Systematic comparison of the proteinprotein interaction databases from a users perspective Abstract   In absence of periodic systematic comparisons, biologistsbioinformaticians may be forced to make a subjective selection among the many proteinprotein interaction (PPI) databases and tools. We conducted a comprehensive compilation and comparison of such resources. We compiled 375 PPI resources, shortlisted 125 important ones (both lists are available at startbioinfo.com), and compared the features and coverage of 16 carefullyselected databases related to human PPIs. We quantitatively compared the coverage of u0027experimentally verifiedu0027 as well as u0027totalu0027 (experimentally verified as well as predicted) PPIs for these 16 databases. Coverage was compared in two ways: (a) PPIs obtained in response to gene queries using the web interfaces were compared. As a query set, 108 genes expressed differently across tissues (specific to kidney, testis, and uterus, and ubiquitous  i.e., expressed in 43 human normal tissues) or associated with certain diseases (breast cancer, lung cancer, Alzheimerxe2x80x99s, cystic fibrosis, diabetes, and cardiomyopathy) were chosen. The coverage was also compared for the wellstudied genes versus the lessstudied ones. The coverage of the databases for highquality interactions was separately assessed using a set of literature curated experimentallyproven PPIs (gold standard PPIset); (b) the backenddata from 15 PPI databases was downloaded and compared. Combined results from STRING and UniHI covered around 84% of xe2x80x98experimentally verifiedxe2x80x99 PPIs. Approximately 94% of the xe2x80x98totalxe2x80x99 PPIs available across the databases were retrieved by the combined use of hPRINT, STRING, and IID. Among the experimentally verified PPIs found exclusively in each database, STRING contributed around 71% of the hits. The coverage of certain databases was skewed for some genetypes. Analysis with the goldstandard PPIset revealed that GPSProt, STRING, APID, and HIPPIE, each covered xe2x88xbc70% of the curated interactions. The database usage frequencies did not always correlate with their respective advantages, thereby justifying the need for more frequent studies of this nature.

Predicting Missing and Spurious ProteinProtein Interactions Using Graph Embeddings on GO Annotation Graph Proteinprotein interaction (PPI) prediction is a key step towards many bioinformatics applications including prediction of protein functions and drugdisease interactions. However, previous research on PPI prediction rarely considered missing and spurious interactions in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a novel method that employs graph embeddings to learn vector representations from constructed Gene Ontology (GO) annotation graphs. Our method leverages the information from both termterm relations among GO terms and termprotein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. We compare our method with methods based on information content and on word embeddings, using three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods.