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Table 19 The selected LPLM with ρ = 0.82

From: Ridge regression estimated linear probability model predictions of O-glycosylation in proteins with structural and sequence data

Variable

β

Variable

β

Variable

β

Variable

β

intercept

–3.7350

m6S ☼

–0.4411

p2Ia

–0.3424

p7T

–1.0516

m1Da

0.0982

m6V

0.5139

p2L

–0.0053

p8Ga

–0.0074

m1La

1.0127

m7H

2.3444

p2P

2.6960

p8K ☼

0.5589

m1Ra

–0.1413

m7K

0.8149

p2Va

0.0130

p8Na

0.5041

m1S

–0.1913

m7La

–0.4651

p3Ta

0.4597

p8Qa

0.3987

m3Aa

0.5288

m8A

0.5662

p4Aa

0.9764

pos

0.3384

m3G

1.1410

m8V

–0.0763

p4E

1.3507

ASAa

–0.0144

m4N ☼

0.1992

p1Da

0.4975

p4H

–0.1231

Ia

–0.2885

m4R

0.0413

p1Ea

–0.3194

p4Ia

–0.0779

Helix

1.7757

m4Va

–0.2451

p1Fa

0.9501

p5H

0.1778

BH

0.3832

m4Ya

–0.0003

p1L

–0.7827

p6L

1.2946

BH_strand

–0.8759

m5D

1.1952

p1S

0.1261

p6Ta

1.0123

Phi anglea

–0.0047

m6E

0.0586

p1T

2.3710

p7A

1.4453

Psi anglea

–0.0011

  1. a / ☼ not significant at 10% in the “equivalent” classical logit model / LPM