Partial least square and k‐nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity. (9th April 2014)
- Record Type:
- Journal Article
- Title:
- Partial least square and k‐nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity. (9th April 2014)
- Main Title:
- Partial least square and k‐nearest neighbor algorithms for improved 3D quantitative spectral data–activity relationship consensus modeling of acute toxicity
- Authors:
- Stoyanova‐Slavova, Iva B.
Slavov, Svetoslav H.
Pearce, Bruce
Buzatu, Dan A.
Beger, Richard D.
Wilkes, Jon G. - Abstract:
- <abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <sec id="etc2534-sec-0001" sec-type="section"> <p>A diverse set of 154 chemicals that included US Food and Drug Administration–regulated compounds tested for their aquatic toxicity in <italic>Daphnia magna</italic> were modeled by a 3‐dimensional quantitative spectral data–activity relationship (3D‐QSDAR). Two distinct algorithms, partial least squares (PLS) and Tanimoto similarity‐based k‐nearest neighbors (KNN), were used to process bin occupancy descriptor matrices obtained after tessellation of the 3D‐QSDAR space into regularly sized bins. The performance of models utilizing bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å was explored. Rigorous quality‐control criteria were imposed: 1) 100 randomized 20% hold‐out test sets were generated and the average <italic>R</italic><sup>2</sup><sub>test</sub> of the respective models was used as a measure of their performance, and 2) a Y‐scrambling procedure was used to identify chance correlations. A consensus between the best‐performing composite PLS model using 0.5 Å × 14 ppm × 14 ppm bins and 10 latent variables (average <italic>R</italic><sup>2</sup><sub>test</sub> = 0.770) and the best composite KNN model using 0.5 Å × 8 ppm × 8 ppm and 2 neighbors (average <italic>R</italic><sup>2</sup><sub>test</sub> = 0.801) offered an improvement of about 7.5% (<italic>R</italic><sup>2</sup><sub>test consensus</sub> = 0.845). Projection of the<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <sec id="etc2534-sec-0001" sec-type="section"> <p>A diverse set of 154 chemicals that included US Food and Drug Administration–regulated compounds tested for their aquatic toxicity in <italic>Daphnia magna</italic> were modeled by a 3‐dimensional quantitative spectral data–activity relationship (3D‐QSDAR). Two distinct algorithms, partial least squares (PLS) and Tanimoto similarity‐based k‐nearest neighbors (KNN), were used to process bin occupancy descriptor matrices obtained after tessellation of the 3D‐QSDAR space into regularly sized bins. The performance of models utilizing bins ranging in size from 2 ppm × 2 ppm × 0.5 Å to 20 ppm × 20 ppm × 2.5 Å was explored. Rigorous quality‐control criteria were imposed: 1) 100 randomized 20% hold‐out test sets were generated and the average <italic>R</italic><sup>2</sup><sub>test</sub> of the respective models was used as a measure of their performance, and 2) a Y‐scrambling procedure was used to identify chance correlations. A consensus between the best‐performing composite PLS model using 0.5 Å × 14 ppm × 14 ppm bins and 10 latent variables (average <italic>R</italic><sup>2</sup><sub>test</sub> = 0.770) and the best composite KNN model using 0.5 Å × 8 ppm × 8 ppm and 2 neighbors (average <italic>R</italic><sup>2</sup><sub>test</sub> = 0.801) offered an improvement of about 7.5% (<italic>R</italic><sup>2</sup><sub>test consensus</sub> = 0.845). Projection of the most frequently occurring bins on the standard coordinate space indicated that the presence of a primary or secondary amino group—substituted aromatic systems—would result in an increased toxic effect in <italic>Daphnia</italic>. The presence of a second aromatic ring with highly electronegative substituents 5 Å to 7 Å apart from the first ring would lead to a further increase in toxicity. <italic>Environ Toxicol Chem</italic> 2014;33:1271–1282. © 2014 SETAC</p> </sec> </abstract> … (more)
- Is Part Of:
- Environmental toxicology and chemistry. Volume 33:Number 6(2014:Jun.)
- Journal:
- Environmental toxicology and chemistry
- Issue:
- Volume 33:Number 6(2014:Jun.)
- Issue Display:
- Volume 33, Issue 6 (2014)
- Year:
- 2014
- Volume:
- 33
- Issue:
- 6
- Issue Sort Value:
- 2014-0033-0006-0000
- Page Start:
- 1271
- Page End:
- 1282
- Publication Date:
- 2014-04-09
- Subjects:
- Pollution -- Environmental aspects -- Periodicals
Environmental chemistry -- Periodicals
615.902 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1552-8618 ↗
http://www.setacjournals.org/perlserv/?request=get-archive&issn=1552-8618 ↗
http://onlinelibrary.wiley.com/ ↗
http://firstsearch.oclc.org ↗ - DOI:
- 10.1002/etc.2534 ↗
- Languages:
- English
- ISSNs:
- 0730-7268
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3791.785000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 3499.xml