Emerging Pattern Mining To Aid Toxicological Knowledge Discovery
نویسندگان
چکیده
منابع مشابه
Emerging Pattern Mining To Aid Toxicological Knowledge Discovery
Knowledge-based systems for toxicity prediction are typically based on rules, known as structural alerts, that describe relationships between structural features and different toxic effects. The identification of structural features associated with toxicological activity can be a time-consuming process and often requires significant input from domain experts. Here, we describe an emerging patte...
متن کاملToxicological knowledge discovery by mining emerging patterns from toxicity data
Predicting the risk of toxic and environmental effects of chemical compounds is of great importance to all chemical industries [1]. Expert systems have shown success in predicting toxic risk by applying established knowledge of toxicology encoded as a knowledge base of structural alerts and a reasoning model. A disadvantage of expert systems is that developing new structural alerts requires con...
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The design of new alerts, that is, collections of structural features observed to result in toxicological activity, can be a slow process and may require significant input from toxicology and chemistry experts. A method has therefore been developed to help automate alert identification by mining descriptions of activating structural features directly from toxicity data sets. The method is based...
متن کاملData Quality Issues in Toxicological Knowledge Discovery
Every SAR technique for toxicity prediction relies on the exact estimation and representation of chemical and toxicological properties. We will present potential sources of errors associated with the utilization of l~trge, noncongeneric datasets and complex toxicologi(:al endpoints (e.g. carcinogenicity). According to (~xperience we have identified the major problems in the areas of compound i ...
متن کاملRepresentational/Efficiency Issues in Toxicological Knowledge Discovery
We tackle the problem of automatic detection of StructureActivity relationships by means of Inductive Logic Programming (ILP). We describe an algorithm for constructing features from background knowledge (stochastic propositionalization SP) and an abstract level representation for chemical compounds.
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ژورنال
عنوان ژورنال: Journal of Chemical Information and Modeling
سال: 2014
ISSN: 1549-9596,1549-960X
DOI: 10.1021/ci5001828