The application of model-based complexity inference method to molecular evolution analysis

Fengrong Ren; Tanaka Hiroshi; Toshitsugu Okayama; Takashi Gojobori

doi:10.3233/978-1-60750-896-0-367

The application of model-based complexity inference method to molecular evolution analysis

Fengrong Ren^*, Tanaka Hiroshi, Toshitsugu Okayama, Takashi Gojobori

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this study, a new method based on the concept of complexity in inductive inference is proposed for reconstructing molecular phylogenetic tree. This method describes the complexity of molecular phylogenetic tree by three terms, which are related to tree topology, the branch lengths and fitness between the model and data measured by likelihood function. The computer simulation is used to investigate the efficiency of this method. The results suggest that this method is superior to the traditional methods because it avoids excess-complexity in the tree model estimation available from DNA sequence.

Original language	English (US)
Title of host publication	MedInfo 1998 - 9th World Congress on Medical Informatics
Publisher	IOS Press
Pages	367-371
Number of pages	5
ISBN (Print)	9051994079, 9789051994070
DOIs	https://doi.org/10.3233/978-1-60750-896-0-367
State	Published - 1998
Externally published	Yes
Event	9th World Congress on Medical Informatics, MedInfo 1998 - Seoul, Korea, Republic of Duration: Aug 18 1998 → Aug 22 1998

Publication series

Name	Studies in Health Technology and Informatics
Volume	52
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Other

Other	9th World Congress on Medical Informatics, MedInfo 1998
Country/Territory	Korea, Republic of
City	Seoul
Period	08/18/98 → 08/22/98

Keywords

Maximum likelihood method
Minimum model-based complexity Method
Molecular Phylogenetic Tree

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/978-1-60750-896-0-367

Cite this

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title = "The application of model-based complexity inference method to molecular evolution analysis",

abstract = "In this study, a new method based on the concept of complexity in inductive inference is proposed for reconstructing molecular phylogenetic tree. This method describes the complexity of molecular phylogenetic tree by three terms, which are related to tree topology, the branch lengths and fitness between the model and data measured by likelihood function. The computer simulation is used to investigate the efficiency of this method. The results suggest that this method is superior to the traditional methods because it avoids excess-complexity in the tree model estimation available from DNA sequence.",

keywords = "Maximum likelihood method, Minimum model-based complexity Method, Molecular Phylogenetic Tree",

author = "Fengrong Ren and Tanaka Hiroshi and Toshitsugu Okayama and Takashi Gojobori",

year = "1998",

doi = "10.3233/978-1-60750-896-0-367",

language = "English (US)",

isbn = "9051994079",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

pages = "367--371",

booktitle = "MedInfo 1998 - 9th World Congress on Medical Informatics",

address = "Netherlands",

note = "9th World Congress on Medical Informatics, MedInfo 1998 ; Conference date: 18-08-1998 Through 22-08-1998",

}

Ren, F, Hiroshi, T, Okayama, T & Gojobori, T 1998, The application of model-based complexity inference method to molecular evolution analysis. in MedInfo 1998 - 9th World Congress on Medical Informatics. Studies in Health Technology and Informatics, vol. 52, IOS Press, pp. 367-371, 9th World Congress on Medical Informatics, MedInfo 1998, Seoul, Korea, Republic of, 08/18/98. https://doi.org/10.3233/978-1-60750-896-0-367

The application of model-based complexity inference method to molecular evolution analysis. / Ren, Fengrong; Hiroshi, Tanaka; Okayama, Toshitsugu et al.
MedInfo 1998 - 9th World Congress on Medical Informatics. IOS Press, 1998. p. 367-371 (Studies in Health Technology and Informatics; Vol. 52).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - The application of model-based complexity inference method to molecular evolution analysis

AU - Ren, Fengrong

AU - Hiroshi, Tanaka

AU - Okayama, Toshitsugu

AU - Gojobori, Takashi

PY - 1998

Y1 - 1998

N2 - In this study, a new method based on the concept of complexity in inductive inference is proposed for reconstructing molecular phylogenetic tree. This method describes the complexity of molecular phylogenetic tree by three terms, which are related to tree topology, the branch lengths and fitness between the model and data measured by likelihood function. The computer simulation is used to investigate the efficiency of this method. The results suggest that this method is superior to the traditional methods because it avoids excess-complexity in the tree model estimation available from DNA sequence.

AB - In this study, a new method based on the concept of complexity in inductive inference is proposed for reconstructing molecular phylogenetic tree. This method describes the complexity of molecular phylogenetic tree by three terms, which are related to tree topology, the branch lengths and fitness between the model and data measured by likelihood function. The computer simulation is used to investigate the efficiency of this method. The results suggest that this method is superior to the traditional methods because it avoids excess-complexity in the tree model estimation available from DNA sequence.

KW - Maximum likelihood method

KW - Minimum model-based complexity Method

KW - Molecular Phylogenetic Tree

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U2 - 10.3233/978-1-60750-896-0-367

DO - 10.3233/978-1-60750-896-0-367

M3 - Conference contribution

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SN - 9051994079

SN - 9789051994070

T3 - Studies in Health Technology and Informatics

SP - 367

EP - 371

BT - MedInfo 1998 - 9th World Congress on Medical Informatics

PB - IOS Press

T2 - 9th World Congress on Medical Informatics, MedInfo 1998

Y2 - 18 August 1998 through 22 August 1998

ER -

The application of model-based complexity inference method to molecular evolution analysis

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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