Pregled bibliografske jedinice broj: 826451
Estimation of chance accuracy in classification structure-property models
Estimation of chance accuracy in classification structure-property models // Math/Chem/Comp 2016, 28th MC2 Conference, Book of Abstracts / Vančik, Hrvoj ; Cioslowski, Jerzy (ur.).
Zagreb: -, 2016. str. 13-13 (poster, međunarodna recenzija, sažetak, znanstveni)
CROSBI ID: 826451 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Estimation of chance accuracy in classification
structure-property models
Autori
Batista, Jadranko ; Lučić, Bono
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni
Izvornik
Math/Chem/Comp 2016, 28th MC2 Conference, Book of Abstracts
/ Vančik, Hrvoj ; Cioslowski, Jerzy - Zagreb, 2016, 13-13
Skup
Math/Chem/Comp 2016, 28th MC2 Conference
Mjesto i datum
Dubrovnik, Hrvatska, 20.06.2016. - 26.06.2016
Vrsta sudjelovanja
Poster
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
classification model ; two-state model ; structure-property model ; structure prediction ; chance correlation ; membrane protein ; secondary structure
Sažetak
For each model, including classification structure-property models related to small molecules or proteins, it is possible to calculate (or to estimate by simulations) the level of accuracy is obtained with randomly generated data or by a random model. However, in generation of random data one must ensure that random data have structure and distribution similar to those of real inputs. We present the analysis of several sets of data relating to the modeling/prediction of (1) the secondary structure of membrane proteins (based on their primary structure) and (2) relationship between the structure and properties of small molecules. All these problems have two or more classes, i.e. defined and experimentally determined groups. Obtained results show that the accuracy which is obtained by chance (with randomly generated data, and/or by a random model), is determined to a large extent by the distribution of experimental input data (classes) and by the (im)partiality of the model (the total numbers of individual classes predicted by impartial model are almost the same as those in experimental data).
Izvorni jezik
Engleski
Znanstvena područja
Fizika, Kemija, Biologija
POVEZANOST RADA
Projekti:
MZO-Croatia - basic grant
Zaklada HAZU
MZOS-098-1770495-2919 - Razvoj metoda za modeliranje svojstava bioaktivnih molekula i proteina (Lučić, Bono, MZOS ) ( CroRIS)
Ustanove:
Institut "Ruđer Bošković", Zagreb
Profili:
Bono Lučić (autor)