Biological Data Mining

1
Biological Data Mining

• A comparison of Neural Network and Symbolic
  Techniques
• http//www.cmd.port.ac.uk/biomine/

2
Grantholder

• Professor Martyn Ford
• Centre for Molecular Design
• University of Portsmouth
• martyn.ford_at_port.ac.uk

3
Collaborators

• Dr Anthony Browne
  School of Computing, Information
  Systems and Mathematics, London Guildhall
  University. abrowne_at_lgu.ac.uk
• Professor Philip Picton
  School of Technology and
  Design, University College Northampton.
  phil.picton_at_northampton.ac.uk
• Dr David Whitley
  Centre for Molecular Design,
  University of Portsmouth.
  david.whitley_at_port.ac.uk

4
Objectives

• The project aims
• to develop validate techniques for extracting
  explicit information from bioinformatic data
• to express this information as logical rules and
  decision trees
• to apply these new procedures to a range of
  scientific problems related to bioinformatics and
  cheminformatics

5
Extracting information

• Artificial neural networks (ANNs) can be used to
  identify the non-linear relationships that
  underlie bioinformatic data, but . . .
• trained ANNs do not lead to a concise and
  explicit model
• specifying the underlying structure is therefore
  difficult
• as a result, ANNs are often regarded as black
  boxes

6
Data Mining and Neural Networks

• Standard data mining algorithms exist (such as
  ID3 or C5) so why use an ANN? It would be
  advantageous if the rules extracted
• Give a better fit to the data with the same
  number of rules (i.e. explain the data more
  accurately)
• Give the same fit to the data with less rules
  (i.e. explain the data more comprehensibly) or
• Give both a better fit to the data and use less
  rules (i.e. explain the data more comprehensibly
  and more accurately).

7
Extracting Decision Trees

• The TREPAN procedure (Craven,1996)
• extracts decision trees from ANNs
• performs better than the symbolic learning
  algorithms ID3 and C5
• the current implementation is restricted to a
  particular network architecture, but
• the underlying algorithm is independent of
  network architecture

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Trepan

• Builds a decision tree representing the function
  the ANN has learnt by recursively partitioning
  the input space.
• Draws query instances by taking into account the
  distribution of instances in the problem domain.
• For real-valued features uses kernel density
  estimates to generate a model of the underlying
  data that is used to select instances for
  presentation to the network.

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Trepan

• Builds the decision tree in a best-first manner
• as each node is added the fidelity of the
  decision tree to the ANN is maximised
• this is done by examining the significance of the
  distributions at consecutive levels of the tree
  (Kolmogorov-Smirnoff test for real valued
  features, chi-squared for discrete ones)
• Allows the user to control the size of the final
  tree by selecting appropriate stopping criteria.

10
Aims

• Implement the TREPAN algorithm in a portable
  format, independent of network architecture.
• Extend the algorithm to enable the extraction of
  regression trees.
• Provide a Bayesian formulation for the decision
  tree extraction algorithm.
• Compare the performance of these algorithms with
  existing symbolic data mining techniques (ID3/C5).

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Aims

• Apply the extracted decision trees
• to searches of bioinformatic databases
• protein databases
• genomic databases
• to searches of cheminformatic databases
• chemical libraries
• natural product databases
• to investigate ligand/receptor binding
• to quantify molecular similarity/diversity
• to identify new leads and optimise properties

12
Case study ligand interaction with GPCRs

• 28 GPCRs
• a number of putative interaction sites
• 3 principal properties of amino acids (AAs)
• MLR results for 2 ligands