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Phi-LOG: a domain specific language for solving phylogenetic inference problems.

E Pontelli1, D Ranjan, B Milligan

  • 1Dept. Computer Science and Biology, New Mexico State University, NM, USA. epontell@cs.nmsu.edu

Proceedings. IEEE Computer Society Bioinformatics Conference
|April 20, 2005
PubMed
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Domain experts can now program high-level phylogenetic inference solutions using Phi-LOG, a new domain-specific language. This tool simplifies complex biological data analysis through automated infrastructure generation.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Domain experts often require high-level abstractions for complex problem-solving.
  • Phylogenetic inference is a critical but complex task in evolutionary biology.
  • Existing programming methods may not adequately support high-level reasoning for biologists.

Purpose of the Study:

  • To introduce Phi-LOG, a domain-specific language (DSL) designed for high-level programming of phylogenetic inference.
  • To enable biologists to express solutions to phylogenetic problems at an abstract level.
  • To present a software engineering framework for automatic generation of DSL implementation infrastructure.

Main Methods:

  • Design and motivation for the Phi-LOG domain-specific language.

Related Experiment Videos

  • Utilizing a software engineering framework based on Denotational Semantics and Logic Programming.
  • Automatic generation of interpreter, compiler, and debugger for the DSL.

    • Main Results:

    • Phi-LOG enables high-level programming for phylogenetic inference by biologists.
    • The implementation infrastructure for Phi-LOG is automatically generated.
    • The approach leverages Denotational Semantics and Logic Programming for robust DSL development.

    Conclusions:

    • Phi-LOG offers a powerful abstraction for biologists to tackle phylogenetic inference.
    • Automated infrastructure generation streamlines the development and deployment of specialized tools.
    • This work bridges the gap between domain expertise and computational solutions in evolutionary biology.