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diff --git a/public/enm-workshop.rst b/public/enm-workshop.rst index 26a3524..d54f4cb 100644 --- a/public/enm-workshop.rst +++ b/public/enm-workshop.rst @@ -1,29 +1,29 @@ .. |date| date:: - ============================================================= Read across toxicity predictions with nano-lazar ============================================================= .. class:: center - Christoph Helma + Christoph Helma, Denis Gebele, Micha Rautenberg in silico toxicology gmbh .. image:: http://www.enanomapper.net/sites/all/themes/theme807/logo.png :align: center -Requirements -============ +Requirements for nanoparticle read-across +========================================= -- Nanoparticle characterisation -- Toxicity measurements +.. class:: incremental + - Nanoparticle characterisation + - Toxicity measurements -eNanoMapper data import -======================= +eNanoMapper particle characterisation +===================================== .. class:: incremental @@ -38,52 +38,61 @@ eNanoMapper toxicity endpoints .. class:: incremental -- Toxicity endpoints: 41 -- Toxicity endpoints with more than one measurement value: 22 -- Toxicity endpoints with more than 10 measurements: 2 + - Toxicity endpoints: 41 + - Toxicity endpoints with more than one measurement value: 22 + - Toxicity endpoints with more than 10 measurements: 2 Selected data ============= Protein corona dataset Au particles (106 particles) -Toxicity endpoint: +Toxicity endpoint: Net cell association (A549 cell line) Read across procedure ===================== .. class:: incremental -- Identify relevant fragments (significant correlation with toxicity) - TODO list of fragments, number -- Calculate similarities (weighted cosine similarity, correlation coefficients = weights) -- Identify neighbors (particles with more than 0.95 similarity) -- Calculate prediction (weighted average from neighbors, similarities = weights) + - Identify relevant properties (statistically significant correlation with toxicity: 14 from 30 properties) + - Calculate similarities (weighted cosine similarity with correlation coefficients as weights) + - Identify neighbors (particles with similarity > 0.95) + - Calculate prediction (weighted average from neighbors with similarities as weights) -Future development -================== + Algorithms for feature selection, similarity calculation and predictions may change in the future. + +Future development (I) +====================== - Validation of predictions - Applicability domain/reliability of predictions - Accuracy improvements: + - additional data - feature selection - similarity calculation - predictions (local regression models) +Future development (I) +====================== + - Usability improvements: + - additional data (extension of applicability domain, additional endpoints and chemistries) - inclusion of ontologies - - descriptor calculation directly from core and coating chemistries + - inclusion of protein corona characterisation? + - particle characterisation without experimental data -Webinterface -============ + - descriptor calculation from core and coating chemistries + - ontological descriptors -https://nano-lazar.in-silico.ch/predict +nano-lazar +===================== -Your recommendations? +:Webinterface: https://nano-lazar.in-silico.ch/predict +:Presentation: https://nano-lazar.in-silico.ch/predict/enm-workshop.html +:Source code: https://github.com/enanomapper/nano-lazar-gui +:Issues: https://github.com/enanomapper/nano-lazar-gui/issues -Source code -=========== +Your comments, ideas, recommendations? -https://github.com/opentox/nano-lazar |