The Experts below are selected from a list of 11502 Experts worldwide ranked by ideXlab platform
Alexandre Varnek - One of the best experts on this subject based on the ideXlab platform.
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chemical data visualization and analysis with Incremental generative topographic mapping big data challenge
Journal of Chemical Information and Modeling, 2015Co-Authors: Helena A Gaspar, Gilles Marcou, Dragos Horvath, I I Baskin, Alexandre VarnekAbstract:This paper is devoted to the analysis and visualization in 2-dimensional space of large data sets of millions of compounds using the Incremental Version of generative topographic mapping (iGTM). The iGTM algorithm implemented in the in-house ISIDA-GTM program was applied to a database of more than 2 million compounds combining data sets of 36 chemicals suppliers and the NCI collection, encoded either by MOE descriptors or by MACCS keys. Taking advantage of the probabilistic nature of GTM, several approaches to data analysis were proposed. The chemical space coverage was evaluated using the normalized Shannon entropy. Different views of the data (property landscapes) were obtained by mapping various physical and chemical properties (molecular weight, aqueous solubility, LogP, etc.) onto the iGTM map. The superposition of these views helped to identify the regions in the chemical space populated by compounds with desirable physicochemical profiles and the suppliers providing them. The data sets similarity i...
Christopher K. I. Williams - One of the best experts on this subject based on the ideXlab platform.
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Developments of the generative topographic mapping
Neurocomputing, 1998Co-Authors: Christopher M. Bishop, Markus Svensen, Christopher K. I. WilliamsAbstract:The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput. 10(1), 215-234) as a probabilistic re- formulation of the self-organizing map (SOM). It offers a number of advantages compared with the standard SOM, and has already been used in a variety of applications. In this paper we report on several extensions of the GTM, including an Incremental Version of the EM algorithm for estimating the model parameters, the use of local subspace models, extensions to mixed discrete and continuous data, semi-linear models which permit the use of high-dimensional manifolds whilst avoiding computational intractability, Bayesian inference applied to hyper-parameters, and an alternative framework for the GTM based on Gaussian processes. All of these developments directly exploit the probabilistic structure of the GTM, thereby allowing the underlying modelling assumptions to be made explicit. They also highlight the advantages of adopting a consistent probabilistic framework for the formulation of pattern recognition algorithms.
Helena A Gaspar - One of the best experts on this subject based on the ideXlab platform.
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chemical data visualization and analysis with Incremental generative topographic mapping big data challenge
Journal of Chemical Information and Modeling, 2015Co-Authors: Helena A Gaspar, Gilles Marcou, Dragos Horvath, I I Baskin, Alexandre VarnekAbstract:This paper is devoted to the analysis and visualization in 2-dimensional space of large data sets of millions of compounds using the Incremental Version of generative topographic mapping (iGTM). The iGTM algorithm implemented in the in-house ISIDA-GTM program was applied to a database of more than 2 million compounds combining data sets of 36 chemicals suppliers and the NCI collection, encoded either by MOE descriptors or by MACCS keys. Taking advantage of the probabilistic nature of GTM, several approaches to data analysis were proposed. The chemical space coverage was evaluated using the normalized Shannon entropy. Different views of the data (property landscapes) were obtained by mapping various physical and chemical properties (molecular weight, aqueous solubility, LogP, etc.) onto the iGTM map. The superposition of these views helped to identify the regions in the chemical space populated by compounds with desirable physicochemical profiles and the suppliers providing them. The data sets similarity i...
Gilles Marcou - One of the best experts on this subject based on the ideXlab platform.
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chemical data visualization and analysis with Incremental generative topographic mapping big data challenge
Journal of Chemical Information and Modeling, 2015Co-Authors: Helena A Gaspar, Gilles Marcou, Dragos Horvath, I I Baskin, Alexandre VarnekAbstract:This paper is devoted to the analysis and visualization in 2-dimensional space of large data sets of millions of compounds using the Incremental Version of generative topographic mapping (iGTM). The iGTM algorithm implemented in the in-house ISIDA-GTM program was applied to a database of more than 2 million compounds combining data sets of 36 chemicals suppliers and the NCI collection, encoded either by MOE descriptors or by MACCS keys. Taking advantage of the probabilistic nature of GTM, several approaches to data analysis were proposed. The chemical space coverage was evaluated using the normalized Shannon entropy. Different views of the data (property landscapes) were obtained by mapping various physical and chemical properties (molecular weight, aqueous solubility, LogP, etc.) onto the iGTM map. The superposition of these views helped to identify the regions in the chemical space populated by compounds with desirable physicochemical profiles and the suppliers providing them. The data sets similarity i...
I I Baskin - One of the best experts on this subject based on the ideXlab platform.
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chemical data visualization and analysis with Incremental generative topographic mapping big data challenge
Journal of Chemical Information and Modeling, 2015Co-Authors: Helena A Gaspar, Gilles Marcou, Dragos Horvath, I I Baskin, Alexandre VarnekAbstract:This paper is devoted to the analysis and visualization in 2-dimensional space of large data sets of millions of compounds using the Incremental Version of generative topographic mapping (iGTM). The iGTM algorithm implemented in the in-house ISIDA-GTM program was applied to a database of more than 2 million compounds combining data sets of 36 chemicals suppliers and the NCI collection, encoded either by MOE descriptors or by MACCS keys. Taking advantage of the probabilistic nature of GTM, several approaches to data analysis were proposed. The chemical space coverage was evaluated using the normalized Shannon entropy. Different views of the data (property landscapes) were obtained by mapping various physical and chemical properties (molecular weight, aqueous solubility, LogP, etc.) onto the iGTM map. The superposition of these views helped to identify the regions in the chemical space populated by compounds with desirable physicochemical profiles and the suppliers providing them. The data sets similarity i...
