NDLI: Neural grammar networks for toxicology

Content Provider	IEEE Xplore Digital Library
Author	Cameron, C.J.F. Ma, E.Y.T. Kremer, S.C.
Copyright Year	2010
Description	Author affiliation: Department of Biology at the University of Waterloo, Waterloo, Ontario, Canada (Ma, E.Y.T.) \|\| School of Computer Science at the University of Guelph, Guelph, Ontario, Canada (Cameron, C.J.F.; Kremer, S.C.)
Abstract	In this paper we compare two methods for toxicity prediction: a novel method called a neural grammar network (NGN) and a more conventional Quantitative Structure Activity Relation (QSAR) approach based on a feed forward artificial neural network (ANN). Focusing each round of training and prediction on target organisms and specific organ systems sufficiently narrows down the parameters for us to do useful toxicity prediction. We represent the molecules in the dataset two ways. Simplified Molecular Input Line Entry Specification (SMILES) are input to the NGN while Feature vectors (or chemical descriptors) are input to the ANN. We perform training and testing on a regression-type problem wherein we predict the Lethal Dose for 50% (LD50) of the population of a given organism for the molecules in each dataset. The results of the experiment indicates that the SMILES-NGN method outperformed the ANN method in QSAR. The SMILES-NGN estimates were closer to their targets for 87% of the trials on randomized training data (as described in Section II.B) and 62% on grouped data when compared to ANN. The results also showed less variance in 87% of cases for NGN-SMILES estimates compared to ANN. Using a toxicity prediction method such as the one presented here allows the prediction of toxicity without the need for costly lab experiment (and which are, by definition, lethal to the test subjects).
Starting Page	1
Ending Page	8
File Size	322791
Page Count	8
File Format	PDF
ISBN	9781424467662
e-ISBN	9781424467662
DOI	10.1109/CIBCB.2010.5510322
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-02
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Toxicology Artificial neural networks Next generation networking Organisms Testing Feeds Chemicals Performance evaluation Training data Prediction methods Artificial Neural Networks Neural Grammar Network QSAR
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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