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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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. Extracting information

• Artificial neural networks can be trained to
  reproduce the non-linear relationships underlying
  bioinformatic data with good predictive accuracy
• but it is often hard to comprehend those
  relationships from the internal structure of the
  network
• with the result that networks are often regarded
  as black boxes.
• Decision trees using symbolic rules are easier to
  interpret
• leading to a greater likelihood of understanding
  the relationships in the data
• allowing the 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 the sequence
• H-Arg-Pro-Lys-Pro-Gln-Gln-Phe-Phe-Gly-Leu-Met-NH2
• Wang et al. used the multipin technique to
  synthesize 512 29 stereoisomers generated by
  systematic replacement of L- by D-amino acids at
  9 positions
• The aim was to measure binding potencies to NK1
  receptors 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 trees showed high fidelity with the networks
  on a 10 test set.

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8. Results

• Binding activity was determined by five
  positions, viz.
• H-Arg-Pro-Lys-Pro-Gln-Gln-Phe-Phe-Gly-Leu-Met-NH2
• The positions identified agree with the FIRM
  (Formal Inference-based Recursive Modelling)
  analysis of Young and Hawkins
• Young S Hawkins D.M. (2000) Analysis of a
  large, high-throughput screening data using
  recursive partitioning. Molecular Modelling
  Prediction of Bioactivity (ed. Gundertofte
  JØrgensen).

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9. A Typical Trepan Tree
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10. Test set confusion matrix tree versus
network
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11. Test set confusion matrix tree versus
observed
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12. Future Work

• Complete the implementation of the Trepan
  algorithm.
• model the distribution of the input data and
  generate a set of query instances to be
  classified by the network used as additional
  training cases during tree extraction.
• Extend the algorithm to enable the extraction of
  regression trees.
• Provide a Bayesian formulation for the decision
  tree extraction algorithm.

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13. Future Applications

• Apply Trepan to ligand-receptor binding problems.
• compare the performance of these algorithms with
  existing symbolic data mining techniques
  (ID3/C5).

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14. References

• Wang J-X et al. (1993) Study of
  stereo-requirements of substance P binding to NK1
  receptors using analogues with systematic D-amino
  acid replacements. Biorganic Medicinal
  Chemistry Letters, 3, 451-456.
• Young S Hawkins D.M. (2000) Analysis of a
  large, high-throughput screening data using
  recursive partitioning. Molecular Modelling
  Prediction of Bioactivity (ed. Gundertofte
  JØrgensen).

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Grantholder

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

Research Fellows
Dr Shuang Cang Mar - Sept 2000 Dr Abul Azad
Jan 2001 -
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Collaborators

• Dr Antony 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