Grid computing for the estimation of toxicity: Acute toxicity on Fathead Minnow (Pimephales promelas)

Uko Maran, Sulev Sild, Paolo Mazzatorta, Mosé Casalegno, Emilio Benfenati, Mathilde Romberg

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

15 Citations (Scopus)

Abstract

The computational estimation of toxicity is time-consuming and therefore needs support for distributed, high-performance and/or grid computing. The major technology behind the estimation of toxicity is quantitative structure activity relationship modelling. It is a complex procedure involving data gathering, preparation and analysis. The current paper describes the use of grid computing in the computational estimation of toxicity and provides a comparative study on the acute toxicity of fathead minnow (Pimephales promelas) comparing the heuristic multi-linear regression and artificial neural network approaches for quantitative structure activity relationship models.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages60-74
Number of pages15
Volume4360 LNBI
Publication statusPublished - 2007
EventDistributed, High-Performance and Grid Computing in Computational Biology International Workshop, GCCB 2006 - Eilat, Israel
Duration: Jan 21 2007Jan 21 2007

Other

OtherDistributed, High-Performance and Grid Computing in Computational Biology International Workshop, GCCB 2006
CountryIsrael
CityEilat
Period1/21/071/21/07

Fingerprint

Cyprinidae
Quantitative Structure-Activity Relationship
Grid computing
Grid Computing
Toxicity
Acute
Quantitative Structure-activity Relationship
Linear Models
Technology
Linear regression
Comparative Study
Artificial Neural Network
Preparation
High Performance
Heuristics
Neural networks
Modeling

Keywords

  • Chemistry
  • Distributed computing
  • Modelling and prediction
  • Molecular descriptors
  • QSAR
  • Workflow

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Maran, U., Sild, S., Mazzatorta, P., Casalegno, M., Benfenati, E., & Romberg, M. (2007). Grid computing for the estimation of toxicity: Acute toxicity on Fathead Minnow (Pimephales promelas). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4360 LNBI, pp. 60-74)

Grid computing for the estimation of toxicity : Acute toxicity on Fathead Minnow (Pimephales promelas). / Maran, Uko; Sild, Sulev; Mazzatorta, Paolo; Casalegno, Mosé; Benfenati, Emilio; Romberg, Mathilde.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4360 LNBI 2007. p. 60-74.

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

Maran, U, Sild, S, Mazzatorta, P, Casalegno, M, Benfenati, E & Romberg, M 2007, Grid computing for the estimation of toxicity: Acute toxicity on Fathead Minnow (Pimephales promelas). in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 4360 LNBI, pp. 60-74, Distributed, High-Performance and Grid Computing in Computational Biology International Workshop, GCCB 2006, Eilat, Israel, 1/21/07.
Maran U, Sild S, Mazzatorta P, Casalegno M, Benfenati E, Romberg M. Grid computing for the estimation of toxicity: Acute toxicity on Fathead Minnow (Pimephales promelas). In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4360 LNBI. 2007. p. 60-74
Maran, Uko ; Sild, Sulev ; Mazzatorta, Paolo ; Casalegno, Mosé ; Benfenati, Emilio ; Romberg, Mathilde. / Grid computing for the estimation of toxicity : Acute toxicity on Fathead Minnow (Pimephales promelas). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 4360 LNBI 2007. pp. 60-74
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