Sammanfattning

Protein-protein interactions are important for the majority of biological processes. A significant number of computational methods have been developed to predict protein-protein interactions using proteins' sequence, structural and genomic data. Hence, this fact motivated us to perform a comparative study of various machine learning methods, training them on the set of known protein-protein interactions, using proteins' global and local attributes. The results of the classifiers were evaluated through cross-validation and several performance measures were computed. It was noticed from the results that support vector machine outperformed other classifiers. This fact has also been established through statistical test, called Wilcoxon rank sum test, at 5% significance level.

Publicerad i

Advances in Intelligent Systems and Computing
2014, Volym: 242, nummer: 242, sidor: 211-218
Titel: Man-Machine Interactions 3. Advances in Intelligent Systems and Computing
ISBN: 978-3-319-02308-3
Utgivare: Springer International Publishing Switzerland 2014

SLU författare

• Klingström, Tomas

  • Institutionen för husdjurens biovetenskaper, Sveriges lantbruksuniversitet
    

• Forsberg, Simon

  • Institutionen för kliniska vetenskaper, Sveriges lantbruksuniversitet
    

• Kierczak, Marcin

  • Institutionen för kliniska vetenskaper, Sveriges lantbruksuniversitet
    

UKÄ forskningsämne

Bioinformatik och systembiologi
Genetik och förädling
Bioinformatik (beräkningsbiologi)


Publikationens identifierare

DOI: https://doi.org/10.1007/978-3-319-02309-0_22

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/51961