

0,009

169

6 - 5–11 - 1

0,490

0,308

0,011

2

6 - 1919 - 1

0,413

0,049

0,006

58

6 - 16–11 - 1

0,328

0,046

0,008

114

6 - 11–1 - 1

0,384

0,069

0,009

170

6 - 8–9 - 1

0,483

0,057

0,011

3

6 - 1810 - 1

0,440

0,045

0,006

59

6 - 19–15 - 1

0,443

0,052

0,008

115

6 - 16–3 - 1

0,426

0,087

0,009

171

6 - 4–17 - 1

0,497

0,299

0,011

4

6 - 20–11 - 1

0,382

0,038

0,007

60

6 - 15–1 - 1

0,439

0,121

0,008

116

6 - 10–10 - 1

0,454

0,104

0,009

172

6 - 6–16 - 1

0,445

0,218

0,011

5

6 - 18–5 - 1

0,391

0,047

0,007

61

6 - 18–3 - 1

0,444

0,043

0,008

117

6 - 11–16 - 1

0,408

0,065

0,009

173

6 - 6–15 - 1

0,495

0,071

0,011

6

6 - 19–11 - 1

0,436

0,041

0,007

62

6 - 17–2 - 1

0,417

0,045

0,008

118

6 - 8–20 - 1

0,420

0,194

0,009

174

6 - 6–7 - 1

0,463

0,172

0,011

7

6 - 19–18 - 1

0,438

0,042

0,007

63

6 - 15–14 - 1

0,420

0,066

0,008

119

6 - 14–3 - 1

0,477

0,041

0,009

175

6 - 5–20 - 1

0,485

0,106

0,011

8

6 - 20–13 - 1

0,433

0,051

0,007

64

6 - 11–4 - 1

0,352

0,053

0,008

120

6 - 9–16 - 1

0,482

0,112

0,009

176

6 - 9–7 - 1

0,402

0,065

0,011

9

6 - 17–9 - 1

0,416

0,060

0,007

65

6 - 14–18 - 1

0,398

0,141

0,008

121

6 - 15–3 - 1

0,400

0,049

0,009

177

6 - 8–15 - 1

0,429

0,121

0,011

10

6 - 19–14 - 1

0,439

0,085

0,007

66

6 - 9–8 - 1

0,482

0,059

0,008

122

6 - 9–2 - 1

0,396

0,065

0,009

178

6 - 8–5 - 1

0,396

0,054

0,012

11

6 - 20–19 - 1

0,412

0,042

0,007

67

6 - 15–2 - 1

0,329

0,045

0,008

123

6 - 11–17 - 1

0,398

0,051

0,009

179

6 - 5–17 - 1

0,429

0,225

0,012

12

6 - 18–19 - 1

0,422

0,063

0,007

68

6 - 20–17 - 1

0,352

0,044

0,008

124

6 - 7–20 - 1

0,435

0,069

0,009

180

6 - 5–8 - 1

0,437

0,312

0,012

13

6 - 17–4 - 1

0,417

0,091

0,007

69

6 - 9–14 - 1

0,490

0,060

0,008

125

6 - 8–18 - 1

0,460

0,187

0,009

181

6 - 5–15 - 1

0,370

0,215

0,012

14

6 - 20–8 - 1

0,392

0,046

0,007

70

6 - 14–9 - 1

0,367

0,049

0,008

126

6 - 10–1 - 1

0,340

0,081

0,009

182

6 - 4–20 - 1

0,491

0,178

0,012

15

6 - 18–7 - 1

0,390

0,079

0,007

71

6 - 12–12 - 1

0,424

0,067

0,008

127

6 - 12–16 - 1

0,438

0,045

0,009

183

6 - 7–3 - 1

0,410

0,147

0,012

16

6 - 14–14 - 1

0,423

0,085

0,007

72

6 - 16–5 - 1

0,388

0,070

0,008

128

6 - 10–17 - 1

0,425

0,138

0,009

184

6 - 4–13 - 1

0,453

0,269

0,012

17

6 - 18–8 - 1

0,363

0,055

0,007

73

6 - 13–5 - 1

0,450

0,071

0,008

129

6 - 7–1 - 1

0,430

0,084

0,009

185

6 - 6–18 - 1

0,490

0,221

0,012

18

6 - 19–4 - 1

0,365

0,045

0,007

74

6 - 8–14 - 1

0,445

0,076

0,008

130

6 - 10–14 - 1

0,414

0,112

0,009

186

6 - 4–4 - 1

0,446

0,483

0,012

19

6 - 20–7 - 1

0,440

0,087

0,007

75

6 - 17–18 - 1

0,432

0,095

0,008

131

6 - 9–15 - 1

0,442

0,149

0,009

187

6 - 3–12 - 1

0,491

0,216

0,012

20

6 - 20–6 - 1

0,451

0,044

0,007

76

6 - 8–8 - 1

0,486

0,254

0,008

132

6 - 16–16 - 1

0,430

0,093

0,009

188

6 - 3–8 - 1

0,465

0,102

0,013

21

6 - 15–13 - 1

0,431

0,087

0,007

77

6 - 13–4 - 1

0,423

0,071

0,008

133

6 - 11–19 - 1

0,467

0,107

0,009

189

6 - 6–3 - 1

0,469

0,066

0,013

22

6 - 18–6 - 1

0,357

0,046

0,007

78

6 - 14–8 - 1

0,439

0,064

0,008

134

6 - 13–16 - 1

0,459

0,084

0,010

190

6 - 12–5 - 1

0,373

0,052

0,013

23

6 - 16–13 - 1

0,467

0,045

0,007

79

6 - 20–10 - 1

0,410

0,043

0,008

135

6 - 8–10 - 1

0,404

0,053

0,010

191

6 - 4–1 - 1

0,486

0,114

0,013

24

6 - 13–17 - 1

0,463

0,069

0,007

80

6 - 12–4 - 1

0,439

0,081

0,008

136

6 - 9–3 - 1

0,474

0,145

0,010

192

6 - 3–20 - 1

0,476

0,353

0,013

25

6 - 17–17 - 1

0,425

0,045

0,008

81

6 - 15–20 - 1

0,444

0,096

0,008

137

6 - 18–1 - 1

0,337

0,083

0,010

193

6 - 6–20 - 1

0,468

0,059

0,013

26

6 - 16–6 - 1

0,413

0,053

0,008

82

6 - 17–20 - 1

0,421

0,058

0,008

138

6 - 6–1 - 1

0,477

0,335

0,010

194

6 - 3–2 - 1

0,474

0,158

0,013

27

6 - 19–9 - 1

0,422

0,174

0,008

83

6 - 13–11 - 1

0,395

0,094

0,008

139

6 - 8–3 - 1

0,390

0,066

0,010

195

6 - 3–6 - 1

0,498

0,180

0]014

28

6 - 13–8 - 1

0,419

0,048

0,008

84

6 - 17–6 - 1

0,428

0,054

0,008

140

6 - 8–16 - 1

0,396

0,127

0,010

196

6 - 20 - 1

0,327

0,066

0,014

29

6 - 13–18 - 1

0,391

0,054

0,008

85

6 - 15–12 - 1

0,432

0,043

0,008

141

6 - 10–12 - 1

0,424

0,124

0,010

197

6 - 4–10 - 1

0,485

0,177

0,014

30

6 - 16–10 - 1

0,383

0,052

0,008

86

6 - 14–2 - 1

0,391

0,132

0,008

142

6 - 10–6 - 1

0,461

0,073

0,010

198

6 - 8–19 - 1

0,452

0,099

0,014

31

6 - 13–20 - 1

0,449

0,112

0,008

87

6 - 12–1 - 1

0,448

0,044

0,008

143

6 - 6–4 - 1

0,418

0,068

0,010

199

6 - 15 - 1

0,323

0,073

0,014

32

6 - 12–11 - 1

0,454

0,116

0,008

88

6 - 17–8 - 1

0,361

0,044

0,008

144

6 - 5–14 - 1

0,478

0,207

0,010

200

6 - 15–15 - 1

0,472

0,045

0,015

33

6 - 17–19 - 1

0,381

0,120

0,008

89

6 - 11–2 - 1

0,467

0,092

0,008

145

6 - 7–17 - 1

0,449

0,206

0,010

201

6 - 19 - 1

0,317

0,105

0,015

34

6 - 19–2 - 1

0,358

0,068

0,008

90

6 - 13–9 - 1

0,445

0,086

0,008

146

6 - 9–11 - 1

0,458

0,090

0,010

202

6 - 4–8 - 1

0,438

0,100

0,015

35

6 - 16–15 - 1

0,393

0,088

0,008

91

6 - 17–13 - 1

0,408

0,045

0,008

147

6 - 8–7 - 1

0,441

0,062

0,010

203

6 - 19–3 - 1

0,412

0,052

0,015

36

6 - 16–17 - 1

0,435

0,092

0,008

92

6 - 10–5 - 1

0,432

0,056

0,008

148

6 - 7–19 - 1

0,432

0,069

0,010

204

6 - 15–10 - 1

0,443

0,090

0,016

37

6 - 19–5 - 1

0,386

0,083

0,008

93

6 - 18–11 - 1

0,456

0,085

0,008

149

6 - 5–4 - 1

0,459

0,151

0,010

205

6 - 11–10 - 1

0,393

0,065

0,016

38

6 - 20–16 - 1

0,357

0,048

0,008

94

6 - 14–12 - 1

0,428

0,067

0,008

150

6 - 10–2 - 1

0,415

0,054

0,010

206

6 - 9–5 - 1

0,477

0,112

0,016

39

6 - 16–7 - 1

0,419

0,056

0,008

95

6 - 17–15 - 1

0,461

0,051

0,008

151

6 - 6–9 - 1

0,454

0,091

0,010

207

6 - 6–17 - 1

0,498

0,254

0,017

40

6 - 19–20 - 1

0,415

0,077

0,008

96

6 - 10–11 - 1

0,433

0,052

0,008

152

6 - 7–13 - 1

0,472

0,063

0,010

208

6 - 11 - 1

0,377

0,154

0,017

41

6 - 18–15 - 1

0,396

0,114

0,008

97

6 - 10–16 - 1

0,469

0,097

0,008

153

6 - 11–9 - 1

0,373

0,053

0,010

209

6 - 14 - 1

0,349

0,083

0,017

42

6 - 17–14 - 1

0,390

0,052

0,008

98

6 - 11–11 - 1

0,414

0,053

0,008

154

6 - 9–1 - 1

0,434

0,055

0,010

210

6 - 18–13 - 1

0,400

0,044

0,018

43

6 - 16–9 - 1

0,386

0,047

0,008

99

6 - 9–10 - 1

0,434

0,112

0,008

155

6 - 11–6 - 1

0,410

0,053

0,010

211

6 - 5 - 1

0,336

0,110

0,019

44

6 - 18–12 - 1

0,419

0,049

0,008

100

6 - 10–20 - 1

0,498

0,056

0,008

156

6 - 7–15 - 1

0,361

0,062

0,011

212

6 - 20–9 - 1

0,421

0,050

0,022

45

6 - 16–19 - 1

0,406

0,077

0,008

101

6 - 12–6 - 1

0,433

0,088

0,008

157

6 - 7–14 - 1

0,482

0,105

0,011

213

6 - 6 - 1

0,409

0,181

0,022

46

6 - 11–18 - 1

0,452

0,086

0,008

102

6 - 10–18 - 1

0,456

0,101

0,009

158

6 - 9–6 - 1

0,473

0,150

0,011

214

6 - 10 - 1

0,387

0,122

0,022

47

6 - 18–18 - 1

0,431

0,128

0,008

103

6 - 12–13 - 1

0,465

0,112

0,009

159

6 - 13–3 - 1

0,394

0,054

0,011

215

6 - 3 - 1

0,454

0,196

0,023

48

6 - 17–12 - 1

0,405

0,045

0,008

104

6 - 13–2 - 1

0,459

0,072

0,009

160

6 - 5–6 - 1

0,468

0,090

0,011

216

6 - 2 - 1

0,360

0,257

0,023

49

6 - 15–16 - 1

0,430

0,056

0,008

105

6 - 16–12 - 1

0,394

0,054

0,009

161

6 - 8–4 - 1

0,448

0,057

0,011

217

6 - 9–4 - 1

0,446

0,098

0,026

50

6 - 16–14 – 1

0,418

0,049

0,008

106

6 - 11–3 - 1

0,475

0,126

0,009

162

6 - 4–5 - 1

0,454

0,065

0,011

218

6 - 15–11 - 1

0,388

0,153

0,027

51

6 - 16–4 – 1

0,446

0,074

0,008

107

6 - 15–7 - 1

0,429

0,094

0,009

163

6 - 5–10 - 1

0,447

0,138

0,011

219

6 - 4 - 1

0,477

0,187

0,027

52

6 - 13–10 - 1

0,424

0,049

0,008

108

6 - 10–19 - 1

0,407

0,109

0,009

164

6 - 7–10 - 1

0,462

0,118

0,011

220

6 - 19–10 - 1

0,408

0,106

0,028

53

6 - 18–20 - 1

0,444

0,049

0,008

109

6 - 11–7 - 1

0,440

0,044

0,009

165

6 - 8–12 - 1

0,443

0,094

0,011

221

6 - 15–19 - 1

0,445

0,094

0,029

54

6 - 15–4 - 1

0,449

0,045

0,008

110

6 - 14–4 - 1

0,436

0,051

0,009

166

6 - 10–7 - 1

0,412

0,110

0,011

222

6 - 15–17 - 1

0,351

0,066

0,029

55

6 - 14–10 - 1

0,364

0,048

0,008

111

6 - 19–13 - 1

0,377

0,091

0,009

167

6 - 6–12 - 1

0,480

0,077

0,011

223

6 - 3–4 - 1

0,479

0,161

0,030

56

6 - 20–3 - 1

0,356

0,047

0,008

112

6 - 11–14 - 1

0,428

0,082

0,009

168

6 - 6–5 - 1

0,408

0,139

0,011

224

6 - 6–13 - 1

0,403

0,272

0,030

No.

ANN architecture

MSE 50 epochs

MSE 500 epochs

MSE 5000 epochs

No.

ANN architecture

MSE 50 epochs

MSE 500 epochs

MSE 5000 epochs

No.

ANN architecture

MSE 50 epochs

MSE 500 epochs

MSE 5000 epochs

No.

ANN architecture

MSE 50 epochs

MSE 500 epochs

MSE 5000 epochs

225

6 - 15–8 - 1

0,357

0,051

0,031

281

6 - 7–8 - 1

0,465

0,197

0,057

337

6 - 7 - 1

0,421

0,111

0,065

393

6 - 5–3 - 1

0,504

0,165

0,216

226

6 - 10–15 - 1

0,451

0,084

0,033

282

6 - 14–13 - 1

0,398

0,056

0,057

338

6 - 3–9 - 1

0,501

0,262

0,065

394

6 - 3–14 - 1

0,500

0,263

0,218

227

6 - 20–1 - 1

0,369

0,043

0,034

283

6 - 8–2 - 1

0,410

0,117

0,057

339

6 - 3–15 - 1

0,489

0,185

0,065

395

6 - 2–6 - 1

0,497

0,405

0,232

228

6 - 17–1 - 1

0,388

0,064

0,036

284

6 - 17–16 - 1

0,405

0,094

0,057

340

6 - 18–17 - 1

0,441

0,076

0,067

396

6 - 5–9 - 1

0,495

0,107

0,232

229

6 - 15–5 - 1

0,401

0,057

0,036

285

6 - 19–6 - 1

0,450

0,047

0,057

341

6 - 16–2 - 1

0,441

0]052

0,068

397

6 - 2–18 - 1

0,494

0,227

0,250

230

6 - 7–16 - 1

0,404

0,241

0,037

286

6 - 18–4 - 1

0,387

0,053

0,057

342

6 - 9–18 - 1

0,409

0,075

0,070

398

6 - 1–18 - 1

0,501

0,495

0,251

231

6 - 10–4 - 1

0,456

0,114

0,037

287

6 - 12–14 - 1

0,486

0,131

0,057

343

6 - 13–6 - 1

0,478

0,063

0,071

399

6 - 1–16 - 1

0,495

0,494

0,252

232

6 - 16–20 - 1

0,432

0,056

0,037

288

6 - 15–6 - 1

0,471

0,047

0,057

344

6 - 17–7 - 1

0,391

0,089

0,084

400

6 - 1–10 - 1

0,499

0,489

0,254

233

6 - 14–20 - 1

0,389

0,050

0,038

289

6 - 8–13 - 1

0,476

0,071

0,057

345

6 - 20–12 - 1

0,410

0,037

0,085

401

6 - 1–8 - 1

0,487

0,489

0,265

234

6 - 20–20 - 1

0,428

0,076

0,039

290

6 - 12–10 - 1

0,362

0,053

0,057

346

6 - 9 - 1

0,349

0,201

0,086

402

6 - 2–8 - 1

0,462

0,235

0,283

235

6 - 19–1 - 1

0,417

0,132

0,042

291

6 - 14–16 - 1

0,433

0,056

0,057

347

6 - 8–1 - 1

0,434

0,097

0,090

403

6 - 2–13 - 1

0,435

0,270

0,297

236

6 - 6–14 - 1

0,428

0,063

0,042

292

6 - 13–12 - 1

0,448

0,057

0,057

348

6 - 4–7 - 1

0,443

0,211

0,091

404

6 - 2–20 - 1

0,419

0,326

0,298

237

6 - 12–2 - 1

0,399

0,059

0,043

293

6 - 12 - 1

0,342

0,087

0,057

349

6 - 3–5 - 1

0,479

0,247

0,094

405

6 - 1–5 - 1

0,490

0,264

0,326

238

6 - 20–4 - 1

0,446

0,064

0,044

294

6 - 5–16 - 1

0,477

0,062

0,057

350

6 - 16–1 - 1

0,425

0,121

0,096

406

6 - 1–20 - 1

0,497

0,440

0,328

239

6 - 18–14 - 1

0,352

0,038

0,044

295

6 - 13–1 - 1

0,352

0,052

0,058

351

6 - 2–10 - 1

0,399

0,318

0,097

407

6 - 3–7 - 1

0,452

0,227

0,330

240

6 - 18 - 1

0,300

0,141

0,045

296

6 - 5–19 - 1

0,456

0,075

0,058

352

6 - 2–3 - 1

0,497

0,362

0,097

408

6 - 2–17 - 1

0,495

0,347

0,344

241

6 - 14–19 - 1

0,410

0,049

0,045

297

6 - 16–18 - 1

0,411

0,045

0,058

353

6 - 3–16 - 1

0,473

0,207

0,098

409

6 - 1–11 - 1

0,501

0,307

0,371

242

6 - 14–7 - 1

0,432

0,058

0,046

298

6 - 6–8 - 1

0,443

0,109

0,058

354

6 - 4–12 - 1

0,486

0,310

0,098

410

6 - 1–12 - 1

0,495

0,249

0,371

243

6 - 18–9 - 1

0,384

0,045

0,047

299

6 - 12–19 - 1

0,473

0,057

0,058

355

6 - 17–11 - 1

0,363

0,061

0,104

411

6 - 1–2 - 1

0,495

0,435

0,382

244

6 - 9–20 - 1

0,377

0,050

0,048

300

6 - 7–7 - 1

0,463

0,126

0,058

356

6 - 17–3 - 1

0,388

0,048

0,106

412

6 - 1–13 - 1

0,494

0,348

0,393

245

6 - 10–3 - 1

0,462

0,110

0,048

301

6 - 8–11 - 1

0,410

0,102

0,058

357

6 - 14–1 - 1

0,415

0,049

0,107

413

6 - 1 - 1

0,435

0,486

0,408

246

6 - 10–9 - 1

0,502

0,052

0,048

302

6 - 12–17 - 1

0,421

0,048

0,058

358

6 - 18–2 - 1

0,353

0,079

0,107

414

6 - 1–1 - 1

0,511

0,284

0,465

247

6 - 20–18 - 1

0,447

0,048

0,049

303

6 - 9–12 - 1

0,391

0,116

0,059

359

6 - 7–18 - 1

0,483

0,101

0,107

415

6 - 1–6 - 1

0,488

0,301

0,476

248

6 - 12–8 - 1

0,413

0,048

0,049

304

6 - 11–12 - 1

0,473

0,083

0,059

360

6 - 15–18 - 1

0,443

0,065

0,107

416

6 - 1–15 - 1

0,489

0,464

0,479

249

6 - 8–17 - 1

0,478

0,115

0,050

305

6 - 13–19 - 1

0,390

0,064

0,059

361

6 - 3–3 - 1

0,486

0,495

0,109

417

6 - 1–3 - 1

0,478

0,381

0,488

250

6 - 7–2 - 1

0,470

0,062

0,050

306

6 - 10–13 - 1

0,439

0,080

0,059

362

6 - 9–13 - 1

0,439

0,090

0,110

418

6 - 1–4 - 1

0,494

0,486

0,495

251

6 - 14–6 - 1

0,441

0,048

0,050

307

6 - 5–12 - 1

0,477

0,279

0,059

363

6 - 2–12 - 1

0,499

0,356

0,110

419

6 - 1–7 - 1

0,484

0,427

0,496

252

6 - 20–14 - 1

0,403

0,043

0,051

308

6 - 10–8 - 1

0,460

0,056

0,059

364

6 - 5–7 - 1

0,428

0,163

0,110

420

6 - 1–19 - 1

0,500

0,495

0,500

253

6 - 13–14 - 1

0,427

0,081

0,051

309

6 - 9–9 - 1

0,417

0,075

0,059

365

6 - 4–15 - 1

0,459

0,291

0,112

421

6 - 1–14 - 1

0,500

0,495

0,501

254

6 - 12–20 - 1

0,440

0,050

0,051

310

6 - 3–11 - 1

0,468

0,138

0,059

366

6 - 2–15 - 1

0,468

0,334

0,113

     

255

6 - 14–5 - 1

0,403

0,045

0,052

311

6 - 7–5 - 1

0,455

0,071

0,060

367

6 - 6–10 - 1

0,445

0,070

0,114

     

256

6 - 8–6 - 1

0,449

0,053

0,052

312

6 - 6–2 - 1

0,496

0,087

0,060

368

6 - 5–13 - 1

0,418

0,353

0,114

     

257

6 - 14–11 - 1

0,422

0,072

0,053

313

6 - 12–9 - 1

0,406

0,125

0,060

369

6 - 5–18 - 1

0,449

0,331

0,115

     

258

6 - 13–15 - 1

0,430

0,071

0,053

314

6 - 4–6 - 1

0,406

0,269

0,060

370

6 - 4–19 - 1

0,482

0,183

0,116

     

259

6 - 7–9 - 1

0,453

0,106

0,053

315

6 - 5–5 - 1

0,482

0,068

0,060

371

6 - 5–2 - 1

0,476

0,216

0,118

     

260

6 - 17–10 - 1

0,464

0,047

0,053

316

6 - 6–11 - 1

0,487

0,075

0,060

372

6 - 2–14 - 1

0,480

0,279

0,119

     

261

6 - 12–18 - 1

0,439

0,051

0,053

317

6 - 6–19 - 1

0,361

0]121

0,061

373

6 - 20–15 - 1

0,414

0,048

0,128

     

262

6 - 11–15 - 1

0,367

0,096

0,053

318

6 - 4–2 - 1

0,465

0,190

0,061

374

6 - 4–14 - 1

0,439

0,074

0,130

     

263

6 - 17–5 - 1

0,464

0,132

0,053

319

6 - 5–1 - 1

0,410

0,131

0,061

375

6 - 3–13 - 1

0,492

0,277

0,134

     

264

6 - 11–13 - 1

0,491

0,154

0,054

320

6 - 7–6 - 1

0,496

0,113

0,061

376

6 - 4–18 - 1

0,462

0,136

0,135

     

265

6 - 7–11 - 1

0,458

0,062

0,054

321

6 - 9–17 - 1

0,432

0,146

0,061

377

6 - 2–9 - 1

0,469

0,433

0,136

     

266

6 - 19–16 - 1

0,424

0,165

0,055

322

6 - 3–19 - 1

0,488

0,285

0,061

378

6 - 1–17 - 1

0,456

0,382

0,141

     

267

6 - 16 - 1

0,327

0,064

0,055

323

6 - 2–19 - 1

0,486

0,279

0,061

379

6 - 4–11 - 1

0,389

0,150

0,141

     

268

6 - 20–5 - 1

0,427

0,076

0,055

324

6 - 3–10 - 1

0,495

0,250

0,061

380

6 - 1

0,300

0,218

0,143

     

269

6 - 11–8 - 1

0,456

0,054

0,055

325

6 - 4–9 - 1

0,436

0,222

0,062

381

6 - 3–18 - 1

0,392

0,228

0,143

     

270

6 - 14–15 - 1

0,422

0,060

0,055

326

6 - 9–19 - 1

0,464

0,098

0,062

382

6 - 4–16 - 1

0,461

0,081

0,143

     

271

6 - 13–7 - 1

0,439

0,049

0,055

327

6 - 7–12 - 1

0,418

0,219

0,062

383

6 - 2–2 - 1

0,450

0,451

0,151

     

272

6 - 17 - 1

0,350

0,079

0,055

328

6 - 2–1 - 1

0,503

0,495

0,062

384

6 - 2–5 - 1

0,488

0,354

0,152

     

273

6 - 12–15 - 1

0,470

0,048

0,055

329

6 - 8 - 1

0,411

0,119

0,062

385

6 - 11–5 - 1

0,483

0,099

0,154

     

274

6 - 19–7 - 1

0,420

0,049

0,056

330

6 - 4–3 - 1

0,504

0,288

0,062

386

6 - 2–7 - 1

0,495

0,365

0,168

     

275

6 - 15–9 - 1

0,474

0,058

0,056

331

6 - 14–17 - 1

0,377

0,092

0,063

387

6 - 3–17 - 1

0,395

0,306

0,170

     

276

6 - 11–20 - 1

0,369

0,063

0,056

332

6 - 12–3 - 1

0,451

0,063

0,063

388

6 - 3–1 - 1

0,476

0,203

0,177

     

277

6 - 19–12 - 1

0,414

0,043

0,056

333

6 - 13 - 1

0,298

0,128

0,063

389

6 - 2–11 - 1

0,493

0,319

0,181

     

278

6 - 19–17 - 1

0,404

0,093

0,056

334

6 - 13–13 - 1

0,479

0,048

0,064

390

6 - 1–9 - 1

0,495

0,485

0,209

     

279

6 - 20–2 - 1

0,425

0,080

0,056

335

6 - 7–4 - 1

0,453

0,134

0,064

391

6 - 2–4 - 1

0,496

0,426

0,212

     

280

6 - 19–8 - 1

0,420

0,065

0,056

336

6 - 2–16 - 1

0,485

0,284

0,064

392

6 - 6–6 - 1

0,442

0,194

0,214

   

,

 

The models with the least MSE are marked in bold.

Results

The data have been analyzed using NeuronDotNet computer library [32]. Training an ANN is the process of setting the best weights on the inputs of each of the nodes. The goal is to use the training set to produce weights where the output of the network is as close to the desired output as possible for as many of the examples in the training set as possible [30]. Table 3 shows the MSE for all 421 trained artificial neural models. A satisfactory MSE was yielded for ANNs with:

18 nodes in the first hidden layer and 16 nodes in the second hidden layer

19 nodes in the first hidden layer and 19 nodes in the second hidden layer

18 nodes in the first hidden layer and 10 nodes in the second hidden layer

Figure 2 presents the MSE for ANN model based on 6-18-16-1 architecture and the training set.
Figure 2

Plot of total error in training ANN based on 6-18-16-1 architecture. Training of the feedforward backpropagation neural network as measured by the square error of the difference between the actual and predicted variable.

Table 2 lists classification results on the test set of ANN modelling for presence and absence of Idiopathic Scoliosis in the anamnesis. It proves how well the artificial neural network will perform on new data. 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 Idiopathic Scoliosis in the anamnesis with expression measurement of ACTB and GAPDH with use of ANN based on 6-18-16-1 architecture was 8 of 9 (88%). Only in one case (ID 27 in test set), the prediction was ambiguous.

Conclusions

The results of this study confirm the potential benefits of the artificial neural network application for clinical research and point at human housekeeping genes as a potential target for future molecular investigations on idiopathic scoliosis etiopathogenesis. The analysis indicates the relationship between level of expression of ACTB, GAPDH and familial Idiopathic Scoliosis.

Notes

Abbreviations

IS: 

Idiopathic Scoliosis

ANN: 

Artificial neural network

mRNA: 

Messenger ribonucleic acid

QRT PCR: 

Quantitative Real Time Reverse Transciptase Chain Reaction

Declarations

Acknowledgements

The study was supported by grant 2P05C07430 from State Committee for Scientific Research of Polish Ministry of Science and Higher Education. We thank all the medical staff of Orthopedics Clinic of WSS nr 5 in Sosnowiec who participated in the scoliosis surgery of the patients included in the study and help us to collect the biological samples.

Tomasz Waller and Damian Zapart received a scholarship under the project "DoktoRIS - Scholarship Program for Innovative Silesia" co-financed by the European Union under the European Social Fund.

Authors’ Affiliations

(1)
Institute of Computer Science, Division of Biomedical Computer Systems, University of Silesia
(2)
Department and Clinic of Orthopaedics, Medical University of Silesia
(3)
Department of Molecular Biology, Medical University of Silesia
(4)
Institute of Computer Science, Division of Information Systems, University of Silesia

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© Waller et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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