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Publication Type : Journal Article
Source : Lecture Notes in Computer Science, 4668/2007, p.628-637 (Springer, Berlin / Heidelberg, 2007) IF: 0.51, 2007
Url : https://link.springer.com/chapter/10.1007/978-3-540-74690-4_64
Keywords : Root Mean Square Error, Human Serum Albumin, Data Fusion, QSAR Model, Level Fusion
Campus : Coimbatore
School : School of Engineering
Year : 2007
Abstract : Data fusion originally referred to the process of combining multi-sensor data from different sources such that the resulting information/model is in some sense better than would be possible when these sources where used individually. In this paper the data fusion concept is extended to molecular drug design. Rather than using data from different sensor sources, different descriptor sets are used to predict activities or responses for a set of molecules. Data fusion techniques are applied in order to improve the predictive (QSAR) model on test data. In this case this type of data fusion is referred to as auto-fusion. An effective auto-fusion functional model and alternative architectures are proposed for a predictive molecular design or QSAR model to model and predict the binding affinity to the human serum albumin.
Cite this Research Publication : Changjian Huang, Mark J. Embrechts, N. Sukumar and Curt M. Breneman, “Data Fusion and Auto-fusion for Quantitative Structure-Activity Relationship (QSAR)” Lecture Notes in Computer Science, 4668/2007, p.628-637 (Springer, Berlin / Heidelberg, 2007) IF: 0.51