title: Towards predicting protein-protein interactions in novel organisms.
abstract: Machine learning methods are often used to predict Protein-Protein Interactions (PPI). It is common to develop methods using known PPI from well-characterised reference organisms, drawing from that organism data for inferring a predictive model and evaluating the model. We present evidence that this practice does not give a meaningful indication of the model's performance on genetically distinct organisms. We conclude that this practice cannot be applied to proteins inferred from the genetic sequence of a novel organism for which no PPI data is available, and that there is need for evaluating such methods on organisms distinct from their training organisms.
authors.affiliation: Department of Computer Science, University of Massachusetts Lowell, One University Avenue, Lowell, MA 01854, USA. pshaughn@cs.uml.edu
authors.forename: Patrick
authors.initials: P
authors.lastname: Shaughnessy
authors.forename: Gary
authors.initials: G
authors.lastname: Livingston
authors.forename: Michael V
authors.initials: MV
authors.lastname: Graves
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000235:genetics / Q000378:metabolism; D001185:Artificial Intelligence; D019295:Computational Biology; D003198:Computer Simulation; D030562:Databases, Protein; D019288:Herpesvirus 8, Human / Q000235:genetics / Q000378:metabolism; D017862:Phycodnaviridae / Q000235:genetics / Q000378:metabolism; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D040901:Proteomics / Q000706:statistics & numerical data*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D013045:Species Specificity; D014764:Viral Proteins / Q000737:chemistry / Q000235:genetics / Q000378:metabolism
chemical_list: D014764:Viral Proteins