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Functional programming offers expressive ways to code complex math, but faces adoption hurdles. This work provides accessible examples and a code repository to aid scientists in learning functional programming for machine learning and bioinformatics.

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Area of Science:

  • Computational Science
  • Computer Science
  • Bioinformatics
  • Machine Learning

Background:

  • Scientists require effective methods for translating complex mathematical concepts into code.
  • Functional programming languages (e.g., LISP, Haskell) excel at expressing intricate data operations and mathematical constructs.
  • Despite theoretical advantages, functional programming adoption in science is limited by a steep learning curve and scarce resources.

Purpose of the Study:

  • To bridge the gap in functional programming learning resources for scientists.
  • To demonstrate applied, scientifically relevant examples of functional programming techniques.
  • To encourage the adoption and learning of functional programming within the scientific community.

Main Methods:

  • Development of a multi-language source-code repository (CONNJUR-Sandbox).
  • Inclusion of scientifically substantial examples focusing on machine learning, data processing, and bioinformatics.
  • Provision of reusable code to facilitate software integration and algorithm development.

Main Results:

  • A publicly available code repository offering practical functional programming examples.
  • Demonstration of functional programming's applicability in key scientific domains.
  • Creation of a learning resource to lower the barrier to entry for scientists.

Conclusions:

  • Functional programming can be a powerful tool for scientific computation when made accessible.
  • The provided repository serves as a valuable resource for scientists learning functional programming.
  • Encouraging reuse and learning from these examples can accelerate functional programming adoption in science.