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Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case
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Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case

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In this talk we demonstrate an ECGA and LCS pipeline for reducing protein alphabets from the standard 20 to 5 or less symbols without significant loss of information. The pipeline tailors the …

In this talk we demonstrate an ECGA and LCS pipeline for reducing protein alphabets from the standard 20 to 5 or less symbols without significant loss of information. The pipeline tailors the reduction to different problems thus resulting on different optimal minimal alphabets.

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  • 1. Extended Compact Genetic Algorithms and Learning Classifier Systems for Dimensionality Reduction: a Protein Alphabet Reduction Study Case Jaume Bacardit & Natalio Krasnogor ASAP - Interdisciplinary Optimisation Laboratory School of Computer Science Centre for Integrative Systems Biology School of Biology Centre for Healthcare Associated Infections Institute of Infection, Immunity & Inflammation University of Nottingham Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 1 /73 Tuesday, 30 June 2009
  • 2. Acknowledgements (in no particular order) (in no particular order)  Peter Siepmann Contributors to the talks I will give at BGU  Pawel Widera  School of Physics and Astronomy  James Smaldon  School of Chemistry  School of Pharmacy  Azhar Ali Shah  School of Biosciences  Jack Chaplin  School of Mathematics  Enrico Glaab  School of Computer Science  German Terrazas  Centre for Biomolecular Sciences  Hongqing Cao  all the above at UoN  Jamie Twycross  Jonathan Blake Thanks also go to:  Francisco Romero-Campero  Maria Franco Ben Gurion University of the  Adam Sweetman  Linda Fiaschi Negev’s Distinguished Scientists Visitor Program Funding From: BBSRC, EPSRC, EU, ESF, UoN Professor Dr. Moshe Sipper Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 2 /73 Tuesday, 30 June 2009
  • 3. Outline  Introduction to Learning Classifier Systems and Extended Compact GA  Problem Definition  Methods (ECGA, LCS, Mutual Information)  Results  Conclusions and further work Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 3 /73 Tuesday, 30 June 2009
  • 4. Based on Various Papers  J.Bacardit, M.Stout, J.D. Hirst, A.Valencia, R.E.Smith, and N.Krasnogor. Automated alphabet reduction for protein datasets. BMC Bioinformatics, 10(6), 2009.  J. Bacardit, M. Stout, J.D. Hirst, K. Sastry, X. Llora, and N. Krasnogor. Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. In Proceedings of the 2007 Genetic and Evolutionary Computation Conference, number ISBN 978-1-59593-697-4, pages 346-353. ACM Press, 2007. This paper won the Bronze Medal in the THE 2007 “HUMIES” AWARDS FOR HUMAN-COMPETITIVE RESULTS PRODUCED BY GENETIC AND EVOLUTIONARY COMPUTATION. J.  J.Bacardit and N. Krasnogor. Performance and efficiency of memetic pittsburgh learning classifier systems. Evolutionary Computation, 17(3):(to appear), 2009.  J.Bacardit, E.K. Burke, and N.Krasnogor. Improving the scalability of rule-based evolutionary learning. Memetic Computing, 1(1):(to appear), 2009  J. Bacardit, M. Stout, and N. Krasnogor. A tale of human-competiveness in bioinformatics. Newsletter of ACM Special Interest Group on Genetic and Evolutionary Computation: SIGEvolution, 3(1):2-10, 2008. All papers available from: www.cs.nott.ac.uk/~nxk/publications.html Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 4 /73 Tuesday, 30 June 2009
  • 5.  Learning Classifier Systems (LCS) are one of the major families of techniques that apply evolutionary computation to machine learning tasks  Machine learning: How to construct programs that automatically improve with experience [Mitchell, 1997]  Classification task: Learning how to label correctly new instances from a domain based on a set of previously labeled instances  LCS are almost as ancient as GAs, Holland made one of the first proposals  Two of the first LCS proposals are [Holland & Reitman, 78] and [Smith, 80] Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 5 /73 Tuesday, 30 June 2009
  • 6.  Traditionally there have been two different paradigms of LCS  The Pittsburgh approach [Smith, 80]  The Michigan approach [Holland & Reitman, 78]  More recently: The Iterative Rule Learning approach [Venturini, 93]  Knowledge representations  All the initial approaches were rule-based  In recent years several knowledge representations have been used in the LCS field: decision trees, synthetic prototypes, etc. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 6 /73 Tuesday, 30 June 2009
  • 7. Classification task  Classification task: Learning how to label correctly new instances from a domain based on a set of previously labeled instances New Instance Learning Inference Training Set Algorithm Engine Class Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 7 /73 Tuesday, 30 June 2009
  • 8. Classification task 1 If (X<0.25 and Y>0.75) or (X>0.75 and Y Y<0.25) then  0 1 X Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 8 /73 Tuesday, 30 June 2009
  • 9. Paradigms of LCS  The Pittsburgh approach  Each individual is a complete solution to the classification problem  Traditionally this means that each individual is a variable-length set of rules  GABIL [De Jong & Spears, 93] is a well- known representative of this approach  Fitness function is based on the rule set accuracy on the training set (usually also on complexity) Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 9 /73 Tuesday, 30 June 2009
  • 10. Paradigms of LCS  The Pittsburgh approach  Crossover operator Parents Offspring  Mutation operator: bit flipping  Individuals are interpreted as a decision list: an ordered rule set Instance 1 matches rules 2, 3 and 7  Rule 2 will be used Instance 2 matches rules 1 and 8  Rule 1 will be used 1 2 3 4 5 6 7 8 Instance 3 matches rule 8  Rule 8 will be used Instance 4 matches no rules  Instance 4 will not be classified Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 10 /73 Tuesday, 30 June 2009
  • 11. Paradigms of LCS  The Michigan approach  Each individual is a single rule  The whole population cooperates to solve the classification problem  A reinforcement system is used to identify the good rules  A GA is used to explore the search space for more rules  XCS [Wilson, 95] is the most well-known Michigan LCS Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 11 /73 Tuesday, 30 June 2009
  • 12. Paradigms of LCS  Working cycle Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 12 /73 Tuesday, 30 June 2009
  • 13. Paradigms of LCS  The Iterative Rule Learning approach  Each individual is a single rule  Individuals compete as in a standard GA  A single GA run generates one rule  The GA is run iteratively to learn all rules that solve the problem  Instances already covered by previous rules are removed from the training set of the next iteration Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 13 /73 Tuesday, 30 June 2009
  • 14. Paradigms of LCS  The Iterative Rule Learning approach  HIDER System [Aguilar, Riquelme & Toro, 03] 1. Input: Examples 2. RuleSet = Ø 3. While |Examples| > 0 1. Rule = Run GA with Examples 2. RuleSet = RuleSet U Rule 3. Examples = Examples Covered(Rule) 4. EndWhile 5. Output: RuleSet Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 14 /73 Tuesday, 30 June 2009
  • 15. Bioinformatics-oriented Hierarchical Evolutionary Learning (BioHel)  BioHEL [Bacardit et al., 07] is a recent learning system that applies the Iterative Rule Learning (IRL) approach to generate sets of rules  IRL was first used in EC by the SIA system [Venturini, 93]  BioHEL is strongly inspired by GAssist [Bacardit, 04], a Pittsburgh approach Learning Classifier System Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 15 /73 Tuesday, 30 June 2009
  • 16.  BioHEL learning paradigm  IRL has been used for many years in the ML community, with the name of separate-and-conquer Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 16 /73 Tuesday, 30 June 2009
  • 17. BioHEL’s objective function  An objective function based on the Minimum- Description-Length (MDL) (Rissanen,1978) principle that tries to promote rules with  High accuracy: not making mistakes  High coverage: covering as many examples as possible without sacrificing accuracy. Recall (TP/(TP+FN)) will be used to define coverage  Low complexity: rules as simple and general as possible  The objective function is a linear combination of the three objectives above Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 17 /73 Tuesday, 30 June 2009
  • 18. BioHEL’s objective function  Intuitively, we would like to have accurate rules covering as many examples as possible.  However, in complex and inconsistent domains it is rare to obtain such rules  In these cases, easier path for evolutionary search is to maximize accuracy at the expense of coverage  Therefore, we need to enforce that the evolved rules cover enough examples Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 18 /73 Tuesday, 30 June 2009
  • 19. Methods: BioHEL’s objective function  Three parameters define the shape of the function  The choice of the coverage break is crucial for the proper performance of the system  Also, coverage term penalizes rules that do not cover a minimum percentage of examples or that cover too many Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 19 /73 Tuesday, 30 June 2009
  • 20. BioHEL’s other characteristics  Attribute list rule representation  Automatically identifying the relevant attributes for a given rule and discarding all the other ones  The ILAS windowing scheme  Efficiency enhancement method, not all training points are used for each fitness computation  An explicit default rule mechanism  Generating more compact rule sets  Iterative process terminates when it is impossible to evolve a rule where the associated class is the majority class among the matched examples  At this point, all remaining training instances are assigned to the default class  Ensembles for consensus prediction  Easy way of boosting robustness Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 20 /73 Tuesday, 30 June 2009
  • 21. Knowledge representations  Representation of XCS for binary problems: ternary representation  Ternary alphabet {0,1,#}  If A1=0 and A2=1 and A3 is irrelevant  class 0  01#|0  Representation of XCS for real-valued attributes: real-valued interval.  XCSR [Wilson, 99]  Interval is codified with two variables: center & spread: [center- spread, center+spread]  UBR [ Stone & Bull, 03]  The two bounds of the interval are codified directly with two real- valued variables. The variable with lowest value is the lower bound, the variable with higher value is the upper bound Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 21 /73 Tuesday, 30 June 2009
  • 22. Knowledge representations  Representation of GABIL for nominal attributes  Predicate → Class  Predicate: Conjunctive Normal Form (CNF) (A1=V11∨.. ∨ A1=V1n) ∧.....∧ (An=Vn2∨.. ∨ An=Vnm)  Ai : ith attribute  Vij : jth value of the ith attribute  The rules can be mapped into a binary string, e.g., 3 attributes with {3,5,2} values each respectively:  (A1=V11∨ A1=V13) ∧ (A2=V22 ∨ A2=V24 ∨ A2=V25) ∧ (A3=V31) 101|01011|10 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 22 /73 Tuesday, 30 June 2009
  • 23. Knowledge representations  Pittsburgh representations for real-valued attributes:  Rule-based: Adaptive Discretization Intervals (ADI) representation [Bacardit, 04]  Intervals in ADI are build using as possible bounds the cut-points proposed by a discretization algorithm  Search bias promotes maximally general intervals  Several discretization algorithms are used at the same time to reduce bias Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 23 /73 Tuesday, 30 June 2009
  • 24. Knowledge representations  Pittsburgh representations for real-valued attributes:  Decision trees [Llorà, 02]  Nodes in the trees can use orthogonal or oblique criteria Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 24 /73 Tuesday, 30 June 2009
  • 25. Knowledge representations  Pittsburgh representations for real-valued attributes  Synthetic prototypes [Llorà, 02]  Each individual is a set of synthetic instances  These instances are used as the core of a nearest-neighbor classifier ? Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 25 /73 Tuesday, 30 June 2009
  • 26. Extended Compact Genetic Algorithm (ECGA)  ECGA belongs to a class of Evolutionary Algorithms called Estimation of Distribution Algorithms (EDA)  no crossover or mutation!  instead a probabilistic model of the structure of the problem is kept  individuals are sampled from this probability distribution model Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 26 /73 Tuesday, 30 June 2009
  • 27. Taken from: Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) by Georges R. Harik, Fernando G. Lobo and Kumara Sastry. Studies in Computational Intelligence, Volume 33/2006, Springer, 2007. Text Text Key Idea Behind Compact GA (CGA) Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 27 /73 Tuesday, 30 June 2009
  • 28. Taken from: Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) by Georges R. Harik, Fernando G. Lobo and Kumara Sastry. Studies in Computational Intelligence, Volume 33/2006, Springer, 2007.  Genes Interactions must be accounted for  Approximates complex distributions by Marginal Distribution Models (i.e. genes partitions)  Selects amongst alternative models by means of the MDL: Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 28 /73 Tuesday, 30 June 2009
  • 29. Taken from: Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA) by Georges R. Harik, Fernando G. Lobo and Kumara Sastry. Studies in Computational Intelligence, Volume 33/2006, Springer, 2007. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 29 /73 Tuesday, 30 June 2009
  • 30. Outline  Introduction to Learning Classifier Systems and Extended Compact GA  Problem Definition  Methods (ECGA, LCS, Mutual Information)  Results  Conclusions and further work Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 30 /73 Tuesday, 30 June 2009
  • 31. Protein Structure Prediction (PSP) has as goal to predict the 3D structure of a protein based on its primary sequence Primary Sequence 3D Structure Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 31 /73 Tuesday, 30 June 2009
  • 32.  PSP is a very costly process  As an example, one of the best PSP methods in the last CASP meeting, Rosetta@Home used up to 104 computing years to predict a single protein’s 3D structure  Ways to alleviate computational burden:  to simplify the problem  to simplify the representation used to model the proteins Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 32 /73 Tuesday, 30 June 2009
  • 33. From Full PSP to CN prediction  Two residues of a chain are said to be in contact if their distance is less than a certain threshold Primary Contact Native State Sequence  CN of a residue : number of contacts that a certain residue has  In this specific case we predict, e.g., whether the CN of a residue is smaller or higher than the middle point of the CN domain Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 33 /73 Tuesday, 30 June 2009
  • 34. From Full PSP to SA prediction  Solvent Accessibility: Amount of surface of each residue that is exposed to the solvent (e.g. water)  Metric is normalised for each AA type  Problem is to predict whether SA is lower or higher than 25% Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 34 /73 Tuesday, 30 June 2009
  • 35.  PSP is a very costly process  As an example, one of the best PSP methods in the last CASP meeting, Rosetta@Home used up to 104 computing years to predict a single protein’s 3D structure  Ways to alleviate computational burden:  to simplify the problem  to simplify the representation used to model the proteins Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 35 /73 Tuesday, 30 June 2009
  • 36.  Primary Sequence of a protein (the amino acid type of the elements of a protein chain) is an usual target for such simplification  It is composed of a quite high cardinality alphabet of 20 symbols  One example of reduction widely used in the community is the hydrophobic-polar (HP) alphabet, reducing these 20 symbols to just two  HP representation usually is too simple, information is lost in the reduction process  M. Stout, et al. Prediction of residue exposure and contact number for simplified hp lattice model proteins using learning classifier systems. In Proceedings of the 7th International FLINS Conference on Applied Artificial Intelligence, pages 601-608. World Scientific, August 2006.  M. Stout, J. Bacardit, J.D. Hirst, N. Krasnogor, and J. Blazewicz. From hp lattice models to real proteins: coordination number prediction using learning classifier systems. In 4th European Workshop on Evolutionary Computation and Machine Learning in Bioinformatics, volume 3907 of Springer Lecture Notes in Computer Science, page 208–220, Budapest, Hungary, April 2006. Springer. ISBN 978-3-540-33237-4.  papers at: http://www.cs.nott.ac.uk/~nxk/publications.html Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 36 /73 Tuesday, 30 June 2009
  • 37.  Research Question:  Are there “simplified” alphabets that retain key information content while simplifying interpretation,processing time, etc?  If yes, are these alphabet general for any problem domain or domain specific?  Can we automatically generate these alphabets and tailor them to the specific domain we are predicting? Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 37 /73 Tuesday, 30 June 2009
  • 38. Outline  Introduction to Learning Classifier Systems and Extended Compact GA  Problem Definition  Methods (ECGA, LCS, Mutual Information)  Results  Conclusions and further work Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 38 /73 Tuesday, 30 June 2009
  • 39.  Use an (automated) information theory-driven pipeline to reduce alphabet for PSP datasets  Use the Extended Compact Genetic Algorithm (ECGA) to find a dimensionality reduction policy (guided by a fitness function based on the Mutual Information (MI) metric)  Two PSP datasets will be used as testbed:  Coordination Number (CN) prediction  Relative Solvent Accessibility (SA) prediction  Verify the optimized reduction policies with BioHEL, an evolutionary-computation based rule learning system J.Bacardit, M.Stout, J.D. Hirst, A.Valencia, R.E.Smith, and N.Krasnogor. Automated alphabet reduction for protein datasets. BMC Bioinformatics, 10(6), 2009. J. Bacardit, M. Stout, and N. Krasnogor. A tale of human-competiveness in bioinformatics. Newsletter of ACM Special Interest Group on Genetic and Evolutionary Computation: SIGEvolution, 3(1):2-10, 2008. J. Bacardit, M. Stout, J.D. Hirst, K. Sastry, X. Llora, and N. Krasnogor. Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. In Proceedings of the 2007 Genetic and Evolutionary Computation Conference, number ISBN 978-1-59593-697-4, pages 346-353. ACM Press, 2007. All papers at: http://www.cs.nott.ac.uk/~nxk/publications.html Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 39 /73 Tuesday, 30 June 2009
  • 40.  Protein dataset proposed by [Kinjo et al., 05]  1050 proteins  259768 residues  Proteins were selected from PDB-REPRDB using these conditions:  Less than 30% sequence identity  More than 50 residues  Resolution better than 2Å  No membrane proteins, no chain breaks, no non- standard residues  Crystallographic R-factor better than 20%  Dataset is partitioned into training/test sets using ten- fold cross-validation Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 40 /73 Tuesday, 30 June 2009
  • 41. Instance Representation AAi-5 AAi-4 AAi-3 AAi-2 AAi-1 AAi AAi+1 AAi+2 AAi+3 AAi+4 AAi+5 CNi-5 CNi-4 CNi-3 CNi-2 CNi-1 CNi CNi+1 CNi+2 CNi+3 CNi+4 CNi+5 AAi-1,AAi,AAi+1  CNi AAi,AAi+1,AAi+2  CNi+1 AAi+1,AAi+2,AAi+3  CNi+2 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 41 /73 Tuesday, 30 June 2009
  • 42. Taken from: J. Bacardit, M. Stout, J.D. Hirst, K. Sastry, X. Llora, and N. Krasnogor. Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. In Proceedings of the 2007 Genetic and Evolutionary Computation Conference, number ISBN 978-1-59593-697-4, pages 346-353. ACM Press, 2007. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 42 /73 Tuesday, 30 June 2009
  • 43. General Workflow of the Alphabet Reduction Pipeline Size = N Test set Dataset ECGA Dataset BioHEL Ensemble |∑|=20 |∑|=N of rule sets Accuracy Mutual Information Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 43 /73 Tuesday, 30 June 2009
  • 44. Methods: alphabet reduction strategies  Three strategies were evaluated  They represent progressive levels of sophistication  Mutual Information (MI)  Robust Mutual Information (RMI)  Dual Robust Mutual Information (DualRMI)  Thus MI, RMI, DualRMI were used in separate experiments as the “fitness” function for the ECGA tournament phase. Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 44 /73 Tuesday, 30 June 2009
  • 45. Methods: MI strategy  There are 21 symbols (20AA+end of chain) in the alphabet  Each symbol will be assigned to a group in the chromosome used by ECGA Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 45 /73 Tuesday, 30 June 2009
  • 46. Methods: MI stragegy  Objective function for MI strategy: Mutual Information  Mutual Information is a measure that quantifies the interrelationship that two discrete variables have among each other  X is the reduced representation of the window of residues around the target.  Y is the two-state definition fo CN or SA Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 46 /73 Tuesday, 30 June 2009
  • 47. Methods: MI strategy  Steps of objective function computation for the MI strategy 1. Reduction mappings are extracted from the chromosome 2. Instances of the training set are transformed into the lower cardinality alphabet 3. Mutual information between the class attribute and the string formed by concatenating the input attributes is computed 4. This MI is assigned as the result of the evaluation function Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 47 /73 Tuesday, 30 June 2009
  • 48. Methods: MI strategy  Problem of MI strategy  Mutual Information needs redundancy in order to become a good estimator  That is, each possible pattern in X and Y should be well represented in the dataset  Patterns in Y are always well represented. What happens with patterns in X in our dataset?  Our sample, despite having almost 260000 residues is too small #letters Represented patterns 2 100% 3 97.8% 4 57.6% 5 11.3% 20 3.1E-07 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 48 /73 Tuesday, 30 June 2009
  • 49. Methods: RMI strategy  In order to solve the sample size problem of the MI strategy, we use a robust MI estimator proposed by [Cline et al., 02]  Pairs of (x,y) in the dataset are scrambled  That is, each x in the dataset is randomly joined to an y but the distribution of x and y remains equal  MI is computed for the scrambled dataset  This process is repeated N time, and the average scrambled MI is computed  Finally, the value for the objective function is MI – Mis  Mis is an estimation of the sampling bias in the data. By subtracting it from the original MI metric we obtain a less biased objective function Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 49 /73 Tuesday, 30 June 2009
  • 50. Methods: DualRMI strategy  The next strategy is based on some observations we did in previous work [Bacardit et al., 06]  Example of a rule set for prediction CN from primary sequence  Predicate associated to the target residue (AA) is very different from the predicates associated to the other window positions Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 50 /73 Tuesday, 30 June 2009
  • 51. Methods: DualRMI strategy  Why not generating two reduced alphabets at the same time?  One for the target residue  One for the other residues in the window  Objective function remains unchanged Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 51 /73 Tuesday, 30 June 2009
  • 52. Outline  Introduction to Learning Classifier Systems and Extended Compact GA  Problem Definition  Methods (ECGA, LCS, Mutual Information)  Results  Conclusions and further work Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 52 /73 Tuesday, 30 June 2009
  • 53. Experimental design  For each problem (CN, SA)  For each reduction strategy (MI, RMI, DualRMI)  ECGA was run to generate alphabets of two, three, four and five letters  Afterwards, BioHEL was trained over the reduced datasets to determine the prediction accuracy that could be obtained from each alphabet size  Comparisons are drawn Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 53 /73 Tuesday, 30 June 2009
  • 54. Reduced alphabets for CN Amino acids that remain always in the same group are marked with solid rectangles Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 54 /73 Tuesday, 30 June 2009
  • 55. Alphabets for CN  The two-letter alphabet divides the amino-acids between hydrophobic and polar  RMI could not find a five-letter alphabet  DualRMI did, but only for the target residue  RMI and DualRMI have a much larger number of framed residues, showing more robustness  For DualRMI we can observe small groups of hydrophobic residues, while all polar ones are in the same group  We can also observe a strange group, GHTS, that mixes different kind of physico-chemical properties  Not explained by properties but by inherent distribution in datasets Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 55 /73 Tuesday, 30 June 2009
  • 56.  A retrospective analysis of the dataset reveals why GHTS are clustered together  We computed the proportion of residues for each amino acid type with high CN  These four residues have very similar average behavior in relation to CN Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 56 /73 Tuesday, 30 June 2009
  • 57. Accuracy of CN prediction Using Biohel  Accuracy difference between the AA representation and the best reduced alphabets is 0.7%  Difference in non- significant according to t-tests  RMI and DualRMI perform similarly Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 57 /73 Tuesday, 30 June 2009
  • 58. Reduced alphabets for SA Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 58 /73 Tuesday, 30 June 2009
  • 59. Reduced alphabets for SA  Even though SA and CN are somewhat related structural features, the resulting alphabets are different  These alphabets contain more groups of polar residues, and less groups of hydrophobic ones (in contrast with CN)  In DualRMI and 5 letters we can observe very small groups  A, EK for the target alphabet  G,X for the other residues alphabet  Again, the GHTS group appears Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 59 /73 Tuesday, 30 June 2009
  • 60.  Analysis of average SA behavior for each AA type  The reduced alphabet matched perfectly the properties of the SA features Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 60 /73 Tuesday, 30 June 2009
  • 61. Accuracy of SA prediction with BioHel  Accuracy of reduced alphabets for SA prediction  Only DualRMI managed to give a performace statistically similar to the original AA representation  Accuracy difference is 0.4% Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 61 /73 Tuesday, 30 June 2009
  • 62. Comparison to Other Reduced Alphabets from the Literature and Expert-Designed Alphabets Based on Physico-Chemical Properties Alphabet Letters CN acc. SA acc. Diff. Ref. AA 20 74.0±0.6 70.7±0.4 --- --- DualRMI 5 73.3±0.5 70.3±0.4 0.7/0.4 This work WW5 6 73.1±0.7 69.6±0.4 0.9/1.1 [Wang & Wang, 99] Alphabets from the literature SR5 6 73.1±0.7 69.6±0.4 0.9/1.1 [Solis & Rackovsky, 00] MU4 5 72.6±0.7 69.4±0.4 1.4/1.3 [Murphy et al., 00] MM5 6 73.1±0.6 69.3±0.3 0.9/1.4 [Melo & Marti-Renom, 06] Expert designed HD1 7 72.9±0.6 69.3±0.4 1.1/1.4 This work alphabets HD2 9 73.0±0.6 69.3±0.4 1.0/1.4 This work HD3 11 73.2±0.6 69.9±0.4 0.8/0.8 This work Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 62 /73 Tuesday, 30 June 2009
  • 63. Reduced Alphabets Comparison  Automatically reduced alphabets obtain better accuracy, but how different are the alphabets themselves?  We applied again the AA-wise high CN/SA analysis  Two metrics were computed  Transitions: how many times does the group index change through the list of sorted AA.  The less number of changes, the more homogenous the groups are  Average range: The range of a reduction group is the difference between the minimum and maximum CN/SA of the AAs belonging to that group  The smaller the average range, the more focused the reduction groups are in relation to that structural property Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 63 /73 Tuesday, 30 June 2009
  • 64. Reduced Alphabets Comparison (CN) Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 64 /73 Tuesday, 30 June 2009
  • 65. Reduced Alphabets Comparison (SA) Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 65 /73 Tuesday, 30 June 2009
  • 66. Additional Results  Are the alphabets interchangeable across problems?  Can these reduced alphabets be applied to an evolutionary information-based representation? Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 66 /73 Tuesday, 30 June 2009
  • 67. Results: Are the alphabets interchangeable?  We applied the alphabet optimized for CN to SA and vice versa  SA alphabet is good for predicting CN, but CN alphabet obtains poor performance on SA  Reduced alphabets must always be tailored to the domain at hand Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 67 /73 Tuesday, 30 June 2009
  • 68. Results  Application of the reduced alphabets to an evolutionary information-based representation  So far we have used only the simple primary sequence representation  Can this process be applied to much richer (and complex) representations?  We computed the position-specific scoring matrices (PSSM) representation of our dataset using PSI-BLAST. Each instance (9 window positions) is represented by 180 continuous variables (rather than 20+1 as originally done)  Then, we reduced this representation using our alphabets  The values of each PSSM profile corresponding to amino acids in the same reduction group are averaged Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 68 /73 Tuesday, 30 June 2009
  • 69. Results  Application of reduced alphabets to a PSSM representation  Thus, we reduced the representation from 180 attributes to 45 Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 69 /73 Tuesday, 30 June 2009
  • 70. Results  Results of learning from the reduced PSSM representation  Accuracy difference is still less than 1%  Obtained rules sets are simpler and training process is much faster  Performance levels are similar to recent works in the literature [Kinjo et al., 05][Dor and Zhou, 07] Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 70 /73 Tuesday, 30 June 2009
  • 71. Conclusions  We have proposed an automated alphabet reduction protocol for protein datasets  Protocol does not use any domain knowledge  It automatically tailors the reduced datasets to the domain at hand  Our experiments show that it is possible to obtain quite reduced alphabets (5 letters) with similar performance than the original AA alphabet  Our reduced alphabets are better at CN and SA prediction than other alphabet from the literature, as they are better suited for these tasks  The findings from the protocol can be used in state-of-the-art protein representations as PSSM profiles  We found some unexpected reduction groups (GHTS) but the properties of the data showed us that this is not an artifact Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 71 /73 Tuesday, 30 June 2009
  • 72. Future work  Explore alternative objective evaluation functions  Other robust MI estimation  Explore slightly higher cardinality alphabets  Is it possible to close the accuracy gap even more?  Apply this protocol to other kind of datasets  E.g. protein mutations  Structural aspects defined as continuous variables, not just discrete ones Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 72 /73 Tuesday, 30 June 2009
  • 73. Acknowledgements (in no particular order) (in no particular order)  Peter Siepmann Contributors to the talks I will give at BGU  Pawel Widera  School of Physics and Astronomy  James Smaldon  School of Chemistry  School of Pharmacy  Azhar Ali Shah  School of Biosciences  Jack Chaplin  School of Mathematics  Enrico Glaab  School of Computer Science  German Terrazas  Centre for Biomolecular Sciences  Hongqing Cao  all the above at UoN  Jamie Twycross  Jonathan Blake Thanks also go to:  Francisco Romero-Campero  Maria Franco Ben Gurion University of the  Adam Sweetman  Linda Fiaschi Negev’s Distinguished Scientists Visitor Program Funding From: BBSRC, EPSRC, EU, ESF, UoN Professor Dr. Moshe Sipper Ben-Gurion University of the Negev - June 23rd to July 5th 2009 - Distinguished Scientist Visitor Program - Beer Sheva, Israel 73 /73 Tuesday, 30 June 2009

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