Pregled bibliografske jedinice broj: 42868
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 // 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)
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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