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Data integration with self-organising neural network reveals chemical structure and therapeutic effects of drug ATC codes

McGarry, Kenneth and Assamoha, Ennock (2017) Data integration with self-organising neural network reveals chemical structure and therapeutic effects of drug ATC codes. In: The 17th Annual UK Workshop on Computational Intelligence (UKCI-2017), 6-8 Sep 2017, Cardiff.

Item Type: Conference or Workshop Item (Paper)

Abstract

Anatomical Therapeutic Codes (ATC) are a drug classification system which is extensively used in the field of drug development research. There are many drugs and medical compounds that as yet do not have ATC codes, it would be useful to have codes automatically assigned to them by computational methods. Our initial work involved building feedforward multi-layer perceptron models (MLP) but the classification accuracy was poor. To gain insights into the
problem we used the Kohonen self-organizing neural network to visualize the relationship between the class labels and the independent variables. The information gained from the learned internal clusters gave a deeper insight into the mapping process. The ability to accurately predict ATC codes was unbalanced due to over and under representation of some ATC classes. Further difficulties arise because many drugs have several, quite different ATC codes because they have
many therapeutic uses. We used chemical fingerprint data representing a drugs chemical structure and chemical activity variables. Evaluation metrics were computed,
analysing the predictive performance of various self-organizing models.

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More Information

Depositing User: Kenneth McGarry

Identifiers

Item ID: 7533
URI: http://sure.sunderland.ac.uk/id/eprint/7533
Official URL: http://www.cardiff.ac.uk/conferences/ukci2017

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD orcid.org/0000-0002-9329-9835

Catalogue record

Date Deposited: 12 Jul 2017 08:27
Last Modified: 30 Sep 2020 11:03

Contributors

Author: Kenneth McGarry ORCID iD
Author: Ennock Assamoha

University Divisions

Faculty of Technology
Faculty of Technology > School of Computer Science

Subjects

Computing > Artificial Intelligence
Sciences > Chemistry

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