Generation of genetic networks from a small number of gene expression patterns under the Boolean network model.

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Jakir Hossain
Jakir Hossain
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Momtaz Begum
Momtaz Begum
α Dhaka University of Engineering & Technology

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Generation of genetic networks from a small number of gene expression patterns under the Boolean network model.

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Abstract

There are lots of work for inferring genetic network architectures from state transition tables which correspond to time series of gene expression patterns, using the Boolean network model. Results of those computational experiments suggested that a small number of state transition (INPUT/OUTPUT) pairs are sufficient in order to infer the original Boolean network correctly. Tatsuya AKUTSU, Satoru MIYANO and Satoru KUHARA gave a mathematical proof for this. So there is possibility to devise an algorithm to generate all consistent genetic networks from a small number of gene expression patterns under the Boolean network model.

References

14 Cites in Article
  1. Akutsu Tatsuya,Miyano Satoru,Kuhara Satoru Identification Of Genetic networks From A Small Number Of Gene Expression Patterns Under The Boolean Network Model.
  2. S Liang,S Fuhrman,R Somogyi (1998). REVEAL, a general reverse engineering algorithm for inference of genetic network architectures.
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  4. D Kightley,N Chandra,K Elliston (2004). Inferring Gene Regulatory Networks fromRaw Data: A Molecular Epistemics Approach.
  5. Rui Xu,Donald Wunsch,I,Ronald Frank,T Akutsu,S Kuhara,O Maruyama,S Miyano (1998). Inference of Genetic Regulatory Networks with Recurrent Neural Network Models Using Particle Swarm Optimization.
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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Jakir Hossain. 1970. \u201cGeneration of genetic networks from a small number of gene expression patterns under the Boolean network model.\u201d. Unknown Journal GJCST Volume 11 (GJCST Volume 11 Issue 21): .

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v1.2

Issue date

December 27, 2011

Language
en
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There are lots of work for inferring genetic network architectures from state transition tables which correspond to time series of gene expression patterns, using the Boolean network model. Results of those computational experiments suggested that a small number of state transition (INPUT/OUTPUT) pairs are sufficient in order to infer the original Boolean network correctly. Tatsuya AKUTSU, Satoru MIYANO and Satoru KUHARA gave a mathematical proof for this. So there is possibility to devise an algorithm to generate all consistent genetic networks from a small number of gene expression patterns under the Boolean network model.

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Generation of genetic networks from a small number of gene expression patterns under the Boolean network model.

Momtaz Begum
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