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Table 3 Hierarchical regression and support vector machine models with PTEN mutation status and protein assays predicting neurobehavioral measures

From: Cross-level analysis of molecular and neurobehavioral function in a prospective series of patients with germline heterozygous PTEN mutations with and without autism

  Hierarchical regression Support vector machine
Predictors retained beyond
PTEN mutation status
PTEN mutation Protein levels PTEN mutation and protein levels
  R2 ΔR2 ΔF (p) Fivefold cross-validation R/R2
Full-scale IQ PTEN, total AKT  < .01 .17 5.90 (.005) .33/.11*
Verbal IQ PTEN, total AKT, MnSOD  < .01 .16 4.97 (.004) .35/.12*
Non-verbal IQ EIF2A  < .01 .08 4.73 (.034) .28/.08*
Language composite EIF2A  < .01 .09 5.57 (.022) .27/.08*
Frontal-subcortical composite PTEN, p27  < .01 .16 5.56 (.006) .26/.07*
Attention composite p27 .02 .07 4.72 (.034) .14/.02
Working memory .02 .37/.14*
Processing speed .01 .20/.04
Motor skills composite p27 .01 .10 6.48 (.014) .01/.01
Internalizing problems  < .01 .24/.06
Externalizing problems P-AKT, P-S6, S6 .02 .20 4.67 (.006) .47/.22**
Autism traits .03 .17/.03
Repetitive behavior Total P-ERK, P-S6  < .01 .20 4.66 (.006) .22/.05
Adaptive behavior .01 .10/.01
  1. Hierarchical regression models included PTEN mutation status in step 1 and all protein levels in step 2, with specific predictor entry set at p < .05 and removal at p > .10. Support vector models included PTEN mutation status and all protein assays in separately predicting each neurobehavioral variable. Support vector model fivefold cross-validation R significance based on full sample. *p < .05, **p < .001