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Table 2 Comparison of Partial Least Squares and Neural Net.

From: An integrated approach to epitope analysis I: Dimensional reduction, visualization and prediction of MHC binding using amino acid principal components and regression approaches

 

PLS

Method 1

NetMHCII

NetMHCIIPan

 

AROC

r 2

AROC

r 2

AROC

r 2

AROC

r 2

 

SB

WB

 

SB

WB

 

SB

WB

 

SB

WB

 

DRB1*0101

0.713

0.579

0.541

0.838

0.645

0.796

0.848

0.691

0.811

0.835

0.647

0.753

DRB1*0301

0.675

0.610

0.476

0.987

0.954

0.996

0.958

0.882

0.966

0.841

0.602

0.736

DRB1*0401

0.690

0.537

0.491

0.986

0.956

0.995

0.951

0.845

0.945

0.778

0.631

0.636

DRB1*0404

0.695

0.559

0.595

0.986

0.961

0.995

0.940

0.845

0.954

0.854

0.630

0.769

DRB1*0405

0.702

0.577

0.527

0.985

0.966

0.996

0.927

0.846

0.947

0.809

0.588

0.682

DRB1*0701

0.729

0.612

0.559

0.987

0.958

0.997

0.965

0.893

0.963

0.879

0.716

0.801

DRB1*0802

0.776

0.602

0.587

0.990

0.980

0.997

0.979

0.880

0.973

0.841

0.550

0.770

DRB1*0901

0.659

0.532

0.403

0.988

0.961

0.997

0.969

0.899

0.956

0.813

0.576

0.673

DRB1*1101

0.681

0.565

0.550

0.981

0.957

0.996

0.968

0.893

0.969

0.855

0.594

0.787

DRB1*1302

0.600

0.521

0.441

0.978

0.830

0.997

0.981

0.837

0.965

0.806

0.579

0.759

DRB1*1501

0.656

0.552

0.494

0.987

0.960

0.995

0.940

0.795

0.945

0.768

0.544

0.667

DRB3*0101

0.595

0.510

0.451

0.983

0.932

0.996

0.956

0.872

0.935

0.879

0.613

0.737

DRB4*0101

0.724

0.667

0.604

0.987

0.966

0.997

0.686

0.942

0.976

0.892

0.621

0.795

DRB5*0101

0.727

0.607

0.553

0.985

0.958

0.997

0.960

0.884

0.965

0.872

0.649

0.789

Average

0.687

0.574

0.519

0.975

0.927

0.982

0.931

0.857

0.948

0.837

0.610

0.740

  1. The performance of partial least squares (PLS) compared to the neural network regression base on amino acid principal components (NN PCAA) described with two neural network predictors based on substitution matrices. SB and WB columns are the area under the receiver operator curve (AROC) obtained by converting the continuous for the regression fit output to a categorical output SB = strong binder (< 50 nM) WB = weak binder (> 50 nM and <500 nM) and non-binder (> 500 nM). The r2 is indicated is the metric for how well the particular predictor predicts the values in the training set.