Transcript Document 7525884

Data Mining with Neural Networks
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Standard data mining terminology
Preprocessing data
Running neural networks via Analyze/StripMiner
Cherkassky’s nonlinear regression problem
Magnetocardiogram data
CBA (chemical and biological agents) Data
Drug design with neural networks
The paradox of learning
Principal Component Analysis (PCA)
The Kernel Transformation and SVMs (Support Vector Machines)
Structural and empirical risk minimization
(Vapnik’s theory of statistical learning)
Standard Data Mining Terminology
• Basic Terminology
- MetaNeural Format
- Descriptors, features, response (or activity) and ID
- Classification versus regression
- Modeling/Feature detection
- Training/Validation/Calibration
- Vertical and horizontal view of data
• Outliers, rare events and minority classes
• Data Preparation
- Data cleansing
- Scaling
• Leave-one-out and leave-several-out validation
• Confusion matrix and ROC curves
Installing Basic Version of Analyze
• Put analyze and gnuplot and wgnuplt.hlp and wgnuplot.mnu in working folder
• gnuplot scripts for plotting are:
- analyze resultss.ttt –3305 for scatterplot
- analyze resultss.ttt –3313 for errorplot
- analyze resultss.ttt –3362 for baniary classification
• More fancy graphics are in the *.jar files (needs java runtime environment)
• For basic help you can try:
- analyze > readme.txt
- analyze help –998
- analyze help –997
- analyze help –008
• For beginners (unless the Java runtime environment is installed), I
recommend displaying results via gnuplot operators –3305, -3313 and –3362
• To familiarize with Analyze, study the script files from this handout
• Don’t forget to scale data
Running neural networks in Analyze/Stripminer
• Prepare a.pat and a.tes files for training and testing (or what you want to name it)
• Make sure data are in MetaNeural format and properly scaled
(scaling: analyze a.txt 8)
(splitting: analyze a.txt.txt 20; seed ‘0’ keeps order)
(copy cmatrix.txt a.pat and copy dmatrix.txt a.tes)
• Run neural network “analyze a.pat 4331”
• copy a meta, edit meta and run again for overriding parameter settings
• Results are in resultss.xxx and resultss.ttt for training and testing respectively
• Either descale (option –4) and inspect results.xxx and results.ttt
(analyze resultss.xxx –4; analyze resultss.ttt –4)
• Or visualize via analyze resultss.ttt –3305 (and –3313, and –3362)
A Vertical and a Horizontal View of the Data Matrix
• Vertical view: feature space


ANM  a Tj  for j  1, N

a Tj  a1 j ... aij ... a NM   aij  for i  1, M
• Horizontal view: data space

x
 1

  x2 
A 
 ... 
 
 xN 

xi  ai1 ai 2 ... aiM
yi
IDi 
Preprocessing: Basic scaling for neural networks
• Mahalanobis scale descriptors
x 
x x
x
• [0-1] scale response
y 
y  ymin
ymax  ymin
• Use operator 8 in Analyze code:
e.g., typing “analyze a.pat 8” will give scaled results in a.pat.txt
• Note: another handy operator is the splitting operator (20)
e.g., typing < analyze a.pat.txt 20>
will split file in cmatrix.txt and dmatrix.txt
usimg 0 as random number seed put the first #data in cmatrix.txt
using a different seed scrambles up data
Cherkassky’s Nonlinear Benchmark Data
• Generate 500 data (400 training; 100 testing)
y  exp 2 x1 sin x4   sin x2 x3 
Errorplot for Test Data
1.4
 0.25  x  0.25
K-PLS
Target and Predicted Values
• Impossible data for linear models
Target and Predicted Values
REM cherkasm
REM GENERATE DATA (2 500 2)
analyze a.pat 3301
REM SCALE DATA
analyze cherkas.pat 8
REM SPLIT DATA IN TRAINING AND TEST SET (400 2)
analyze cherkas.pat.txt 20
copy cmatrix.txt a.pat
copy dmatrix.txt a.tes
REM RUN METANEURAL VIA ANALYZE
analyze a.pat 4331
DESCALE RESULTS
analyze resultss.ttt -4
REM VISUALIZE RESULTS FOR TEST SET
analyze resultss.ttt -3305
pause
analyze resultss.ttt -3313
pause
gnuplot error1.plt
pause
REM VISUALIZE RESULTS FOR TRAINING SET
analyze resultss.xxx -3305
pause
analyze resultss.xxx -3313
pause
1.3
1.2
1.1
1
0.9
target
predicted
0.8
0.7
0
10
20
30
40
50
60
70
80
90
100
90
100
Sorted Sequence Number
Thu Mar 13 18:14:14 2003
Errorplot for Test Data
1.4
PLS
1.3
1.2
1.1
1
0.9
target
predicted
0.8
0.7
Note: eta = 0.01; train to 0.02 error
0
10
20
30
40
50
60
Sorted Sequence Number
Thu Mar 13 19:28:39 2003
70
80
Iris Data
Target and Predicted Values
REM IRISM.BAT (3 classes)
REM GENERATE DATA (5)
analyze iris 3301
REM STRIP HEADER
analyze iris.txt 100
REM SCALE DATA
analyze iris.txt.txt 8
copy iris.txt.txt.txt a.txt
REM SPLIT DATA (100 2)
analyze a.txt 20
copy cmatrix.txt a.pat
copy dmatrix.txt a.tes
REM METANEURAL
REM do copy a meta afterwards to customize
analyze a.pat 4331
pause
REM SCATTERPLOT FOR TEST DATA
analyze resultss.ttt -3305
pause
REM ERRORPLOT FOR TEST DATA
analyze resultss.ttt -3313
pause
REM SCATTERPLOT FOR TRAINING DATA
analyze resultss.xxx -3305
pause
REM ERRORPLOT FOR TRAINING DATA
analyze resultss.xxx -3313
pause
Errorplot for Test Data
3
2.5
2
1.5
predicted
target
1
0
5
10
15
20
25
30
35
Sorted Sequence Number
Sun Mar 16 16:11:07 2003
40
45
For homework:
- copy a meta
- Edit meta for different experiments
- summarize and report on experiments
50
Classical Regression Analysis
X
T
mn
X nm


1
A
NAME
Ala
Asn
Asp
Cys
Gln
Glu
Gly
His
Ile
Leu
Lys
Met
Phe
Pro
Ser
Thr
Trp
Tyr
Val
PIE
0.23
-0.48
-0.61
0.45
-0.11
-0.51
0
0.15
1.2
1.28
-0.77
0.9
1.56
0.38
0
0.17
1.85
0.89
0.71
PIF
0.31
-0.6
-0.77
1.54
-0.22
-0.64
0
0.13
1.8
1.7
-0.99
1.23
1.79
0.49
-0.04
0.26
2.25
0.96
1.22
DGR
-0.55
0.51
1.2
-1.4
0.29
0.76
0
-0.25
-2.1
-2
0.78
-1.6
-2.6
-1.5
0.09
-0.58
-2.7
-1.7
-1.6
SAC
254.2
303.6
287.9
282.9
335
311.6
224.9
337.2
322.6
324
336.6
336.3
366.1
288.5
266.7
283.9
401.8
377.8
295.1
MR
2.126
2.994
2.994
2.933
3.458
3.243
1.662
3.856
3.35
3.518
2.933
3.86
4.638
2.876
2.279
2.743
5.755
4.791
3.054
Lam
-0.02
-1.24
-1.08
-0.11
-1.19
-1.43
0.03
-1.06
0.04
0.12
-2.26
-0.33
-0.05
-0.31
-0.4
-0.53
-0.31
-0.84
-0.13

X nm wm1

T
X mn X nm wm1

T
X mn X nm wm1

T
X mn X nm wm1

wm1

yˆ test,1


Vol
82.2
112.3
103.7
9.1
127.5
120.5
65
140.6
131.7
131.5
144.3
132.3
155.8
106.7
88.5
105.3
185.9
162.7
115.6
DDGTS
8.5
8.2
8.5
11
6.3
8.8
7.1
10.1
16.8
15
7.9
13.3
11.2
8.2
7.4
8.8
9.9
8.8
12
ID
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

 yn1
T
 X mn
yn1
T 
 X mn yn1

 X
 X X nm
T
mn
T
mn


X nm

 X test,m wm1

X yn1
1
T 
X mn yn1
1
T
mn
Pseudo inverse
c
LS-SVM


K nn wn1  yn1

1 
wn1  K nn   I  yn1
• Adding the ridge makes the matrix positive definite
• The ridge also performs regularization!!!!
• The problem is now equivalent to minimizing the following:
N

i 1
2
yˆ i  yi   w 2
Heuristic formula for lambda
Local Learning in Kernel Space
ntrain
yˆ i 
s
j 1
ntrain
ij
s
j 1
yj
ij


y  Kw


y
w  ntrain
 sij
j 1
ntrain
yˆ i 
s
j 1
ntrain
ij
s
j 1
yj
Local Learning in Kernel Space
ij


y  Kw


y
w  ntrain
 sij
j 1
 ntra in 
   sij 
 i 1 
1
Σ
Σ
x1
y1
This layer gives a similarity score
with each datapoint
Σ
Σ
Σ
yi
xi
ŷi
Σ
xM
Σ
Make up kernels
Σ
sij  e

yM
Weights correspond to
the dependent variable
for the entire training data
 
xi  x j
2 2
Kind of a nearest
neighbor weighted
prediction score
What Does LS-SVM Do?
• K-PLS is like a linear method in “nonlinear kernel” space
• Kernel space is the “latent space” of support vector machines (SVMs)
• How to make LS-SVM work?
- Select kernel transformation (e.g., usually a Gaussian kernel)
- Select regularization parameter
w1
(Data Set)NxM
Kernel, KNN
NxN
wi
wN
S
ŷ N
prediction
Weight vector
K NN

 wN  yˆ N
What is in a Kernel?
• A kernel can be considered as a (nonlinear) data transformation
- Many different choices for the kernel are possible
- Most popular is the Radial Basis Function or Gaussian kernel
• The Gaussian kernel is a symmetric matrix
- Entries reflect nonlinear similarities amongst data descriptions
- As defined by:

K NN
 k11 k12
k
k 22
21


 ki1 ki 2

k N 1 k N 2
kij  e

...
...
kij
...
 
x j  xl
2 2
k1N 
k 2 N 
kiN 

k NN 
2
t2
t1
x3
x1
y
x2
PharmaPlot
'negative'
'positive'
Third PLS Component
0.1
0.08
0.06
0.04
0.02
0
-0.02
-0.04
-0.06
-0.08
-0.08 -0.06
-0.04 -0.02
0
First PLS Component
Wed Mar 19 15:23:32 2003
0.02
0.08
0.06
0.04
0.02
Second
PLS Component
0
-0.02
-0.04
-0.06
0.04
Data Visualization with Cardiomag Program
pat1.txt.txt
cardiomag patients.txt 402
vis.txt
pat2.txt.txt
vis.txt.txt
…
pat_ID.jpg
wave_val.cat
patients.txt
pat_view.jar
data visualization mode
(requires Java run time environment)
Raw data
Wavelet transformed data
DATA FOR PATIENT 97
5
2
2
2
2
2
2
2
2
0
0
0
0
0
0
0
0
0
-5
-2
-2
-2
-2
-2
-2
-2
-2
0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40
2
2
2
5
2
2
5
2
2
0
0
0
0
0
0
0
0
0
-2
-2
-2
-5
-2
-2
-5
-2
-2
0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40
5
2
2
2
2
2
5
5
2
0
0
0
0
0
0
0
0
0
-5
-2
-2
-2
-2
-2
-5
-5
-2
0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40
2
2
2
2
2
2
2
2
2
0
-2
0
0
0
0
0
0
0
0
-2
-2
-2
-2
-2
-2
-2
-2
0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40 0 20 40
Worth its Weight in Gold?
Data Mining Applications In DDASSL
• QSAR drug design
• Microarrays
• Breast Cancer Diagnosis(TransScan)
CO1
CO2
CO3
CO4
CO5
CO6
CO7
CO8
CO9
C10
...
C66
T1C11
T2C1
T2C11
T2C13
T2C16
T2C1
T2C9
Molecule #BR #C #CL #F #H #I #N #O #P #S #SI BALA IDC IDCBAR IDW IDWBAR K0 K1 K2
K3 KA1 KA2 KA3 NXC3 NXC4 NXCH10 NXCH3 NXCH4 NXCH5 NXCH6 NXCH7 NXCH8 NXCH9
NXP10 NXP2 NXP3 NXP4 NXP5 NXP6 NXP7 NXP8 NXP9 NXPC4 SI TOPOL90 TOPOL91
TOPOL92 TOPOL93 TOPOL94 TOPOL95 TOPOL96 TOPOL97 TOPOL98 TOPOL99 WW X0 X1 X2
XC3 XC4 XCH10 XCH3 XCH4 XCH5 XCH6 XCH7 XCH8 XCH9 XP10 XP3 XP4 XP5 XP6 XP7
XP8 XP9 XPC4 XV0 XV1 XV2 XVC3 XVC4 XVCH10 XVCH3 XVCH4 XVCH5 XVCH6 XVCH7
XVCH8 XVCH9 XVP10 XVP3 XVP4 XVP5 XVP6 XVP7 XVP8 XVP9 XVPC4 S001 S002 S003
S004 S005 S006 S007 S008 S009 S010 S011 S012 S013 S014 S015 S016 S017 S018 S019
S020 S021 S022 S023 S024 S025 S026 S027 S028 S029 S030 S031 S032 S033 S034 S035
S036 S037 S038 S039 S040 S041 S042 S043 S044 S045 S046 S047 S048 S049 S050 S051
S052 S053 S054 S055 S056 S057 S058 S059 S060 S061 S062 S063 S064 S065 S066 S067
S068 S069 S070 S071 S072 S073 S074 S075 S076 S077 S078 S079 S080 S081 S082 S083
S084 S085 S086 S087 S088 S089 S090 S091 S092 S093 S094 S095 S096 S097 S098 S099
S100 S101 S102 S103 S104 S105 S106 S107 S108 S109 S110 S111 S112 S113 S114 S115
S116 S117 S118 S119 S120 S121 S122 S123 S124 S125 S126 S127 S128 S129 S130 S131
S132 S133 S134 S135 S136 S137 S138 S139 S140 S141 S142 S143 S144 S145 S146 S147
S148 S149 S150 S151 S152 S153 S154 S155 S156 S157 S158 S159 S160 S161 S162 S163
S164 S165 S166 S167 S168 S169 S170 S171 S172 S173 S174 S175 S176 S177 S178 S179
S180 S181 S182 S183 S184 S185 S186 S187 S188 S189 S190 S191 S192 S193 S194 S195
S196 S197 S198 S199 S200 S201 S202 S203 S204 S205 S206 S207 S208 AbsBNP1 AbsBNP10
AbsBNP2 AbsBNP3 AbsBNP4 AbsBNP5 AbsBNP6 AbsBNP7 AbsBNP8 AbsBNP9 AbsBNPMax
AbsBNPMin AbsDGN1 AbsDGN10 AbsDGN2 AbsDGN3 AbsDGN4 AbsDGN5 AbsDGN6 AbsDGN7
AbsDGN8 AbsDGN9 AbsDGNMax AbsDGNMin AbsDKN1 AbsDKN10 AbsDKN2 AbsDKN3 AbsDKN4
AbsDKN5 AbsDKN6 AbsDKN7 AbsDKN8 AbsDKN9 AbsDKNMax AbsDKNMin AbsDRN1 AbsDRN10
AbsDRN2 AbsDRN3 AbsDRN4 AbsDRN5 AbsDRN6 AbsDRN7 AbsDRN8 AbsDRN9 AbsDRNMax
AbsDRNMin AbsEP1 AbsEP10 AbsEP2 AbsEP3 AbsEP4 AbsEP5 AbsEP6 AbsEP7 AbsEP8 AbsEP9
AbsEPMax AbsEPMin AbsFuk1 AbsFuk10 AbsFuk2 AbsFuk3 AbsFuk4 AbsFuk5 AbsFuk6 AbsFuk7
AbsFuk8 AbsFuk9 AbsFukMax AbsFukMin AbsG1 AbsG10 AbsG2 AbsG3 AbsG4 AbsG5 AbsG6
AbsG7 AbsG8 AbsG9 AbsGMax AbsGMin AbsK1 AbsK10 AbsK2 AbsK3 AbsK4 AbsK5 AbsK6
AbsK7 AbsK8 AbsK9 AbsKMax AbsKMin AbsL1 AbsL10 AbsL2 AbsL3 AbsL4 AbsL5 AbsL6 AbsL7
AbsL8 AbsL9 AbsLMax AbsLMin BNP BNP1 BNP10 BNP2 BNP3 BNP4 BNP5 BNP6 BNP7 BNP8
BNP9 BNPAvg BNPMax BNPMin Del(G)NA1 Del(G)NA10 Del(G)NA2 Del(G)NA3 Del(G)NA4 Del(G)NA5
Del(G)NA6 Del(G)NA7 Del(G)NA8 Del(G)NA9 Del(G)NIA Del(G)NMax Del(G)NMin Del(K)IA Del(K)Max
Del(K)Min Del(K)NA1 Del(K)NA10 Del(K)NA2 Del(K)NA3 Del(K)NA4 Del(K)NA5 Del(K)NA6 Del(K)NA7
Del(K)NA8 Del(K)NA9 Del(Rho)NA1 Del(Rho)NA10 Del(Rho)NA2 Del(Rho)NA3 Del(Rho)NA4
Del(Rho)NA5 Del(Rho)NA6 Del(Rho)NA7 Del(Rho)NA8 Del(Rho)NA9 Del(Rho)NIA Del(Rho)NMax
Del(Rho)NMin EP1 EP10 EP2 EP3 EP4 EP5 EP6 EP7 EP8 EP9 Fuk Fuk1 Fuk10 Fuk2 Fuk3
Fuk4 Fuk5 Fuk6 Fuk7 Fuk8 Fuk9 FukAvg FukMax FukMin Lapl Lapl1 Lapl10 Lapl2 Lapl3
Lapl4 Lapl5 Lapl6 Lapl7 Lapl8 Lapl9 LaplAvg LaplMax LaplMin PIP1 PIP10 PIP11 PIP12 PIP13
PIP14 PIP15 PIP16 PIP17 PIP18 PIP19 PIP2 PIP20 PIP3 PIP4 PIP5 PIP6 PIP7 PIP8 PIP9
PIPAvg PIPMax PIPMin piV SIDel(G)N SIDel(K)N SIDel(Rho)N SIEP SIEPA1 SIEPA10 SIEPA2
SIEPA3 SIEPA4 SIEPA5 SIEPA6 SIEPA7 SIEPA8 SIEPA9 SIEPIA SIEPMax SIEPMin SIG SIGA1
SIGA10 SIGA2 SIGA3 SIGA4 SIGA5 SIGA6 SIGA7 SIGA8 SIGA9 SIGIA sigmanew sigmaNV
sigmaPV SIGMax SIGMin SIK SIKA1 SIKA10 SIKA2 SIKA3 SIKA4 SIKA5 SIKA6 SIKA7 SIKA8
SIKA9 SIKIA SIKMax SIKMin sumsigma SurfArea Volume CAQSOL CHEM_POT CLOGP CMR
CSAREA_A CSAREA_B CSAREA_C DELTAHF DIPOLE ETOT EVDW HARDNESS HBAB HBDA HOMO
LENGTH_A LENGTH_B LENGTH_C LUMO MASS MUA MUB MUC NHBA NHBD NUMHB PISUBI
QMINUS QPLUS RA RB RC SAAB SAAC SABC SAREA SASAREA SASVOL SHAPE VLOOP1
VLOOP2 VLOOP3 VLOOP4 VLOOP5 VOLUME
LCCKA Log(10) of inhibition concentration
for "A" receptor site on Cholecystokinin
Electron Density-Derived TAE-Wavelet Descriptors
1 ) Surface properties are encoded on 0.002 e/au3 surface
Breneman, C.M. and Rhem, M. [1997] J. Comp. Chem., Vol. 18 (2), p. 182-197
2 ) Histograms or wavelet encoded of surface properties give TAE
property descriptors
Histograms
PIP (Local Ionization Potential)
Wavelet Coefficients
Validation Model: 100x leave 10% out validations
StripMiner with Feature Selection and Bootstrapping/Bagging
RAW DATA
Pre-processing:
- scaling
- ANN policy
RANDOM GAUGE
VARIABLE
Sensitivity Analysis
bootstrapping
REDUCED
FEATURE SET
Learning Algorithm
Neural Network
SVM
PLS
Bagging Prediction
Neural Network
SVM
PLS
PREDICTIVE MODEL
Data StripMining Approach for Feature Selection
PLS, K-PLS, SVM, ANN
1 - Q2 versus # Features on Validation Set
0.45
'evolve.txt' using 1:2
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
0
100
200
300
# Features
T hu Mar 13 15:59:57 2003
400
500
600
Kernel PLS (K-PLS)
• Introduced by Rosipal and Trejo (J. Machine Learning, December 2001)
• K-PLS gives almost identical (but more stable) results to SVMs for QSAR data
-
K-PLS is more transparent.
K-PLS allows to visualize in SVM Space
Computationally efficient and few heuristics
There is no patent on K-PLS
• Consider K-PLS as a “better” nonlinear PLS
t2
t1
x3
x1
y
x2
• Binding affinities to human serum
albumin (HSA): log K’hsa
• Gonzalo Colmenarejo, GalaxoSmithKline
J. Med. Chem. 2001, 44, 4370-4378
•
•
•
•
•
95 molecules, 250-1500+ descriptors
84 training, 10 testing (1 left out)
551 Wavelet + PEST + MOE descriptors
Widely different compounds
Acknowledgements: Sean Eakins (Concurrent)
N. Sukumar (Rensselaer)
WORK IN PROGRESS
GATCAATGAGGTGGACACCAGAGGCGGGGACTTGTAAATAACACTGGGCTGTAGGAGTGA
TGGGGTTCACCTCTAATTCTAAGATGGCTAGATAATGCATCTTTCAGGGTTGTGCTTCTA
TCTAGAAGGTAGAGCTGTGGTCGTTCAATAAAAGTCCTCAAGAGGTTGGTTAATACGCAT
GTTTAATAGTACAGTATGGTGACTATAGTCAACAATAATTTATTGTACATTTTTAAATAG
CTAGAAGAAAAGCATTGGGAAGTTTCCAACATGAAGAAAAGATAAATGGTCAAGGGAATG
GATATCCTAATTACCCTGATTTGATCATTATGCATTATATACATGAATCAAAATATCACA
CATACCTTCAAACTATGTACAAATATTATATACCAATAAAAAATCATCATCATCATCTCC
ATCATCACCACCCTCCTCCTCATCACCACCAGCATCACCACCATCATCACCACCACCATC
ATCACCACCACCACTGCCATCATCATCACCACCACTGTGCCATCATCATCACCACCACTG
TCATTATCACCACCACCATCATCACCAACACCACTGCCATCGTCATCACCACCACTGTCA
TTATCACCACCACCATCACCAACATCACCACCACCATTATCACCACCATCAACACCACCA
CCCCCATCATCATCATCACTACTACCATCATTACCAGCACCACCACCACTATCACCACCA
CCACCACAATCACCATCACCACTATCATCAACATCATCACTACCACCATCACCAACACCA
CCATCATTATCACCACCACCACCATCACCAACATCACCACCATCATCATCACCACCATCA
CCAAGACCATCATCATCACCATCACCACCAACATCACCACCATCACCAACACCACCATCA
CCACCACCACCACCATCATCACCACCACCACCATCATCATCACCACCACCGCCATCATCA
TCGCCACCACCATGACCACCACCATCACAACCATCACCACCATCACAACCACCATCATCA
CTATCGCTATCACCACCATCACCATTACCACCACCATTACTACAACCATGACCATCACCA
CCATCACCACCACCATCACAACGATCACCATCACAGCCACCATCATCACCACCACCACCA
CCACCATCACCATCAAACCATCGGCATTATTATTTTTTTAGAATTTTGTTGGGATTCAGT
ATCTGCCAAGATACCCATTCTTAAAACATGAAAAAGCAGCTGACCCTCCTGTGGCCCCCT
TTTTGGGCAGTCATTGCAGGACCTCATCCCCAAGCAGCAGCTCTGGTGGCATACAGGCAA
CCCACCACCAAGGTAGAGGGTAATTGAGCAGAAAAGCCACTTCCTCCAGCAGTTCCCTGT
APPENDIX: Downloading and Installing the JAVA
and the JAVA™ Runtime Environment
• To be able to make JAVA™ plots, the installation of JRE (the JAVA™ Runtime
Environment is required.
• The current version is the JAVA™ 2 Standard Edition Runtime Environment 1.4
This provides complete runtime support for JAVA™ 2 applications.
• In order to build a JAVA™ application you must download SDK.
The JAVA™ 2 SDK is a development environment for building applications,
applets, and components using the JAVA™ programming language.
• The current version of JRE or JDK for a specific platform can be downloaded
from the following site:
http://java.sun.com/j2se/1.4/download.html
• Make sure you set a path to the bin folder in the autoexec.bat file (or equivalent
for WindowsNT/XT or LINUX/UNIX.
Performance Indicators
• The RPI definitions include r2 and R2 for the training set and q2 and Q2 for
the test set. r2 is the correlation coefficient and q2 is 1-the correlation coefficient
for the test set.
• R2 is defined as

R  1 


2
 x  x  y  y 
x  x 2  y  y 2
2



train
• Q2 is defined as R2 for the test set

Q 


2
 x  x  y  y 
x  x 2  y  y 2
2



test
Note
iv) In bootstrap mode q2 and Q2 are usually very close to each other,
significant differences between q2 and Q2 often indicate an improper
choice for the krnel width, or an error in data scaling/pre-processing