1. Introduction

   • Jude Shavlik
   • Lawrence Hunter
   • David Searls

   EditorialNotes Pages: 5 - 9

2. Introduction

   • Jude Shavlik
   • Lawrence Hunter
   • David Searls

   Introduction Pages: 5 - 9

3. Genetic Algorithms, Operators, and DNA Fragment Assembly

   • Rebecca J. Parsons
   • Stephanie Forrest
   • Christian Burks

   OriginalPaper Pages: 11 - 33

4. Genetic algorithms, operators, and DNA fragment assembly

   • Rebecca J. Parsons
   • Stephanie Forrest
   • Christian Burks

   OriginalPaper Pages: 11 - 33

5. Discovering Dependencies via Algorithmic Mutual Information: A Case Study in DNA Sequence Comparisons

   • Aleksandar Milosavljević

   OriginalPaper Pages: 35 - 50

6. Discovering dependencies via algorithmic mutual information: A case study in DNA sequence comparisons

   • Aleksandar Milosavljević

   OriginalPaper Pages: 35 - 50

7. Unsupervised learning of multiple motifs in biopolymers using expectation maximization

   • Timothy L. Bailey
   • Charles Elkan

   OriginalPaper Pages: 51 - 80

8. Unsupervised Learning of Multiple Motifs in Biopolymers Using Expectation Maximization

   • Timothy L. Bailey
   • Charles Elkan

   OriginalPaper Pages: 51 - 80

9. DEXTER: A system that experiments with choices of training data using expert knowledge in the domain of DNA hydration

   • Dawn M. Cohen
   • Casimir Kulikowski
   • Helen Berman

   OriginalPaper Pages: 81 - 101

10. DEXTER: A System that Experiments with Choices of Training Data Using Expert Knowledge in the Domain of DNA Hydration

    • Dawn M. Cohen
    • Casimir Kulikowski
    • Helen Berman

    OriginalPaper Pages: 81 - 101

11. Use of adaptive networks to define highly predictable protein secondary-structure classes

    • Alan S. Lapedes
    • Evan W. Steeg
    • Robert M. Farber

    OriginalPaper Pages: 103 - 124

12. Use of Adaptive Networks to Define Highly Predictable Protein Secondary–Structure Classes

    • Alan S. Lapedes
    • Evan W. Steeg
    • Robert M. Farber

    OriginalPaper Pages: 103 - 124

13. Machine discovery of protein motifs

    • Darrell Conklin

    OriginalPaper Pages: 125 - 150

14. Machine Discovery of Protein Motifs

    • Darrell Conklin

    OriginalPaper Pages: 125 - 150

15. Searching for Representations to Improve Protein Sequence Fold-Class Prediction

    • Thomas R. Ioerger
    • Larry A. Rendell
    • Shankar Subramaniam

    OriginalPaper Pages: 151 - 175

16. Searching for representations to improve protein sequence fold-class prediction

    • Thomas R. Ioerger
    • Larry A. Rendell
    • Shankar Subramaniam

    OriginalPaper Pages: 151 - 175

17. Neural networks for full-scale protein sequence classification: Sequence encoding with singular value decomposition

    • Cathy Wu
    • Michael Berry
    • Jerry McLarty

    OriginalPaper Pages: 177 - 193

18. Neural Networks for Full-Scale Protein Sequence Classification: Sequence Encoding with Singular Value Decomposition

    • Cathy Wu
    • Michael Berry
    • Jerry McLarty

    OriginalPaper Pages: 177 - 193