Techniques to cope with missing data in host–pathogen protein interaction prediction | Zendy

Meghana Kshirsagar | Zendy; Jaime G. Carbonell | Zendy; Judith KleinSeetharaman | Zendy

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Techniques to cope with missing data in host–pathogen protein interaction prediction

Author(s) -

Meghana Kshirsagar,

Jaime G. Carbonell,

Judith KleinSeetharaman

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts375

Subject(s) - computer science , missing data , machine learning , generality , artificial intelligence , imputation (statistics) , data mining , predictive modelling , psychology , psychotherapist

Approaches that use supervised machine learning techniques for protein-protein interaction (PPI) prediction typically use features obtained by integrating several sources of data. Often certain attributes of the data are not available, resulting in missing values. In particular, our host-pathogen PPI datasets have a large fraction, in the range of 58-85% of missing values, which makes it challenging to apply machine learning algorithms.

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