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Pregled bibliografske jedinice broj: 672546

Semi-Supervised Learning for Quantitative Structure- Activity Modeling


Levatić, Jurica; Džeroski, Sašo; Supek, Fran; Šmuc, Tomislav.
Semi-Supervised Learning for Quantitative Structure- Activity Modeling // Informatica (Ljubljana), 37 (2013), 2; 173-179 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 672546 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Semi-Supervised Learning for Quantitative Structure- Activity Modeling

Autori
Levatić, Jurica ; Džeroski, Sašo ; Supek, Fran ; Šmuc, Tomislav.

Izvornik
Informatica (Ljubljana) (0350-5596) 37 (2013), 2; 173-179

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
semi-supervised learning ; supervised learning ; QSAR ; drug design ; machine learning

Sažetak
In this study, we compare the performance of semi- supervised and supervised machine learning methods applied to various problems of modeling quantitative Structure Activity Relationship (QSAR) in sets of chemical compounds. Semi- supervised learning utilizes unlabeled data in addition to labeled data with the goal of building better predictive models than can be learned by using labeled data alone. Typically, labeled QSAR datasets contain tens to hundreds of compounds, while unlabeled data are easily accessible via public databases containing thousands of chemical compounds: this makes QSAR modeling an attractive domain for the application of semi-supervised learning. We tested four different semi-supervised learning algorithms on three different datasets and compared them to five commonly used supervised learning algorithms. While adding unlabeled data does help for certain pairings of dataset and method, semi-supervised learning is not clearly superior to supervised learning across the QSAR classification problems addressed by this study.

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Računarstvo



POVEZANOST RADA


Projekti:
MZOS-098-0000000-3168 - Strojno učenje prediktivnih modela u računalnoj biologiji (Šmuc, Tomislav, MZOS ) ( POIROT)

Ustanove:
Prirodoslovno-matematički fakultet, Matematički odjel, Zagreb,
Institut "Ruđer Bošković", Zagreb,
Prirodoslovno-matematički fakultet, Zagreb

Profili:

Avatar Url Tomislav Šmuc (autor)

Avatar Url Fran Supek (autor)

Citiraj ovu publikaciju

Levatić, Jurica; Džeroski, Sašo; Supek, Fran; Šmuc, Tomislav.
Semi-Supervised Learning for Quantitative Structure- Activity Modeling // Informatica (Ljubljana), 37 (2013), 2; 173-179 (međunarodna recenzija, članak, znanstveni)
Levatić, J., Džeroski, S., Supek, F. & Šmuc, T. (2013) Semi-Supervised Learning for Quantitative Structure- Activity Modeling. Informatica (Ljubljana), 37 (2), 173-179.
@article{article, year = {2013}, pages = {173-179}, keywords = {semi-supervised learning, supervised learning, QSAR, drug design, machine learning}, journal = {Informatica (Ljubljana)}, volume = {37}, number = {2}, issn = {0350-5596}, title = {Semi-Supervised Learning for Quantitative Structure- Activity Modeling}, keyword = {semi-supervised learning, supervised learning, QSAR, drug design, machine learning} }
@article{article, year = {2013}, pages = {173-179}, keywords = {semi-supervised learning, supervised learning, QSAR, drug design, machine learning}, journal = {Informatica (Ljubljana)}, volume = {37}, number = {2}, issn = {0350-5596}, title = {Semi-Supervised Learning for Quantitative Structure- Activity Modeling}, keyword = {semi-supervised learning, supervised learning, QSAR, drug design, machine learning} }

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Emerging Sources Citation Index (ESCI)
  • Scopus


Uključenost u ostale bibliografske baze podataka:


  • Compendex (EI Village)
  • INSPEC





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