Predictive data mining

Weiss, S. M., and Indurkhya, N. (1997). Predictive Data Mining A Practical Guide (with Software), Morgan Kaufmann, San Francisco, CA. 

M. Ozdemir, Evolutionary Computing for Feature Selection and Predictive Data Mining. Ph.D. dissertation, Rensselaer Polytechnic Institute, Troy, New York, 2002. 

Weiss, S.M. and N. Indurkhya, 1998. Predictive data mining a practical guide. San Francisco, Morgan Kaufmann... 

Data mining can fulfill various different tasks such as classification, clustering and similarity detection, prediction, estimation, or description retrieval, which are described in Sections 9.8.1-9.8.5. 

Increased trust in pattern recognition The active user involvement in the data mining process can lead to a deeper understanding of the data and increases the trust in the resulting patterns. In contrast, "black box" systems often lead to a higher uncertainty, because the user usually does not know, in detail, what happened during the data analysis process. This may lead to a more difficult data interpretation and/or model prediction. 

Machine learning provides the easiest approach to data mining, and also provides solutions in many fields of chemistry quality control in analytical chemistry [31], interpretation of mass spectra [32], as well prediction of pharmaceutical properties [33, 34] or drug design [35]. 

Berger CR. Predictive Analysis Is Data Mining s Future BioIT world 2005, June 20, 2005 http //www.bio-itworld.eom/newsitems/2005/06-05/06-23-05-news-oracle. 

Hastie T, Tibshirani R, Friedman JFI. The elements of statistical learning data mining, inference, and prediction. New York Springer, 2001. 

KING, R.D., KARWATH, A., CLARE, A., DEHASPE, L., Accurate prediction of protein functional class from sequence in Mycobacterium tuberculosis and Escherichia coli genomes using data mining, Yeast, 2000,17, 283-293. 

MacKay s textbook [114] offers not only a comprehensive coverage of Shannon s theory of information but also probabilistic data modeling and the mathematical theory of neural networks. Artificial NN can be applied when problems appear with processing and analyzing the data, with their prediction and classification (data mining). The wide range of applications of NN also comprises optimization issues. The information-theoretic capabilities of some neural network algorithms are examined and neural networks are motivated as statistical models [114]. 

Hastie, T, Tibshirani, R. and Eriedman, J. H. (2001). The Elements of Statistical Learning. Data Mining, Inference and Prediction. Springer, New York. 

It makes sense to use data mining of structural features and molecular properties to assist in the building of alerts for more precise structural features. Various in vitro testing results can be used to describe structures at the compound and feature levels. These profiles can be used either to build weight of evidence strategies or as predictors for prediction models. 

The following summarizes three related computational approaches that enable the prediction of target or mechanism of action (MOA) from chemical stmcture chemical similarity searching, data mining/machine learning and bioactivity spectra. 

Processed data input to the ToxGastDB database and can be downloaded for data-mining and predictive and mechanistic modeling... 

Fischer, Christopher C., Kevin J. Tibbetts, Dane Morgan, and Gerbrand Ceder. Predicting Crystal Structure by Merging Data Mining with Quantum Mechanics. Nature Materials 5 (2006) 641-646. The researchers developed a data-mining algorithm in 2006 to look at crystal structures. 

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