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Chemical Data Visualization and Analysis with Incremental Generative Topographic Mapping: Big Data Challenge

https://doi.org/10.1021/ci500575y
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Developments of the generative topographic mapping
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Generative Topographic Mapping-Based Classification Models and Their Applicability Domain: Application to the Biopharmaceutics Drug Disposition Classification System (BDDCS)
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The Scaffold Tree − Visualization of the Scaffold Universe by Hierarchical Scaffold Classification
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Chemoinformatic Analysis of Combinatorial Libraries, Drugs, Natural Products, and Molecular Libraries Small Molecule Repository
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Shannon Entropy-Based Fingerprint Similarity Search Strategy
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Toward Navigating Chemical Space of Ionic Liquids: Prediction of Melting Points Using Generative Topographic Maps
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Benchmarking Sets for Molecular Docking
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The Role of Big Data and Advanced Analytics in Drug Discovery, Development, and Commercialization
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Probabilistic Principal Component Analysis
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Entropy and diversity
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GTM: The Generative Topographic Mapping
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Expanding the fragrance chemical space for virtual screening
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R: A Language and Environment for Statistical Computing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Generative topographic mapping : a powerful tool for big chemical data visualization, analysis and modeling
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