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Towards model governance in predictive toxicology
Palczewska, Anna Maria Fu, X. Trundle, Paul R. Yang, Longzhi Neagu, Daniel Ridley, Mick J. Travis, Kim
Palczewska, Anna Maria
Fu, X.
Trundle, Paul R.
Yang, Longzhi
Neagu, Daniel
Ridley, Mick J.
Travis, Kim
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2013
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Abstract
Efficient management of toxicity information as an enterprise asset is increasingly important for the chemical, pharmaceutical, cosmetics and food industries. Many organisations focus on better information organisation and reuse, in an attempt to reduce the costs of testing and manufacturing in the product development phase. Toxicity information is extracted not only from toxicity data but also from predictive models. Accurate and appropriately shared models can bring a number of benefits if we are able to make effective use of existing expertise. Although usage of existing models may provide high-impact insights into the relationships between chemical attributes and specific toxicological effects, they can also be a source of risk for incorrect decisions. Thus, there is a need to provide a framework for efficient model management. To address this gap, this paper introduces a concept of model governance, that is based upon data governance principles. We extend the data governance processes by adding procedures that allow the evaluation of model use and governance for enterprise purposes. The core aspect of model governance is model representation. We propose six rules that form the basis of a model representation schema, called Minimum Information About a QSAR Model Representation (MIAQMR). As a proof-of-concept of our model governance framework we develop a web application called Model and Data Farm (MADFARM), in which models are described by the MIAQMR-ML markup language. (C) 2013 Elsevier Ltd. All rights reserved.
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Palczewska A, Fu X, Trundle P et al (2013) Towards model governance in predictive toxicology. International Journal of Information Management. 33(3): 567-582.
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