Pretražite po imenu i prezimenu autora, mentora, urednika, prevoditelja

Napredna pretraga

Pregled bibliografske jedinice broj: 42868

CROMRSEL-s: efficient algorithms for the selection of most important variables in the QSAR modeling


Lučić, Bono; Trinajstić, Nenad
CROMRSEL-s: efficient algorithms for the selection of most important variables in the QSAR modeling // Rational Approaches to Drug Design, Abstract Book / Hoeltje, Hans-Dieter ; Sippl, Wolfgang (ur.).
Düsseldorf: Heinrich-Heine-Universitat, 2000. str. 71-71 (poster, međunarodna recenzija, sažetak, znanstveni)


CROSBI ID: 42868 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
CROMRSEL-s: efficient algorithms for the selection of most important variables in the QSAR modeling
(CROMRSEL-s : efficient algorithms for the selection of most important variables in the QSAR modeling)

Autori
Lučić, Bono ; Trinajstić, Nenad

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Rational Approaches to Drug Design, Abstract Book / Hoeltje, Hans-Dieter ; Sippl, Wolfgang - Düsseldorf : Heinrich-Heine-Universitat, 2000, 71-71

Skup
13th European Symposium on Quantitative-Structure-Activity Relationships

Mjesto i datum
Düsseldorf, Njemačka, 27.08.2000. - 01.09.2000

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
variable selection; multiregression; neural network; modeling

Sažetak
CROMRsel is a suite of programs for variables selection based on the multiregression (MR) models. Three algorithms were developed: (1) for the selection of the best possible MR models containing I descriptors selected from the total number of N descriptors; (2) for the stepwise 'i by i' selection (usually, i =1-4) - starting from the small initial set of descriptors (selected by algorithm 1), in each next step i descriptors were added to those selected in the previous step; (3) variant of (2) in which N models containing I descriptors were generated in such a way that stepwise selection starts from each of N descriptors and, finally, the best among N models is chosen. For the cases in which N!/(N-I)!×I! < 1010 algorithm (1) and, otherwise, algorithms (2) and (3) was used. CROMRsel algorithms were applied to several data sets (studied by NNs, PLS, PCA, genetic algorithms or classical regression) from medicinal, analytical and physical chemistry, and, in all cases, we obtained significantly better models.

Izvorni jezik
Engleski

Znanstvena područja
Kemija



POVEZANOST RADA


Projekti:
00980606

Ustanove:
Institut "Ruđer Bošković", Zagreb

Profili:

Avatar Url Bono Lučić (autor)

Avatar Url Nenad Trinajstić (autor)


Citiraj ovu publikaciju:

Lučić, Bono; Trinajstić, Nenad
CROMRSEL-s: efficient algorithms for the selection of most important variables in the QSAR modeling // Rational Approaches to Drug Design, Abstract Book / Hoeltje, Hans-Dieter ; Sippl, Wolfgang (ur.).
Düsseldorf: Heinrich-Heine-Universitat, 2000. str. 71-71 (poster, međunarodna recenzija, sažetak, znanstveni)
Lučić, B. & Trinajstić, N. (2000) CROMRSEL-s: efficient algorithms for the selection of most important variables in the QSAR modeling. U: Hoeltje, H. & Sippl, W. (ur.)Rational Approaches to Drug Design, Abstract Book.
@article{article, author = {Lu\v{c}i\'{c}, Bono and Trinajsti\'{c}, Nenad}, year = {2000}, pages = {71-71}, keywords = {variable selection, multiregression, neural network, modeling}, title = {CROMRSEL-s: efficient algorithms for the selection of most important variables in the QSAR modeling}, keyword = {variable selection, multiregression, neural network, modeling}, publisher = {Heinrich-Heine-Universitat}, publisherplace = {D\"{u}sseldorf, Njema\v{c}ka} }
@article{article, author = {Lu\v{c}i\'{c}, Bono and Trinajsti\'{c}, Nenad}, year = {2000}, pages = {71-71}, keywords = {variable selection, multiregression, neural network, modeling}, title = {CROMRSEL-s : efficient algorithms for the selection of most important variables in the QSAR modeling}, keyword = {variable selection, multiregression, neural network, modeling}, publisher = {Heinrich-Heine-Universitat}, publisherplace = {D\"{u}sseldorf, Njema\v{c}ka} }




Contrast
Increase Font
Decrease Font
Dyslexic Font