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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 (106 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/predict/enm-workshop.html
:Source code: https://github.com/enanomapper/nano-lazar-gui
:Issues: https://github.com/enanomapper/nano-lazar-gui/issues
Your comments, ideas, recommendations?
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