loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Edward L. Boone 1 ; Susan J. Simmons 2 and Karl Ricanek 2

Affiliations: 1 Virginia Commonwealth University, United States ; 2 University of North Carolina Wilmington, United States

Keyword(s): Quantitative trait loci, Epistasis, Bayesian statistics, Markov chain Monte Carlo model composition.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Symbolic Systems

Abstract: Epistasis or the interaction between loci on a genome is of great interest to geneticists. Herein, a powerful Bayesian method utilizing Markov chain Monte Carlo model composition approach using restricted spaces is developed for identifying epistatic effects in Recombinant Inbred Lines (RIL). The method is verified through a simulation study and applied to an Arabidopsis thaliana data set with cotyledon as the quantitative trait.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.15.235.80

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
L. Boone, E.; J. Simmons, S. and Ricanek, K. (2011). A BAYESIAN METHOD FOR THE DETECTION OF EPISTASIS IN QUANTITATIVE TRAIT LOCI USING MARKOV CHAIN MONTE CARLO MODEL COMPOSITION WITH RESTRICTED MODEL SPACES. In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-8425-40-9; ISSN 2184-433X, SciTePress, pages 71-78. DOI: 10.5220/0003139200710078

@conference{icaart11,
author={Edward {L. Boone}. and Susan {J. Simmons}. and Karl Ricanek.},
title={A BAYESIAN METHOD FOR THE DETECTION OF EPISTASIS IN QUANTITATIVE TRAIT LOCI USING MARKOV CHAIN MONTE CARLO MODEL COMPOSITION WITH RESTRICTED MODEL SPACES},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2011},
pages={71-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003139200710078},
isbn={978-989-8425-40-9},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - A BAYESIAN METHOD FOR THE DETECTION OF EPISTASIS IN QUANTITATIVE TRAIT LOCI USING MARKOV CHAIN MONTE CARLO MODEL COMPOSITION WITH RESTRICTED MODEL SPACES
SN - 978-989-8425-40-9
IS - 2184-433X
AU - L. Boone, E.
AU - J. Simmons, S.
AU - Ricanek, K.
PY - 2011
SP - 71
EP - 78
DO - 10.5220/0003139200710078
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic