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Table 1 Prediction results for feature ranking methods (chi squared, SWRF, SLR and LP) on two LOAD datasets (TGen and ADRC) with two sets of SNPs (chromosome 19 only and genome-wide)

From: The application of network label propagation to rank biomarkers in genome-wide Alzheimer’s data

Dataset

# SNPs

Method

Number of SNPs used in classifier

1

2

5

10

50

100

500

1000

TGen

3,652 (chr19)

Chi Sq

0.6628 ± 0.0284

0.6791 ± 0.0278

0.6811 ± 0.0280

0.6567 ± 0.0290

0.5768 ± 0.0306

0.5925 ± 0.0300

0.5627 ± 0.0306

0.5512 ± 0.0304

SWRF

0.6783 ± 0.0284

0.6778 ± 0.0284

0.6791 ± 0.0282

0.6697 ± 0.0286

0.6620 ± 0.0286

0.6280 ± 0.0290

0.5941 ± 0.0304

0.5691 ± 0.0306

SLR

0.5014 ± 0.0302

0.6821 ± 0.0282

0.7129 ± 0.0270

0.7170 ± 0.0269

0.6847 ± 0.0282

0.6747 ± 0.0286

*

*

LP

0.6733 ± 0.0284

0.6904 ± 0.0278

0.7080 ± 0.0270

0.7184 ± 0.0267

0.7093 ± 0.0270

0.6945 ± 0.0274

0.6246 ± 0.0298

0.5989 ± 0.0302

234,665 (chr1-22)

Chi Sq

0.6628 ± 0.0284

0.6991 ± 0.0274

0.7230 ± 0.0267

0.7310 ± 0.0263

0.7068 ± 0.0272

0.6549 ± 0.0292

0.6059 ± 0.0302

0.5990 ± 0.0302

SWRF

0.6640 ± 0.0284

0.6705 ± 0.0284

0.7020 ± 0.0280

0.6796 ± 0.0286

0.6749 ± 0.0284

0.6087 ± 0.0306

0.5447 ± 0.0253

0.5261 ± 0.0169

SLR

0.6783 ± 0.0284

0.7076 ± 0.0270

0.7291 ± 0.0261

0.7424 ± 0.0257

0.7464 ± 0.0257

*

*

*

LP

0.6733 ± 0.0284

0.6904 ± 0.0284

0.7088 ± 0.0269

0.7396 ± 0.0257

0.7519 ± 0.0251

0.7286 ± 0.0270

0.6138 ± 0.0237

0.5735 ± 0.0178

ADRC

13,087 (chr19)

Chi Sq

0.6834 ± 0.0220

0.7369 ± 0.0206

0.7433 ± 0.0204

0.7169 ± 0.0212

0.6446 ± 0.0229

0.6109 ± 0.0233

0.5282 ± 0.0239

0.5361 ± 0.0241

SWRF

0.6834 ± 0.0229

0.7006 ± 0.0221

0.7169 ± 0.0206

0.7122 ± 0.0206

0.6894 ± 0.0220

0.6580 ± 0.0225

0.5343 ± 0.0235

0.4965 ± 0.0239

SLR

0.6834 ± 0.0220

0.6855 ± 0.0218

0.6964 ± 0.0216

0.7068 ± 0.0213

0.7041 ± 0.0213

0.6478 ± 0.0227

*

*

LP

0.6325 ± 0.0220

0.6756 ± 0.0214

0.7342 ± 0.0210

0.7378 ± 0.0212

0.6894 ± 0.0218

0.6616 ± 0.0225

0.6095 ± 0.0241

0.5687 ± 0.0239

682,685 (chr1-22)

Chi Sq

0.6834 ± 0.0220

0.7369 ± 0.0206

0.7433 ± 0.0204

0.7184 ± 0.0212

0.6438 ± 0.0227

0.6034 ± 0.0235

0.5445 ± 0.0239

0.5349 ± 0.0239

SWRF

0.6834 ± 0.0220

0.7006 ± 0.0213

0.6978 ± 0.0216

0.6934 ± 0.0220

0.6851 ± 0.0220

0.6293 ± 0.0231

0.5160 ± 0.0178

0.5029 ± 0.0127

SLR

0.6834 ± 0.0220

0.6911 ± 0.0218

0.7100 ± 0.0214

0.7354 ± 0.0206

0.6970 ± 0.0218

0.6874 ± 0.0220

*

*

LP

0.6325 ± 0.0229

0.6756 ± 0.0221

0.7342 ± 0.0206

0.7315 ± 0.0206

0.7151 ± 0.0210

0.7145 ± 0.0214

0.6096 ± 0.0204

0.5435 ± 0.0122

  1. The entries are the cross-fold classification AUCs and the 95% confidence intervals obtained from application of the kNN classifier to a specified number of top-ranked SNPs. Bold cells indicate where LP significantly outperforms at least one of the other methods. The SLR method is implicitly feature-selective, reducing the feature space to under 500 features for all experiments (indicated by cells containing *).
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BMC Genomics

ISSN: 1471-2164

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