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+.. |date| date::
+
+
+=============================================================
+Read across toxicity predictions with nano-lazar
+=============================================================
+
+.. class:: center
+
+  Christoph Helma, Denis Gebele, Micha Rautenberg
+
+  in silico toxicology gmbh
+
+  .. image:: http://www.enanomapper.net/sites/all/themes/theme807/logo.png
+    :align: center
+
+Requirements for nanoparticle read-across
+=========================================
+
+.. class:: incremental
+
+  - Nanoparticle characterisation
+  - Toxicity measurements
+
+eNanoMapper particle characterisation
+=====================================
+
+.. class:: incremental
+
+  - Nanoparticles imported: 464 
+  - Nanoparticles with particle characterisation: 394 
+  - Nanoparticles with toxicity data: 167 
+  - Nanoparticles with toxicity data and particle characterisation: 160
+
+
+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
+
+Selected data
+=============
+
+Protein corona dataset Au particles (105 particles)
+Toxicity endpoint: Net cell association (A549 cell line)
+
+Read across procedure
+=====================
+
+.. class:: incremental
+
+  - 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)
+
+  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
+  - inclusion of protein corona characterisation?
+  - particle characterisation without experimental data
+
+    - descriptor calculation from core and coating chemistries
+    - ontological descriptors 
+
+nano-lazar
+=====================
+
+:Webinterface: https://nano-lazar.in-silico.ch/predict
+:Presentation: https://nano-lazar.in-silico.ch/enm-workshop.html
+:Source code: https://github.com/enanomapper/nano-lazar
+:Issues: https://github.com/enanomapper/nano-lazar/issues
+
+Your comments, ideas, recommendations?
+