From b614689b3c293be1cbd566f28996e6c3a41f70a3 Mon Sep 17 00:00:00 2001 From: gebele Date: Wed, 9 Aug 2017 12:31:27 +0000 Subject: deleted obsolete files --- presentation/enm-workshop.html | 445 ----------------------------------------- 1 file changed, 445 deletions(-) delete mode 100644 presentation/enm-workshop.html (limited to 'presentation/enm-workshop.html') diff --git a/presentation/enm-workshop.html b/presentation/enm-workshop.html deleted file mode 100644 index f0c6781..0000000 --- a/presentation/enm-workshop.html +++ /dev/null @@ -1,445 +0,0 @@ - - - - - - - -Read across toxicity predictions with nano-lazar - - - - - - - - - - - - - - -
-
-
- - -
-
-
-

Read across toxicity predictions with nano-lazar

- -

Christoph Helma

-

in silico toxicology gmbh

-http://www.enanomapper.net/sites/all/themes/theme807/logo.png - -
-
-

Requirements

-
    -
  • Nanoparticle characterisation
  • -
  • Toxicity measurements
  • -
-
-
-

eNanoMapper data import

-
    -
  • Nanoparticles imported: 464
  • -
  • Nanoparticles with particle characterisation: 394
  • -
  • Nanoparticles with toxicity data: 167
  • -
  • Nanoparticles with toxicity data and particle characterisation: 160
  • -
-
-
-

eNanoMapper toxicity endpoints

-
    -
  • 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:

-
-
-

Read across procedure

-
    -
  • 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)
  • -
-
-
-

Future development

-
    -
  • Validation of predictions
  • -
  • Applicability domain/reliability of predictions
  • -
  • Accuracy improvements: -- additional data -- feature selection -- similarity calculation -- predictions (local regression models)
  • -
  • Usability improvements: -- additional data (extension of applicability domain, additional endpoints and chemistries) -- inclusion of ontologies -- descriptor calculation directly from core and coating chemistries
  • -
-
-
-

Webinterface

-

https://nano-lazar.in-silico.ch/predict

-

Your recommendations?

-
- -
- - -- cgit v1.2.3