Rule Extraction From Trained Neural Networks

Rule Extraction From Trained Neural Networks

Brian HudsonUniversity of Portsmouth, UK

Artificial Neural Networks

Advantages High accuracy Robust Noisy data

Disadvantages Lack of comprehensibilty

Trepan A method for extracting a decision tree from

an artificial neural network (Craven, 1996). The tree is built by expanding nodes in a

best first manner, producing an unbalanced tree.

The splitting tests at the nodes are m-of-n tests e.g. 2-of-{x1, ¬x2, x3}, where the xi are Boolean

conditions The network is used as an oracle to answer

queries during the learning process.

Splitting Tests Start with a set of candidate tests

binary tests on each value for nominal features

binary tests on thresholds for real-valued features

Find optimal splitting test by a beam search, initializing beam with candidate test maximizing the information gain.

Splitting Tests To each m-of-n test in the beam and each

candidate test, apply two operators: m-of-(n+1) e.g. 2-of-{x1, x2} => 2-of-{x1, x2, x3} (m+1)-of-(n+1) e.g. 2-of-{x1, x2} => 3-of-{x1, x2, x3}

Admit new tests to the beam if they increase the information gain and differ significantly (chi-squared) from existing tests.

Data Modelling The amount of training data reaching

each node decreases with depth of tree. TREPAN creates new training cases by

sampling the distributions of the training data empirical distributions for nominal inputs kernel density estimates for continuous inputs

Apply oracle (i.e. neural network) to new training cases to assign output values.

Application to Bioinformatics

Prediction of Splice Junction sites in Eukaryotic DNA

Splice Junction Sites

Consensus Sequences Donor

-3 -2 -1 +1 +2 +3 +4 +5 +6C/G A G | G T A/G A G T

Acceptor-12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1

1

C/T C/T C/T C/T C/T C/T C/T C/T C/T C/T A G | G

EBI Dataset Clean dataset generated at EBI

(Thanaraj, 1999) Donors

training set: 567 positive, 943 negative test set: 229 positive, 373 negative

Acceptors training set: 637 positive, 468 negative test set: 273 positive, 213 negative

Results

C5 ANN TREPAN

Donor Training set 94.2 94.5 92.3 Test set 91.9 93.9 90.7

Acceptor Training set 86.5 86.3 77.7 Test set 84.7 87.5 78.4

TREPAN Donor Tree

3 of {-2=A, -1=G, +3=A, +4=A, +5=G}

Positive869:74

Negative43:533

C/G A G | G T A/G A G T

Yes No

C5 Donor Tree (extract)p5=G p3=C or p3=T => NEGATIVE p3=A p2=G => POSITIVE p2=A p4=A or p4=G => POSITIVE p4=C or p4=T => NEGATIVE p2=C p4=A => POSITIVE else => NEGATIVE p2=T p6=A or p6=G => NEGATIVE p6=C or p6=T => POSITIVE p3=G p4=T => NEGATIVE p4=C p6=T => POSITIVE else => NEGATIVE

Trepan Acceptor Tree1 of {-3=G, -5=G}

NEGATIVE {-3=A}

NEGATIVE

POSITIVENEGATIVE

2 of {+1!=G, -5=G}

C/T … C/T A G | G

Application to Chemoinformatics

1. Learning general rules2. Conformational Analysis3. QSAR dataset

Oprea Dataset 137 diverse compounds Classification

62 leads, 75 drugs 14 descriptors (from Cerius-2)

MW, MR, AlogP Ndonor, Nacceptor, Nrotbond Number of Lipinski violations

T.I. Oprea, A.M. Davis, S.J. Teague & P.D. Leeson, “Is there a difference between Leads & Drugs? A Historical Perspective”, J. Chem. Inf. & Comput. Sci., 41, 1308-1315, (2001).

C5 tree

MW <= 380 [ Mode: lead ]

Rule of 5 Violations = 0 [ Mode: lead ]

Hbond acceptor <= 2 [ Mode: lead ] => lead

Hbond acceptor > 2 [ Mode: drug ] => drug

Rule of 5 Violations > 0 [ Mode: lead ] => lead

MW > 380 [ Mode: drug ] => drug

Trepan Oprea Tree1 of { MW<296, MR<85 }

Lead52:3

Unclassified12:49

MW<454

Drug1:20

Conformational Analysis 300 conformations from

5ns MD simulation of rosiglitazone Classified by length of long axis into

Extended – distance > 10A Folded – distance < 10A

8 torsion angles

In house data.

Rosiglitazone

Agonist of PPAR gamma Nuclear Receptor Regulates HDL/LDL and triglycerides Active ingredient of Avandia for Type II

Diabetes

SN

O

O

H

O

NCH3

N

Distances

TZD2

2

4

6

8

10

12

14

16

0 1000 2000 3000 4000 5000

C5 treeT5 <= 269 [ Mode: extended ]

T5 <= 52 [ Mode: extended ]

T7 <= 185 [ Mode: extended ] => extended

T7 > 185 [ Mode: folded ]

T6 <= 75 [ Mode: folded ] => folded

T6 > 75 [ Mode: extended ]

T5 <= 41 [ Mode: folded ]

T8 <= 249 [ Mode: folded ] => folded

T8 > 249 [ Mode: extended ] => extended


T5 > 52 [ Mode: extended ]




T8 <= 22 [ Mode: extended ] => extended

T8 > 22 [ Mode: folded ] => folded




T5 > 269 [ Mode: folded ] => folded

Trepan Conformation Tree

T5 < 180

Extended133:0

Unclassified2:5

2 of { T7<181, T2>172}

Folded0:161

Ferreira Dataset “typical” QSAR dataset 48 HIV-1 Protease inhibitors Activity as pIC50

Low pIC50 < 8.0 High pIC50 > 8.0

14 descriptors (mostly topological)

R. Kiralj and M.M.C. Ferreira, “A-priori Molecular Descriptors in QSAR : a case of HIV-1 protease inhibitors I. The Chemometric Approach”, J. Mol. Graph. & Modell. 21, 435-448, (2003)

Original Results PLS model Activity determined by

X9,X11,X10,X13 R2 = 0.91, Q2=0.85, Ncomps=3

C5 tree

X11 <= 2.5 [ Mode: low ]

X13 <= 16.7 [ Mode: low ] => low

X13 > 16.7 [ Mode: high ] => high

X11 > 2.5 [ Mode: high ] => high

Trepan Ferreira Tree

1 of { X13<16.1, X9<3.4 }

High1:24

X1<552

Low17:1

Low4:1

High0:1

X6<0.04

Accuracy

Dataset C5 ANN TREPANFerreira 91.8% 98.0% 91.8%Oprea 69.3% 68.6% 65.7%Conformer 99.0% 94.4% 94.4%

Conclusions Reasonable Accuracy Comprehensible Rules

Acknowledgements David Whitley. Tony Browne. Martyn Ford. BBSRC grant reference BIO/12005.

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Rule Extraction From Trained Neural Networks