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Pregled bibliografske jedinice broj: 307366

Enhanced analytical power of SDS-PAGE using machine learning algorithms


Supek, Fran; Peharec, Petra; Krsnik-Rasol, Marijana; Šmuc, Tomislav
Enhanced analytical power of SDS-PAGE using machine learning algorithms // Proteomics, 8 (2008), 1; 28-31 doi:10.1002/pmic.200700555 (međunarodna recenzija, članak, znanstveni)


Naslov
Enhanced analytical power of SDS-PAGE using machine learning algorithms

Autori
Supek, Fran ; Peharec, Petra ; Krsnik-Rasol, Marijana ; Šmuc, Tomislav

Izvornik
Proteomics (1615-9853) 8 (2008), 1; 28-31

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

Ključne riječi
Support vector machines; principal component analysis; one dimensional gel electrophoresis; data mining; differential protein expression

Sažetak
We aim to demonstrate that a complex plant tissue protein mixture can be reliably 'fingerprinted’ by running conventional one-dimensional SDS-PAGE in bulk and analyzing gel banding patterns using machine learning methods. An unsupervised approach to filter noise and systemic biases (principal component analysis) was coupled to state-of-the-art supervised methods for classification (support vector machines) and attribute ranking (ReliefF) to improve tissue discrimination, visualization and recognition of important gel regions.

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Računarstvo, Biotehnologija



POVEZANOST RADA


Projekt / tema
098-0000000-3168 - Strojno učenje prediktivnih modela u računalnoj biologiji (Tomislav Šmuc, )
119-1191196-1200 - Diferencijalna ekspresija proteina u biljnim stanicama (Biljana Balen, )

Ustanove
Institut "Ruđer Bošković", Zagreb,
Prirodoslovno-matematički fakultet, Zagreb

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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