Rule Extraction from trained Neural Networks Brian Hudson, Centre for Molecular Design, University of Portsmouth

Artificial Neural Networks  Advantages High accuracy Robust Noisy data  Disadvantages Lack of comprehensibilty

Rule Extraction  Rule extraction from trained Neural Networks  High fidelity to original network  TREPAN features Best-first tree growing Sampling query instances M of N rules

Bioinformatics applications  Black box solutions Neural Networks Hidden Markov models  Good test for TREPAN methodology

Gene Splicing  Well known bioinformatics problem  For details & links see

The “answer” is known  Donor sequence C/G A G | G T A/G A G T  Acceptor sequence C/T C/T C/T C/T C/T C/T C/T C/T C/T C/T A G |G

EBI clean dataset  Tidied up dataset generated at EBI  Donors training set 567 real & 943 unreal test set 229 real & 373 unreal  Acceptors training set 637 real & 468 unreal test set 273 real & 213 unreal

Summary of results

TREPAN tree for donors 3 of {p-2 =A, p-1=G, p+3=A, p+4=A, p+5=G} REAL 869/74 UNREAL 43/533 Network : 28x10x1 Training : 92.25% Testing : 90.7% C/G A G | G T A/G A G T

C5 tree for donors (part) p5=G p3=C or p3=T => FALSE p3=A p2=G => REAL p2=A p4=A or p4=G => REAL p4=C or p4=T => FALSE p2=C p4=A => REAL else => FALSE p2=T p6=A or p6=G => FALSE p6=C or p6=T => REAL p3=G p4=T => FALSE p4=C p6=T => REAL else => FALSE p4=A p2=C or p2=G or p2=T => REAL p2=A p-3=T => FALSE else => REAL p4=G p2=A or p2=C or p2=T => FALSE p2=G p1=A or p1=C => REAL p1=G or p1=T => FALSE

TREPAN tree for acceptors 1 of {p-3 =G, p-5=G} UNREAL 26/190 {p-3 =A} UNREAL 25/95 REAL 571/153 Network : 40x13x1 Training : 80.2% Testing : 80.9% UNREAL 13/32 2 of {p+1!=G, p-5=G}

Conclusions  Reasonable prediction rate  ‘explains’ predictions of ANN  comprehensible rules more suited to bioinformatics?

Acknowledgements BBSRC/EPSRC Dave Whitley (CMD) Tony Browne (LGU) Martyn Ford (CMD)