{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/195389"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/195389","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"IDENTIFYING AND EXPLOITING SYNTHETIC LETHALITY FOR CANCER THERAPEUTICS","abstract":"Synthetic Lethality (SL) is considered a promising approach for the development of personalized treatments. However, SL interactions in humans remain largely unknown and there is a need to discover more such pairs, especially those that are clinically actionable, and to develop tools that can enable us to gain deeper insights into their role in cancer and therapy development. In this thesis, new methods have been developed to discover SL pairs and to utilize SL pairs in biological network mining and clinical decision support. Two new methods are designed to identify potential SL pairs using multiple genomic data sources, based on statistical hypothesis testing and collective matrix factorization. A new algorithm to mine and analyse a network of pairwise interactions is presented, finally, an application in personalized treatment recommendations is explored that uses information on SL pairs and other public genomic databases by integrating them within an integer linear programming framework.","abstract_html":"Synthetic Lethality (SL) is considered a promising approach for the development of personalized treatments. However, SL interactions in humans remain largely unknown and there is a need to discover more such pairs, especially those that are clinically actionable, and to develop tools that can enable us to gain deeper insights into their role in cancer and therapy development. In this thesis, new methods have been developed to discover SL pairs and to utilize SL pairs in biological network mining and clinical decision support. Two new methods are designed to identify potential SL pairs using multiple genomic data sources, based on statistical hypothesis testing and collective matrix factorization. A new algorithm to mine and analyse a network of pairwise interactions is presented, finally, an application in personalized treatment recommendations is explored that uses information on SL pairs and other public genomic databases by integrating them within an integer linear programming framework.","abstract_has_math":false,"creators":["HERTY LIANY"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-04-16","date_published":"2021-04-16","updated_at":"2026-07-24T03:32:56Z","subjects":["synthetic lethality, cancer therapeutics, maximum weight bipartite subgraphs, collective matrix factorization, personalized drug recommendations, BPM"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["HERTY LIANY"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2021-04-16"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://scholarbank.nus.edu.sg/handle/10635/195389"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["synthetic lethality, cancer therapeutics, maximum weight bipartite subgraphs, collective matrix factorization, personalized drug recommendations, BPM"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarbank.nus.edu.sg/bitstreams/4ac5a6dc-e6d9-45c1-a028-398a554dfc17/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Synthetic Lethality (SL) is considered a promising approach for the development of personalized treatments. However, SL interactions in humans remain largely unknown and there is a need to discover more such pairs, especially those that are clinically actionable, and to develop tools that can enable us to gain deeper insights into their role in cancer and therapy development. In this thesis, new methods have been developed to discover SL pairs and to utilize SL pairs in biological network mining and clinical decision support. Two new methods are designed to identify potential SL pairs using multiple genomic data sources, based on statistical hypothesis testing and collective matrix factorization. A new algorithm to mine and analyse a network of pairwise interactions is presented, finally, an application in personalized treatment recommendations is explored that uses information on SL pairs and other public genomic databases by integrating them within an integer linear programming framework."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["34ca87da2cd1d5d52e9bd09998c1e6c2","59ba3844ed785bb2c40c3587ae8b00d1"]},{"key":"dc:title","label":"Title","values":["IDENTIFYING AND EXPLOITING SYNTHETIC LETHALITY FOR CANCER THERAPEUTICS"]}]}],"canonical_facts":{"dc:creator":["HERTY LIANY"],"dc:date.issued":["2021-04-16"],"dc:description.abstract":["Synthetic Lethality (SL) is considered a promising approach for the development of personalized treatments. However, SL interactions in humans remain largely unknown and there is a need to discover more such pairs, especially those that are clinically actionable, and to develop tools that can enable us to gain deeper insights into their role in cancer and therapy development. In this thesis, new methods have been developed to discover SL pairs and to utilize SL pairs in biological network mining and clinical decision support. Two new methods are designed to identify potential SL pairs using multiple genomic data sources, based on statistical hypothesis testing and collective matrix factorization. A new algorithm to mine and analyse a network of pairwise interactions is presented, finally, an application in personalized treatment recommendations is explored that uses information on SL pairs and other public genomic databases by integrating them within an integer linear programming framework."],"dc:format.checksum.md5":["34ca87da2cd1d5d52e9bd09998c1e6c2","59ba3844ed785bb2c40c3587ae8b00d1"],"dc:identifier.uri":["https://scholarbank.nus.edu.sg/bitstreams/4ac5a6dc-e6d9-45c1-a028-398a554dfc17/download"],"dc:relation.isreferencedby":["https://scholarbank.nus.edu.sg/handle/10635/195389"],"dc:subject":["synthetic lethality, cancer therapeutics, maximum weight bipartite subgraphs, collective matrix factorization, personalized drug recommendations, BPM"],"dc:title":["IDENTIFYING AND EXPLOITING SYNTHETIC LETHALITY FOR CANCER THERAPEUTICS"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T03:32:56Z"}