Automating Knowledge Discovery for Toxicity Prediction Using Jumping Emerging Pattern Mining
نویسندگان
چکیده
منابع مشابه
Automating Knowledge Discovery for Toxicity Prediction Using Jumping Emerging Pattern Mining
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 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...
متن کاملEmerging 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 کاملIdentifying emerging hotel preferences using Emerging Pattern Mining technique
Hotel managers continue to find ways to understand traveler preferences, with the aim of improving their strategic planning, marketing, and product development. Traveler preference is unpredictable; for example, hotel guests used to prefer having a telephone in the room, but now favor fast Internet connection. Changes in preference influence the performance of hotel businesses, thus creating th...
متن کاملApproaches for Pattern Discovery Using Sequential Data Mining
In this chapter we first introduce sequence data. We then discuss different approaches for mining of patterns from sequence data, studied in literature. Apriori based methods and the pattern growth methods are the earliest and the most influential methods for sequential pattern mining. There is also a vertical format based method which works on a dual representation of the sequence database. Wo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Chemical Information and Modeling
سال: 2012
ISSN: 1549-9596,1549-960X
DOI: 10.1021/ci300254w