loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Author: Shinsuke Uda

Affiliation: Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi Higashi-ku Fukuoka 812-8582, Japan

Keyword(s): Information Theory, Conditional Mutual Information, Permutation Test, Biological Data Analysis.

Abstract: Conditional mutual information is a useful measure for detecting the association between variables that are also affected by other variables. Though permutation tests are used to check whether the conditional mutual information is zero to indicate mutual independence, permutations are difficult to perform because the other variables in a dataset may be associated with the variables in question. This problem is particularly acute when working with datasets of small sample size. This study aims to propose a computational method for approximating conditional mutual information based on the distribution of residuals in regression models. The proposed method can implement the permutation tests for statistical significance by translating the problem of measuring conditional independence into the problem of estimating simple independence. Additionally, a reliability of p-value in permutation test is defined to omit unreliably detected associations. We tested our proposed method’s performanc e in inferring the network structure of an artificial gene network against comparable methods submitted to the Dream4 challenge. (More)

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 3.149.230.44

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:
Uda, S. (2020). Approximate Conditional Independence Test using Residuals. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 297-304. DOI: 10.5220/0008866102970304

@conference{icaart20,
author={Shinsuke Uda.},
title={Approximate Conditional Independence Test using Residuals},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2020},
pages={297-304},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008866102970304},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Approximate Conditional Independence Test using Residuals
SN - 978-989-758-395-7
IS - 2184-433X
AU - Uda, S.
PY - 2020
SP - 297
EP - 304
DO - 10.5220/0008866102970304
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