3.6.7.4 The C50 algorithm

150

3.6.7.5 Seeds classification

152

3.6.7.6 Factor Xa protease cleavage data classification

153

.8 The random forest algorithm

154

ary

158

ic-Epigenetic Interplay Discovery

160

biological question — the genetic-epigenetic interplay

ttern discovery problem

161

gression analysis

162

e ordinary linear regression analysis algorithm

167

.1 The least squared error approach

167

.2 Assess the fitness of a regression model

170

.3 The significance analysis of regression coefficients

173

.4 The regression model confidence bands

175

.5 R function for ordinary linear regression analysis

175

e generalised additive model algorithm

179

e Bayesian linear regression algorithm

183

e constrained regression analysis algorithms

184

.1 The ridge linear regression algorithm

185

.2 The Lasso linear regression algorithm

187

.3 The elastic net linear regression algorithm

190

nking variables using the vip package

191

e nonlinear regression analysis algorithms

192

igenetic-genetic interplay pattern discovery

195

.1 Methylation site to gene — the M2E models

197

.2 Gene to methylation site association — E2M models

203

ary

207

al Pattern Discovery

209

biology question

210

roduction of baseline estimation approaches

210

e Whittaker-Henderson algorithm

212

e spline smoother

218

e adaptive iterative reweighted penalised least square

oother

220

e asymmetric least square smoother

221

e Bayesian Whittaker-Henderson algorithm

224

7.1 The working principle of BWH

224

7.2 The smoothing of the extracted peak spectrum

228

7.3 The generation of the merged and unique peaks

229