Literature DB >> 23704803

Learning Qualitative Differential Equation models: a survey of algorithms and applications.

Wei Pang1, George M Coghill.   

Abstract

Over the last two decades, qualitative reasoning (QR) has become an important domain in Artificial Intelligence. QDE (Qualitative Differential Equation) model learning (QML), as a branch of QR, has also received an increasing amount of attention; many systems have been proposed to solve various significant problems in this field. QML has been applied to a wide range of fields, including physics, biology and medical science. In this paper, we first identify the scope of this review by distinguishing QML from other QML systems, and then review all the noteworthy QML systems within this scope. The applications of QML in several application domains are also introduced briefly. Finally, the future directions of QML are explored from different perspectives.

Entities:  

Year:  2010        PMID: 23704803      PMCID: PMC3659653          DOI: 10.1017/S0269888909990348

Source DB:  PubMed          Journal:  Knowl Eng Rev        ISSN: 0269-8889            Impact factor:   1.115


  5 in total

1.  Protein secondary structure prediction using logic-based machine learning.

Authors:  S Muggleton; R D King; M J Sternberg
Journal:  Protein Eng       Date:  1992-10

2.  Qualitative simulation of genetic regulatory networks using piecewise-linear models.

Authors:  Hidde De Jong; Jean-Luc Gouzé; Céline Hernandez; Michel Page; Tewfik Sari; Johannes Geiselmann
Journal:  Bull Math Biol       Date:  2004-03       Impact factor: 1.758

3.  On the use of qualitative reasoning to simulate and identify metabolic pathways.

Authors:  Ross D King; Simon M Garrett; George M Coghill
Journal:  Bioinformatics       Date:  2005-01-12       Impact factor: 6.937

Review 4.  Methylglyoxal production in bacteria: suicide or survival?

Authors:  G P Ferguson; S Tötemeyer; M J MacLean; I R Booth
Journal:  Arch Microbiol       Date:  1998-10       Impact factor: 2.552

5.  Qualitative models and fuzzy systems: an integrated approach for learning from data.

Authors:  R Bellazzi; L Ironi; R Guglielmann; M Stefanelli
Journal:  Artif Intell Med       Date:  1998 Sep-Oct       Impact factor: 5.326

  5 in total
  4 in total

1.  QML-AiNet: An immune network approach to learning qualitative differential equation models.

Authors:  Wei Pang; George M Coghill
Journal:  Appl Soft Comput       Date:  2015-02       Impact factor: 6.725

2.  Qualitative, semi-quantitative, and quantitative simulation of the osmoregulation system in yeast.

Authors:  Wei Pang; George M Coghill
Journal:  Biosystems       Date:  2015-04-09       Impact factor: 1.973

3.  An integrative top-down and bottom-up qualitative model construction framework for exploration of biochemical systems.

Authors:  Zujian Wu; Wei Pang; George M Coghill
Journal:  Soft comput       Date:  2015       Impact factor: 3.643

4.  An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing.

Authors:  Zujian Wu; Wei Pang; George M Coghill
Journal:  Cognit Comput       Date:  2015-05-03       Impact factor: 5.418

  4 in total

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