Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms Article Swipe

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Samuel C. Stanton , Robert Alberstein , Nathan C. Frey , Andrew M. Watkins , Kyunghyun Cho ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2407.00236

There is a growing body of work seeking to replicate the success of machine learning (ML) on domains like computer vision (CV) and natural language processing (NLP) to applications involving biophysical data. One of the key ingredients of prior successes in CV and NLP was the broad acceptance of difficult benchmarks that distilled key subproblems into approachable tasks that any junior researcher could investigate, but good benchmarks for biophysical domains are rare. This scarcity is partially due to a narrow focus on benchmarks which simulate biophysical data; we propose instead to carefully abstract biophysical problems into simpler ones with key geometric similarities. In particular we propose a new class of closed-form test functions for biophysical sequence optimization, which we call Ehrlich functions. We provide empirical results demonstrating these functions are interesting objects of study and can be non-trivial to solve with a standard genetic optimization baseline.

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Sequence (biology) Algorithm Test (biology) Computer science Mathematics Geology Biology Genetics Paleontology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2407.00236
PDF: https://arxiv.org/pdf/2407.00236
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4400267221

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DOI: https://doi.org/10.48550/arxiv.2407.00236

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Title: Closed-Form Test Functions for Biophysical Sequence Optimization Algorithms

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-06-28

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Authors: Samuel C. Stanton, Robert Alberstein, Nathan C. Frey, Andrew M. Watkins, Kyunghyun Cho

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Landing page: https://arxiv.org/abs/2407.00236

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PDF URL: https://arxiv.org/pdf/2407.00236

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Concepts: Sequence (biology), Algorithm, Test (biology), Computer science, Mathematics, Geology, Biology, Genetics, Paleontology

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