Sampling Random Bioinformatics Puzzles using Adaptive Probability Distributions

Research output: Contribution to journal › Conference article › Research › peer-review

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Sampling Random Bioinformatics Puzzles using Adaptive Probability Distributions. / Have, Christian Theil; Appel, Emil Vincent; Bork-Jensen, Jette; Lassen, Ole Torp.

In: CEUR Workshop Proceedings, Vol. 1661, 2016, p. 39-45.

Research output: Contribution to journal › Conference article › Research › peer-review

Harvard

Have, CT, Appel, EV, Bork-Jensen, J & Lassen, OT 2016, 'Sampling Random Bioinformatics Puzzles using Adaptive Probability Distributions', CEUR Workshop Proceedings, vol. 1661, pp. 39-45. <http://ceur-ws.org/Vol-1661/paper-04.pdf>

APA

Have, C. T., Appel, E. V., Bork-Jensen, J., & Lassen, O. T. (2016). Sampling Random Bioinformatics Puzzles using Adaptive Probability Distributions. CEUR Workshop Proceedings, 1661, 39-45. http://ceur-ws.org/Vol-1661/paper-04.pdf

Vancouver

Have CT, Appel EV, Bork-Jensen J, Lassen OT. Sampling Random Bioinformatics Puzzles using Adaptive Probability Distributions. CEUR Workshop Proceedings. 2016;1661:39-45.

Author

Have, Christian Theil ; Appel, Emil Vincent ; Bork-Jensen, Jette ; Lassen, Ole Torp. / Sampling Random Bioinformatics Puzzles using Adaptive Probability Distributions. In: CEUR Workshop Proceedings. 2016 ; Vol. 1661. pp. 39-45.

Bibtex

@inproceedings{3bc283427593486fb66714487c760a2b,
title = "Sampling Random Bioinformatics Puzzles using Adaptive Probability Distributions",
abstract = "We present a probabilistic logic program to generate an educational puzzle that introduces the basic principles of next generation sequencing, gene finding and the translation of genes to proteins following the central dogma in biology. In the puzzle, a secret {"}protein word{"} must be found by assembling DNA from fragments (reads), locating a gene in this sequence and translating the gene to a protein. Sampling using this program generates random instance of the puzzle, but it is possible constrain the difficulty and to customize the secret protein word. Because of these constraints and the randomness of the generation process, sampling may fail to generate a satisfactory puzzle. To avoid failure we employ a strategy using adaptive probabilities which change in response to previous steps of generative process, thus minimizing the risk of failure.",
keywords = "Bioinformatics, PRISM, Sampling",
author = "Have, {Christian Theil} and Appel, {Emil Vincent} and Jette Bork-Jensen and Lassen, {Ole Torp}",
year = "2016",
language = "English",
volume = "1661",
pages = "39--45",
journal = "CEUR Workshop Proceedings",
issn = "1613-0073",
publisher = "ceur workshop proceedings",

}

RIS

TY - GEN

T1 - Sampling Random Bioinformatics Puzzles using Adaptive Probability Distributions

AU - Have, Christian Theil

AU - Appel, Emil Vincent

AU - Bork-Jensen, Jette

AU - Lassen, Ole Torp

PY - 2016

Y1 - 2016

N2 - We present a probabilistic logic program to generate an educational puzzle that introduces the basic principles of next generation sequencing, gene finding and the translation of genes to proteins following the central dogma in biology. In the puzzle, a secret "protein word" must be found by assembling DNA from fragments (reads), locating a gene in this sequence and translating the gene to a protein. Sampling using this program generates random instance of the puzzle, but it is possible constrain the difficulty and to customize the secret protein word. Because of these constraints and the randomness of the generation process, sampling may fail to generate a satisfactory puzzle. To avoid failure we employ a strategy using adaptive probabilities which change in response to previous steps of generative process, thus minimizing the risk of failure.

AB - We present a probabilistic logic program to generate an educational puzzle that introduces the basic principles of next generation sequencing, gene finding and the translation of genes to proteins following the central dogma in biology. In the puzzle, a secret "protein word" must be found by assembling DNA from fragments (reads), locating a gene in this sequence and translating the gene to a protein. Sampling using this program generates random instance of the puzzle, but it is possible constrain the difficulty and to customize the secret protein word. Because of these constraints and the randomness of the generation process, sampling may fail to generate a satisfactory puzzle. To avoid failure we employ a strategy using adaptive probabilities which change in response to previous steps of generative process, thus minimizing the risk of failure.

KW - Bioinformatics

KW - PRISM

KW - Sampling

M3 - Conference article

AN - SCOPUS:84987728108

VL - 1661

SP - 39

EP - 45

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

SN - 1613-0073

ER -

ID: 179394223