{"id":{"repo_id":"dundee","oai_identifier":"oai:discovery.dundee.ac.uk:studenttheses/697e465a-edbd-41d2-acda-5910a49e4157"},"canonical_url":"https://search.dev.ndltd.org/etd/dundee/oai:discovery.dundee.ac.uk:studenttheses/697e465a-edbd-41d2-acda-5910a49e4157","repository":{"repo_id":"dundee","name":"University of Dundee","base_url":"https://discovery.dundee.ac.uk/ws/oai"},"display":{"title":"Human protein-protein interaction prediction","abstract":"Protein-protein interactions are essential for the survival of all living cells, allowing for processes such as cell signalling, metabolism and cell division to occur. Yet in humans there are only >38k annotated interactions of an interactome estimated to range between 150k to 600k interactions and out of a potential 300M protein pairs.Experimental methods to define the human interactome generate high quality results, but are expensive and slow. Computational methods play an important role to fill the gap.To further this goal, the prediction of human protein-protein interactions was investigated by the development of new predictive modules and the analysis of diverse datasets within the framework of the previously established PIPs protein-protein interaction predictor Scott and Barton 2007. New features considered include the semantic similarity of Gene Ontology annotating terms, clustering of interaction networks, primary sequences and gene co-expression. Integrating the new features in a naive Bayesian manner as part of the PIPs 2 predictor resulted in two sets of predictions. With a conservative threshold, the union of both sets is >300k predicted human interactions with an intersect of >94k interactions, of which a subset have been experimentally validated. The PIPs 2 predictor is also capable of making predictions in organisms that have no annotated interactions. This is achieved by training the PIPs 2 predictor based on a set of evidence and annotated interactions in another organism resulting in a ranking of protein pairs in the original organism of interest. Such an approach allows for predictions to be made across the whole proteome of poorly characterised organism, rather than being limited only to proteins with known orthologues. The work described here has increased the coverage of the human interactome and introduced a method to predict interactions in organisms that have previously had limited or no annotated interactions. The thesis aims to provide a stepping stone towards the completion of the human interactome and a way of predicting interactions in organisms that have been less well studied, but are often clinically relevant.","abstract_html":"Protein-protein interactions are essential for the survival of all living cells, allowing for processes such as cell signalling, metabolism and cell division to occur. Yet in humans there are only >38k annotated interactions of an interactome estimated to range between 150k to 600k interactions and out of a potential 300M protein pairs.Experimental methods to define the human interactome generate high quality results, but are expensive and slow. Computational methods play an important role to fill the gap.To further this goal, the prediction of human protein-protein interactions was investigated by the development of new predictive modules and the analysis of diverse datasets within the framework of the previously established PIPs protein-protein interaction predictor Scott and Barton 2007. New features considered include the semantic similarity of Gene Ontology annotating terms, clustering of interaction networks, primary sequences and gene co-expression. Integrating the new features in a naive Bayesian manner as part of the PIPs 2 predictor resulted in two sets of predictions. With a conservative threshold, the union of both sets is >300k predicted human interactions with an intersect of >94k interactions, of which a subset have been experimentally validated. The PIPs 2 predictor is also capable of making predictions in organisms that have no annotated interactions. This is achieved by training the PIPs 2 predictor based on a set of evidence and annotated interactions in another organism resulting in a ranking of protein pairs in the original organism of interest. Such an approach allows for predictions to be made across the whole proteome of poorly characterised organism, rather than being limited only to proteins with known orthologues. The work described here has increased the coverage of the human interactome and introduced a method to predict interactions in organisms that have previously had limited or no annotated interactions. The thesis aims to provide a stepping stone towards the completion of the human interactome and a way of predicting interactions in organisms that have been less well studied, but are often clinically relevant.","abstract_has_math":false,"creators":["McDowall, Mark"],"institution":"University of Dundee","degree_name":"Doctor of Philosophy","degree_level":"Doctoral Thesis","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Barton, Geoffrey"],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-24T02:07:31Z","subjects":["Bioinformatics","Protein-Protein Interaction","Human","Network Analysis","PIPs"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:discovery.dundee.ac.uk:studenttheses/697e465a-edbd-41d2-acda-5910a49e4157"],"render_values":[{"text":"oai:discovery.dundee.ac.uk:studenttheses/697e465a-edbd-41d2-acda-5910a49e4157","href":null,"code":true}]}]},"links":{"outbound_url":"https://discovery.dundee.ac.uk/en/studentTheses/697e465a-edbd-41d2-acda-5910a49e4157","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Barton, Geoffrey"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["Biotechnology and Biological Sciences Research Council"]},{"key":"dc:creator","label":"Author","values":["McDowall, Mark"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011"]},{"key":"dc:date.issued","label":"Date","values":["2011"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Biological Chemistry and Drug Discovery"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Dundee"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://discovery.dundee.ac.uk/en/studentTheses/697e465a-edbd-41d2-acda-5910a49e4157"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral Thesis"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Bioinformatics","Protein-Protein Interaction","Human","Network Analysis","PIPs"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oai:discovery.dundee.ac.uk:studenttheses/697e465a-edbd-41d2-acda-5910a49e4157","https://discovery.dundee.ac.uk/en/studentTheses/697e465a-edbd-41d2-acda-5910a49e4157"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://discovery.dundee.ac.uk/files/1307338/McDowall_phd_2011.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Protein-protein interactions are essential for the survival of all living cells, allowing for processes such as cell signalling, metabolism and cell division to occur. Yet in humans there are only >38k annotated interactions of an interactome estimated to range between 150k to 600k interactions and out of a potential 300M protein pairs.Experimental methods to define the human interactome generate high quality results, but are expensive and slow. Computational methods play an important role to fill the gap.To further this goal, the prediction of human protein-protein interactions was investigated by the development of new predictive modules and the analysis of diverse datasets within the framework of the previously established PIPs protein-protein interaction predictor Scott and Barton 2007. New features considered include the semantic similarity of Gene Ontology annotating terms, clustering of interaction networks, primary sequences and gene co-expression. Integrating the new features in a naive Bayesian manner as part of the PIPs 2 predictor resulted in two sets of predictions. With a conservative threshold, the union of both sets is >300k predicted human interactions with an intersect of >94k interactions, of which a subset have been experimentally validated. The PIPs 2 predictor is also capable of making predictions in organisms that have no annotated interactions. This is achieved by training the PIPs 2 predictor based on a set of evidence and annotated interactions in another organism resulting in a ranking of protein pairs in the original organism of interest. Such an approach allows for predictions to be made across the whole proteome of poorly characterised organism, rather than being limited only to proteins with known orthologues. The work described here has increased the coverage of the human interactome and introduced a method to predict interactions in organisms that have previously had limited or no annotated interactions. The thesis aims to provide a stepping stone towards the completion of the human interactome and a way of predicting interactions in organisms that have been less well studied, but are often clinically relevant."]},{"key":"dc:title","label":"Title","values":["Human protein-protein interaction prediction"]}]}],"canonical_facts":{"dc:contributor.advisor":["Barton, Geoffrey"],"dc:contributor.sponsor":["Biotechnology and Biological Sciences Research Council"],"dc:creator":["McDowall, Mark"],"dc:date":["2011"],"dc:date.issued":["2011"],"dc:description.abstract":["Protein-protein interactions are essential for the survival of all living cells, allowing for processes such as cell signalling, metabolism and cell division to occur. 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