Development of the KY-methods for use on toxicity prediction○ Kohtaro Yuta

In Silico Data, Ltd. (Japan) (http://www.insilicodata.com)◆ Research object : Developing new powerful data analysis
methods that are specialized in toxicity evaluation◆ Main difficulty to execute toxicity evaluation
1. Structural diversity of compounds is quite large
2. Number of samples used in the analysis is very large
3. Quite complex of the toxicity expression mechanism
4. High classification and prediction value is required

Data analysis techniques that is normallyused are shortage in toxicity evaluation

Data analysis technique that isnormally used is a shortagein toxicity evaluation
New data analysis methods(KY-methods) have beendeveloped in order to solve
toxicity evaluation problems

Outstanding features of the◆ Binary classification ◆1. Constantly achieve perfect (100%) classification
under any conditions
・Highly overlapped class sample data set
・Quite large number of sample data set (tens and several thousands of)
2. Starting sample set was divided into・ small and clean sample set
Repeat these operation, until
all samples are correctly classified・ small and hierarchical sample set◆ Variation of the “KY-methods on binary classifier”
Binary classification ; 3 approaches
1. Two model KY- discriminant method
2. One model KY- discriminant method
3. Model free KY- discriminant method◆ Fitting : Regression analysis ◆1. Constantly achieve high coefficient of correlation andhigh decision coefficient under any conditions
・Widely distributed sample data space・Quite large number of sample data set (tens and several thousands of)
2. Starting sample set was divided into
・’ inlier’ and ‘outlier’ sample set
Repeat these calculation, until
no more can this operation
・ small and hierarchical sample set◆ Variation of the “KY-methods on regression methods”
Fitting (multi regression); 3 approaches
1. KY-fitting with discriminant method
2. Three zone KY-fitting method
3. Model free KY-fitting method

List of application examples of the KY method and conclusions
Example 1 (Binary data) : Ames test sample data set
About 7000 Ames test sample dataset ⇒ Perfect classification＊Usual multi-variate and pattern recognition methods can’tExample 2 (Binary data) : Skin sensitization sample data set
About 600 sample dataset ⇒ Perfect classification＊Usual multi-variate and pattern recognition methods can’t＊Poster P05-21 Sato et.al, Euro Tox 2013
* All six KY-methods are patented and pending applications in JP, USA and EU
and some were Korea and China.

Example 3 ( Continuous data): Fish toxicity sample data set

About 800 Fish toxicity sample dataset ⇒ High decision coefficient＊Usual multi-variate and pattern recognition methods can’t◆ Results and conclusions:

Very high correlation coefficient and perfect classification(100%) were realized by newly developed KY-methods.
On the toxicity research field, it is almost impossible andhard to achieve excellent and stable evaluation results
by using the conventional data analysis methods.
All Rights Reserved, Copyright © INSILICODATA LIMITED 2013

Big toxicity data analysis by the combination of
the PCA method and the KY-methods○ Kohtaro Yuta

In Silico Data, Ltd. (http://www.insilicodata.com)◆ Apply multi-step and re-sampling technologies by the KY-methods to the PCA for handling big toxicity data◆

Not perfect classificationLargeoverlapping
space

Complex loading plot

AdaBoost

PCA

Starting 593 compounds set, 63 parameters

When a large number of sample data were applied to,
1. binary classification method
perfect classification is not feasible.
2. PCA method
clear sample plot and simple loading plot are difficult to generate.
When used in combination of the KY-method and PCA, this was generate great result.
1. KY-method
perfect classification is achieved.
2. PCA method
clear sample plot and simple loading plot are generated.
□What is the KY-methods◆ Classification results of compounds which are included in◆ Features of the KY (K-step Yard sampling) methods

1. Always achieve perfect (100%) classification under any conditions
・Highly overlapped class sample data set
・Quite large number of sample set (tens and several thousands of)
2. Starting sample set was divided into・ small and clean sample set
・ small and hierarchical sample set
3. Applicable not only the discriminant but multi-regression analysis“ Two model KY-method for Discriminant analysis ”
High reliability
High reliability
No classification
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Positive Zone

Perfect classification
Clusteredspace

Gray Zone

Simpleloadingplot

Step1 : Correctly classified 275 compounds which areincluded in the high reliability space, 21 parameters

Clusteredspace
Perfect classification

Simpleloadingplot

Negative Zone

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Step 2the “High reliability” space of the “KY-methods”

Step2: Correctly classified 211 compounds, 15 parameters○ ○ ○○ ○ ○ ○○○ ○○○○ × ○ ○××××××××××
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Clusteredspace
Perfect classification◆ Used samples : Skin sensitization data

Simpleloading
plot

Total ; 593, Positive 419, Negative; 174◆ Classification rｅｓｕｌｔ by the KY-method (100% correct)
Step1; Positive 187
Step2; Positive 177
Step3; Positive 55

Negative 88
Negative 34
Negative 52

Grey zone 318
Grey zone 107
Grey zone0

Perfect classification(100%)◆ Classification Results by various methods (63params)
Methods
Total
Positive Negative
N.N.
86.0%
86.2%
85.6%
SVM
91.7%
98.3%
75.9%
LDA
87.0%
95.2%
67.2%
KNN (K=5) 77.7%
86.9%
55.8%
AdaBoost
82.1%
80.0%
87.4%

Step3 : Finally classified 107 compounds, 28 parameters◆ Conclusions:

1. Even if it was a large number of samples, it has been found that achieving
an excellent data analysis results by using a combination of the KY-methodand PCA.
2. Perfect (100%) classification was achieved by the KY-method
3. As a result of applying PCA for resampled sample set by the KY-method, it
was possible to obtain a clean clustered sample space and much more
simple and clear loading plot.

All research works and screen displays were executed and generated by
the ADMEWORKS : ModelBuilder program developed by FJQS (Fujitsu Kyushu Systems Ltd.)