GreenSim: A network simulator for comprehensively validating and evaluating new machine learning techniques for network structural inference

Christopher Fogelberg, Vasile Palade

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

1 Citation (Scopus)

Abstract

Networks are very important in many fields of machine learning research. Within networks research, inferring the structure of unknown networks is often a key problem; e.g. of genetic regulatory networks. However, there are very few wellknown biological networks, and good simulation is essential for validating and evaluating novel structural inference techniques. Further, the importance of large, genome-wide structural inference is increasingly recognised, but there does not appear to be a good simulator available for large networks. This paper presents GREENSIM, a simulator that helps address this gap. GREENSIM automatically generates large, genome-size networks with more biologically realistic structural characteristics and 2nd-order non-linear regulatory functions. The simulator itself and the novel method used for generating a network structure with appropriate in- and out-degree distributions may also generalise easily to other types of network. GREENSIM is available online at: http://syntilect.com/cgf/pubs:software

Original languageEnglish
Title of host publicationProceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010
PublisherIEEE
Pages225-230
Number of pages6
Volume2
ISBN (Electronic)978-0-7695-4263-8
ISBN (Print)978-1-4244-8817-9
DOIs
Publication statusPublished - 17 Dec 2010
Externally publishedYes
Event22nd International Conference on Tools with Artificial Intelligence - Arras, France
Duration: 27 Oct 201029 Oct 2010

Conference

Conference22nd International Conference on Tools with Artificial Intelligence
Abbreviated titleICTAI 2010
CountryFrance
CityArras
Period27/10/1029/10/10

Fingerprint

Learning systems
Simulators
Genes

Keywords

  • Genetic regulatory networks
  • Networks
  • Simulation
  • Structural inference

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

Cite this

Fogelberg, C., & Palade, V. (2010). GreenSim: A network simulator for comprehensively validating and evaluating new machine learning techniques for network structural inference. In Proceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010 (Vol. 2, pp. 225-230). [5671407] IEEE. https://doi.org/10.1109/ICTAI.2010.105

GreenSim : A network simulator for comprehensively validating and evaluating new machine learning techniques for network structural inference. / Fogelberg, Christopher; Palade, Vasile.

Proceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010. Vol. 2 IEEE, 2010. p. 225-230 5671407.

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Fogelberg, C & Palade, V 2010, GreenSim: A network simulator for comprehensively validating and evaluating new machine learning techniques for network structural inference. in Proceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010. vol. 2, 5671407, IEEE, pp. 225-230, 22nd International Conference on Tools with Artificial Intelligence, Arras, France, 27/10/10. https://doi.org/10.1109/ICTAI.2010.105
Fogelberg C, Palade V. GreenSim: A network simulator for comprehensively validating and evaluating new machine learning techniques for network structural inference. In Proceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010. Vol. 2. IEEE. 2010. p. 225-230. 5671407 https://doi.org/10.1109/ICTAI.2010.105
Fogelberg, Christopher ; Palade, Vasile. / GreenSim : A network simulator for comprehensively validating and evaluating new machine learning techniques for network structural inference. Proceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010. Vol. 2 IEEE, 2010. pp. 225-230
@inproceedings{3ef207d8109b4316bbf5cb178cc64001,
title = "GreenSim: A network simulator for comprehensively validating and evaluating new machine learning techniques for network structural inference",
abstract = "Networks are very important in many fields of machine learning research. Within networks research, inferring the structure of unknown networks is often a key problem; e.g. of genetic regulatory networks. However, there are very few wellknown biological networks, and good simulation is essential for validating and evaluating novel structural inference techniques. Further, the importance of large, genome-wide structural inference is increasingly recognised, but there does not appear to be a good simulator available for large networks. This paper presents GREENSIM, a simulator that helps address this gap. GREENSIM automatically generates large, genome-size networks with more biologically realistic structural characteristics and 2nd-order non-linear regulatory functions. The simulator itself and the novel method used for generating a network structure with appropriate in- and out-degree distributions may also generalise easily to other types of network. GREENSIM is available online at: http://syntilect.com/cgf/pubs:software",
keywords = "Genetic regulatory networks, Networks, Simulation, Structural inference",
author = "Christopher Fogelberg and Vasile Palade",
year = "2010",
month = "12",
day = "17",
doi = "10.1109/ICTAI.2010.105",
language = "English",
isbn = "978-1-4244-8817-9",
volume = "2",
pages = "225--230",
booktitle = "Proceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010",
publisher = "IEEE",

}

TY - GEN

T1 - GreenSim

T2 - A network simulator for comprehensively validating and evaluating new machine learning techniques for network structural inference

AU - Fogelberg, Christopher

AU - Palade, Vasile

PY - 2010/12/17

Y1 - 2010/12/17

N2 - Networks are very important in many fields of machine learning research. Within networks research, inferring the structure of unknown networks is often a key problem; e.g. of genetic regulatory networks. However, there are very few wellknown biological networks, and good simulation is essential for validating and evaluating novel structural inference techniques. Further, the importance of large, genome-wide structural inference is increasingly recognised, but there does not appear to be a good simulator available for large networks. This paper presents GREENSIM, a simulator that helps address this gap. GREENSIM automatically generates large, genome-size networks with more biologically realistic structural characteristics and 2nd-order non-linear regulatory functions. The simulator itself and the novel method used for generating a network structure with appropriate in- and out-degree distributions may also generalise easily to other types of network. GREENSIM is available online at: http://syntilect.com/cgf/pubs:software

AB - Networks are very important in many fields of machine learning research. Within networks research, inferring the structure of unknown networks is often a key problem; e.g. of genetic regulatory networks. However, there are very few wellknown biological networks, and good simulation is essential for validating and evaluating novel structural inference techniques. Further, the importance of large, genome-wide structural inference is increasingly recognised, but there does not appear to be a good simulator available for large networks. This paper presents GREENSIM, a simulator that helps address this gap. GREENSIM automatically generates large, genome-size networks with more biologically realistic structural characteristics and 2nd-order non-linear regulatory functions. The simulator itself and the novel method used for generating a network structure with appropriate in- and out-degree distributions may also generalise easily to other types of network. GREENSIM is available online at: http://syntilect.com/cgf/pubs:software

KW - Genetic regulatory networks

KW - Networks

KW - Simulation

KW - Structural inference

UR - http://www.scopus.com/inward/record.url?scp=78751499701&partnerID=8YFLogxK

U2 - 10.1109/ICTAI.2010.105

DO - 10.1109/ICTAI.2010.105

M3 - Conference proceeding

SN - 978-1-4244-8817-9

VL - 2

SP - 225

EP - 230

BT - Proceedings - 22nd International Conference on Tools with Artificial Intelligence, ICTAI 2010

PB - IEEE

ER -