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Table 2 The list of twenty best genes selected by FSMLP in the first stage

From: Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering

Gene ID Image ID Name
FGFR4 (*)784224 fibroblast growth factor receptor 4
EST 208699 EST
FCGRT (*)770394 Fc fragment of IgG, receptor, transporter, alpha
AF1Q (*)812105 transmembrane protein
HCLS1 (*)767183 hematopoietic cell-specific Lyn substrate 1
NAB2 (*)770868 NGFI-A binding protein 2 (ERG1 binding protein 2)
CDH2 (*)325182 cadherin 2, N-cadherin (neuronal)
EHD1 (*)745019 EH domain containing 1
HLA-DQA1 80109 major histocompatibility complex, class II, DQ alpha 1
LGALS3BP 811000 lectin, galactoside-binding, soluble, 3 binding protein (galectin 6 binding protein)
BAT3 898237 HLA-B associated transcript-3
SGCA 796258 sarcoglycan, alpha (50 kD dystrophin-associated glycoprotein)
ESTs 244618 ESTs
NOE1 52076 olfactomedinrelated ER localized protein
LSP1 (*)143306 lymphocyte-specific protein 1
IFG2 296448 insulin-like growth factor 2 (somatomedin A)
PMS2L12 (*)878652 postmeiotic segregation increased 2-like 12
NA 450152 NA
FVT1 (*)814260 follicular lymphoma variant translocation 1
CCNE1 68950 cyclin E1
  1. We used a network with 2308 input nodes, 150 hidden nodes and 4 output nodes. These twenty genes are selected based on the gate opening values. We made several runs of ordinary MLP using these twenty genes and in each case the network was able to correctly classify all training and blind test examples. In the second stage, the FSMLP network is used to select ten best genes from amongst the twenty genes selected in the first stage. These ten genes are marked by asterisks. This set of ten genes has adequate cancer specific signatures to categorize the four types of SRBCTs.