Figure 1. Illustration of aggregation functions on the local network of 1-decene. 1-decene is marked as target. ... Figure 1. Illustration of aggregation.

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Presentation transcript:

Figure 1. Illustration of aggregation functions on the local network of 1-decene. 1-decene is marked as target. ... Figure 1. Illustration of aggregation functions on the local network of 1-decene. 1-decene is marked as target. Positive and small dots indicates analogs that are positive for a modeled endpoint. Negative indicates analogs that are negative for the modeled endpoint. The table illustrates well known aggregation functions. Data Fusion aggregation function not given. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of ToxicologyThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Toxicol Sci, Volume 165, Issue 1, 11 July 2018, Pages 198–212, https://doi.org/10.1093/toxsci/kfy152 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 2. Force layout graph of 10 million chemicals Figure 2. Force layout graph of 10 million chemicals. A, where each dot represents a chemical and their distance ... Figure 2. Force layout graph of 10 million chemicals. A, where each dot represents a chemical and their distance reflects chemical similarity, calculated by the number of PubChem 2D features shared by 2 chemicals divided by the total number of PubChem 2D features in both compounds (Jaccard similarity). B–D, Step-wise zooming in, where the frame indicates the area shown in the next graph, until in D, individual chemicals are seen with their similarity connections as gray lines, whose length represents % similarity. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of ToxicologyThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Toxicol Sci, Volume 165, Issue 1, 11 July 2018, Pages 198–212, https://doi.org/10.1093/toxsci/kfy152 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 3. Workflow of the presented studies Figure 3. Workflow of the presented studies. A, OECD Reproducibility is evaluated via conditional probabilities ... Figure 3. Workflow of the presented studies. A, OECD Reproducibility is evaluated via conditional probabilities generated from repeated test pairs found in ECHA dossiers. B, A Simple RASAR built from ECHA C&L data is evaluated in cross-validation. C, A Data Fusion RASAR built from ECHA C&L data is evaluated in cross-validation. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of ToxicologyThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Toxicol Sci, Volume 165, Issue 1, 11 July 2018, Pages 198–212, https://doi.org/10.1093/toxsci/kfy152 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 4. Illustration of the closest positive and negative neighbor approach for 1-DECENE. The graph shows chemicals ... Figure 4. Illustration of the closest positive and negative neighbor approach for 1-DECENE. The graph shows chemicals with similarity > 0.9 according to PubChem 2D Tanimoto. The RASAR uses similarity to the closest Positive (large positive node—1, 7-OCTADIENE) and closest Negative (large negative node—MYRCENE) along with other features to characterize a local similarity space. All small nodes here are positives. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of ToxicologyThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Toxicol Sci, Volume 165, Issue 1, 11 July 2018, Pages 198–212, https://doi.org/10.1093/toxsci/kfy152 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 5. Proximity to negative and positive neighbors and probability of skin sensitization. These graphs show how ... Figure 5. Proximity to negative and positive neighbors and probability of skin sensitization. These graphs show how skin sensitizers and nonsensitizers distribute over features describing the closest negative and positive chemicals. A, shows how proximity to closest negative (MaxNeg<sup>3</sup>) and positive neighbor (MaxPos<sup>3</sup>) distribute for actually toxic (red) and non-toxic (green) chemicals. B, The associated probability for a positive classification (color gradient from green as low probability for a toxic to red for high probability of toxic property). (C, D) 2D histograms for negative (C) and positive (D) chemicals. In (C), hexes to the upper left of the red line are correct classifications. In (D), classifications to the lower right of the red line are correct classifications. Brighter hexes indicate more chemicals with the given feature values. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of ToxicologyThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Toxicol Sci, Volume 165, Issue 1, 11 July 2018, Pages 198–212, https://doi.org/10.1093/toxsci/kfy152 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 6. Distribution of sensitizers/nonsensitizers over Simple RASAR hazard estimates. The upper figure (A) is a ... Figure 6. Distribution of sensitizers/nonsensitizers over Simple RASAR hazard estimates. The upper figure (A) is a histogram counting the number of ± chemicals receiving different probabilistic estimates (2.5% increments). The lower figure (B) shows the percentage of ± chemicals at each hazard probability estimate. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of ToxicologyThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Toxicol Sci, Volume 165, Issue 1, 11 July 2018, Pages 198–212, https://doi.org/10.1093/toxsci/kfy152 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 7. Modeling of sufficiently close neighbor availability with increasing number of chemicals with data. Two ... Figure 7. Modeling of sufficiently close neighbor availability with increasing number of chemicals with data. Two substance lists of 33 383 substances (European Inventory of Existing Commercial Chemical Substances [EINECS]), representing here chemicals with no data, and 1387 chemicals (Annex VI of the REACH legislation) are used, representing chemicals with labels. Please note that these are used here only as random lists of chemicals with CAS numbers. EINECS compounds are represented in blue and ANNEX VI Table 3.1 compounds are in red. At start, none of the 33 383 has neighbors with data. Choosing randomly an increasing number from the 1387-chemcial list, more and more chemicals find neighbors indicated by the contraction of dots linked by Jaccard similarities. We use a minimum similarity of 70% in these figures. The number of neighbors is symbolized by the size of red dots. Edges represent similarities between EINECS compounds and Annex compounds. These visualizations are made with the aid of Gephi graph visualization software. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of ToxicologyThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Toxicol Sci, Volume 165, Issue 1, 11 July 2018, Pages 198–212, https://doi.org/10.1093/toxsci/kfy152 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 8. Select features contributing to the Data Fusion RASAR prediction. The most important information sources in ... Figure 8. Select features contributing to the Data Fusion RASAR prediction. The most important information sources in the data fusion approach for 9 hazards are shown. The length of each bar shows the relative importance of the feature (given on the left) towards prediction of the hazard (given at top). Row names take the form <feature>_T for a feature describing the target compound, <feature>_PosAna for a feature describing distance to closest negative analog, and <feature>_NegAna for a feature describing similarity to the closest negative analog. All relative contributions are given as Supplementary Table 1. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the Society of ToxicologyThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Toxicol Sci, Volume 165, Issue 1, 11 July 2018, Pages 198–212, https://doi.org/10.1093/toxsci/kfy152 The content of this slide may be subject to copyright: please see the slide notes for details.