Hybridization and ring optimization for larger sets of embeddable biomarkers

Side Effect Machines (SEMs) are an extension of finite state machines which place a counter on each node that is incremented when that node is visited. Previous studies examined a genetic algorithm to discover node connections in SEMs for edit metric decoding for biological applications, namely to handle sequencing errors. Edit metric codes, while useful for decoding such biologically created errors, have a structure which significantly differentiates them from other codes based on Hamming distance. Further, the inclusion of biologicallymotivated restrictions on allowed words makes development of decoders a bespoke process based on the exact code used. This study examines the use of evolutionary programming for the creation of such decoders, thus allowing for the number of states to be evolved directly, not witnessed in previous approaches which used genetic algorithms. Both direct and fuzzy decoding are used, obtaining correct decoding rates of up to 95% in some SEMs.

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“…Techniques include exhaustive search [15] along with various computational intelligence techniques (e.g. [3], [2]).…”

Section: A Backgroundmentioning

confidence: 99%

Edit metric decoding: Return of the side effect machines

Houghten

Collins

Hughes

et al. 2018

2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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“…In [3] edit metric codes were created for use as embeddable markers for cDNA libraries. Several later studies [2], [4], [5], [24] used a variety of approaches, including evolutionary algorithms, other metaheuristics, and combinatorial searches to create codes useful for biological applications.…”

Section: Related Workmentioning

confidence: 99%

Effective Side Effect Machines for Decoding

Banik

Houghten

2020

2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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The development of general edit metric decoders is a challenging problem, especially with the inclusion of additional biological restrictions that can occur when using error correcting codes in biological applications. Side effect machines (SEMs), an extension of finite state machines, can provide efficient decoding algorithms for such edit metric codes.Several codes of varying lengths are used to study the effectiveness of evolutionary programming (EP) as a general approach for finding SEMs for edit metric decoding. Direct and fuzzy classification methods are compared while also changing some of the EP settings to observe how decoding accuracy is affected. Regardless of code length, the best results are found using the fuzzy classification methods. For codes of length 10, a maximum accuracy of up to 99.4% is achieved for distance 1 whereas distance 2 and 3 achieve up to 97.1% and 85.9%, respectively. The accuracy suffers for longer codes, as the maximum accuracies achieved by codes of length 14 were 92.4%, 85.7% and 69.2% for distance 1, 2, and 3 respectively. Additionally, the SEMs are examined for potential bloat by comparing the number of reachable states against the total number of states. Bloat is seen more in larger machines than it is in smaller machines. Furthermore, the results are analyzed to find potential trends and relationships among the parameters, with the most consistent trend being that, when allowed, the longer codes generally show a propensity for larger machines.

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Effective decoders for DNA codes

Houghten¹,

Banik²

2022

Biosystems

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Hybridization and ring optimization for larger sets of embeddable biomarkers

Cited by 5 publications

References 18 publications

Edit metric decoding: Return of the side effect machines

Edit metric decoding: Return of the side effect machines

Effective Side Effect Machines for Decoding

Effective decoders for DNA codes

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