Table 3 Input disease biomarkers for machine learning.

From: Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson’s disease

Input disease biomarkers

ABCA1

ERBB2

➤ HNRNPA1

➤ PCSK1N

STK11

pCLIC6

➤ pLTB4R

➤ pPRR15

ACAT2

FABP3

IDE

PGM2

TGM3

pDKC1

pNEU1

pSPPL2B

C4BPA

FAM151A

IGF1

RALA

UCHL1

pDYNC1LI1

➤ pPLA2G4A

pSSB

C6orf211;ARMT1

FLOT1

ITCH

RPS4X

UGP2

pEDN1

➤ pPPFIA1

pTBC1D9B

CAPN5

FUT6

KLK10

SAA4

VASP

pFNBP1

pPRG4

pTJP3

CC2D1A

GALNT7

NCCRP1

SLC22A13

pCLDN14

pLAD1

pPRKAR2A

pTMPO

  1. List of the feature selection inputs (potential biomarkers) for disease biomarkers. Intermediate results after backward feature elimination and before exhaustive feature selection are bolded and italicized. The top biomarkers are marked with ➤.
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