Biological Data Mining

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Biological Data Mining

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

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People

• Centre for Molecular Design, University of
  Portsmouth
• Professor Martyn Ford
• Dr David Whitley
• Dr Shuang Cang (Mar - Sept 2000)
• Dr Abul Azad (Jan 2001 - )
• Dr Antony Browne, London Guildhall University.
• Professor Philip Picton, University College
  Northampton.

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1. Objectives

• The project aims
• to develop and 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

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2. Methods for Extracting Information

• Artificial Neural Networks
• good predictive accuracy
• hard to decipher
• often regarded as black boxes
• Decision Trees
• symbolic rules easier to interpret
• more likely to reveal relationships in the data
• allow behaviour of individual cases to be
  explained

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3. Extracting Decision Trees

• The Trepan procedure (Craven,1996) extracts
  decision trees from a neural network and a set of
  training cases by recursively partitioning the
  input space.
• The decision tree is built in a best-first
  manner, expanding the tree at nodes where there
  is greatest potential for increasing the fidelity
  of the tree to the network.

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4. Splitting Tests

• The splitting tests at the nodes are m-of-n
  expressions, e.g. 2-of-x1, x2, x3, where the
  xi are Boolean conditions.
• Start with a set of candidate tests
• binary tests on each value for nominal features
• binary tests on thresholds for real-valued
  features
• Use a beam search with a beam width of two.
• Initialize the beam with the candidate test that
  maximizes the information gain.

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5. Splitting Tests (II)

• To each m-of-n test in the beam and each
  candidate test, apply two operators
• m-of-n1 e.g. 2-of-x1, x2 gt 2-of-x1, x2, x3
• m1-of-n1 e.g. 2-of-x1, x2 gt 3-of-x1, x2,
  x3
• Admit new tests to the beam if they increase the
  information gain and are significantly different
  (chi-squared) from existing tests.

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6. Example Substance P Binding to NK1 Receptors

• Substance P is a neuropeptide with amino acid
  sequence
• H-Arg-Pro-Lys-Pro-Gln-Gln-Phe-Phe-Gly-Leu-Met-NH2
• Wang et al. (1993) used the multipin technique to
  synthesize 512 29 stereoisomers generated by
  systematic replacement of L- by D-amino acids at
  9 positions, and measured binding potencies to
  central NK1 receptors.
• The objective was to identify the positions at
  which stereo-chemistry affects binding strength.

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7. Application of Trepan

• A series of networks with 991 architectures
  were trained using 90 of the data as a training
  set.
• For each network a decision tree was grown using
  Trepan.
• The positions identified agree with the FIRM
  (Formal Inference-based Recursive Modelling)
  analysis of Young and Hawkins (1999).

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8. A Typical Trepan Tree
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9. Future Work

• Complete the implementation of the Trepan
  algorithm.
• model the distribution of the input data and
  generate from this a set of query instances that
  are classified using the network and used as
  additional training cases during extraction of
  the tree.
• 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).
• Apply Trepan to ligand-receptor binding problems.