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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