diff options
Diffstat (limited to 'loael.md')
| -rw-r--r-- | loael.md | 14 |
1 files changed, 6 insertions, 8 deletions
@@ -20,7 +20,7 @@ abstract: | better than random guessing, but the errors to be expected are higher and a manual inspection of prediction results is highly recommended. -documentclass: article +documentclass: achemso bibliography: references.bibtex figPrefix: Figure eqnPrefix: Equation @@ -287,7 +287,7 @@ optimizing the number of RF components by bootstrap resampling. Finally the local RF model is applied to [predict the activity](https://github.com/opentox/lazar/blob/loael-paper.submission/lib/model.rb#L194-L272) of the query compound. The RMSE of bootstrapped local model predictions is used -to construct 95\% prediction intervals at 1.96*RMSE. +to construct 95\% prediction intervals at 1.96*RMSE. The width of the prediction interval indicates the expected prediction accuracy. The "true" value of a prediction should be with 95\% probability within the prediction interval. If RF modelling or prediction fails, the program resorts to using the [weighted mean](https://github.com/opentox/lazar/blob/loael-paper.submission/lib/regression.rb#L6-L16) @@ -609,8 +609,7 @@ experimental variability [@LoPiparo2014]. In the present study, a similar approach was applied to build models generating quantitative predictions of long-term toxicity. Two databases compiling chronic oral rat lowest adverse effect levels (LOAEL) as endpoint were -available from different sources. <span id="dataset-comparison-1" -class="anchor"></span>Our investigations clearly indicated that the +available from different sources. Our investigations clearly indicated that the Nestlé and FSVO databases are very similar in terms of chemical structures and properties as well as distribution of experimental LOAEL values. The only significant difference that we observed was that the @@ -623,7 +622,7 @@ chemicals available in the training datasets had at least two independent studies/LOAELs. These studies were exploited to generate information on the reproducibility of chronic animal studies and were used to evaluate prediction performance of the models in the context of -experimental variability.Considerable variability in the experimental +experimental variability. Considerable variability in the experimental data was observed. Study design differences, including dose selection, dose spacing and route of administration are likely explanation of experimental variability. High experimental variability has an impact on @@ -632,8 +631,7 @@ quality by introducing noise into the training data, secondly it influences accuracy estimates because predictions have to be compared against noisy data where "true" experimental values are unknown. This will become obvious in the next section, where comparison of predictions -with experimental data is discussed.<span id="lazar-predictions" -class="anchor"></span>The data obtained in the present study indicate +with experimental data is discussed. The data obtained in the present study indicate that `lazar` generates reliable predictions for compounds within the applicability domain of the training data (i.e. predictions without warnings, which indicates a sufficient number of neighbors with @@ -713,7 +711,7 @@ where no predictions can be made, because there are no similar compounds in the and in such cases it is preferable to avoid predictions instead of random guessing. --> -Elena: Should we add a GUI screenshot? +TODO: GUI screenshot <!-- is covered in |