[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125931-en":3,"doc-seo-125931-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125931,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Unlocking Insights in the Life Sciences Domain through Knowledge Graph Construction and Hypothesis Generation Using Machine Learning","Life sciences datasets often contain noise and are difficult for machines to use, limiting faster and lower-cost discovery of new scientific findings. This dissertation investigates automated knowledge management and discovery using advanced machine learning. It improves food ontology creation via a semi-supervised, word-embedding framework, builds an Escherichia coli antibiotic resistance knowledge graph with iterative link prediction and wet-lab validation, and introduces the Knowledge Graph Language Model for state-of-the-art link prediction. An active-learning pipeline extracts large-scale food-chemical relationships from papers.","UC Davis  \nUC Davis Electronic Theses and Dissertations  \nTitle  \nUnlocking Insights in the Life Sciences Domain through Knowledge Graph Construction and Hypothesis Generation Using Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/11z2038d](https://escholarship.org/uc/item/11z2038d)  \nAuthor  \nYoun, Jae Sung  \nPublication Date  \n2024  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUnlocking Insights in the Life Sciences Domain through Knowledge Graph Construction and Hypothesis Generation Using Machine Learning  \nBy  \nJAE SUNG YOUN  \nDISSERTATION  \nSubmitted in partial satisfaction of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nin  \nComputer Science  \nin the  \nOFFICE OF GRADUATE STUDIES  \nof the  \nUNIVERSITY OF CALIFORNIA  \nDAVIS  \nApproved:  \n\n| Ilias Tagkopoulos, Chair |\n| --- |\n| Justin B. Siegel |\n\nXin Liu Committee in Charge  \n2024  \nAcknowledgments  \nI thank my wife, Xing Liu, whose constructive criticisms have been my guiding light throughout the tumultuous journey of graduate school. My daughter, Yelynn, has been my little inspiration, sharing the experience of academic pursuit as we both navigated the world of learning together. I am immensely thankful to my parents, whose unconditional support has been the bedrock of my academic endeavors, providing assistance and encouragement without hesitation.  \nI am grateful to my advisor to my advisor, Professor Ilias Tagkopoulos, for believing in meand offering me the opportunity to embark on this transformative journey. The lessons I learned from Ilias during my Ph. D. transcended the realm of academia, shaping not only my skills as a researcher but also nurturing personal growth. I also thank Ilias for his understanding and accommodation of my familial responsibilities, providing invaluable support during the challenges of supporting my family throughout my Ph. D. I extend my appreciation to the members of my dissertation committee, Professor Xin Liu and Professor Justin Siegel, for their insightful feedback that significantly contributed to the completion of my research and the refinement of this dissertation. Finally, a special acknowledgment goes to the members of the Tagkopoulos lab, Navneet, Xiaokang, Beatriz, Ameen, Gabriel, Tarini, Christian, Erol, Trevor, Cheng-En, Fang, Arielle, Adil, Bobby, Miachel, and Keer for their friendship and support, making my time in the lab enriching and memorable.  \nAbstract  \nIn the dynamic landscape of life sciences data, the inherent noise and lack of machinefriendliness present significant challenges. The pressing need arises to transform this complex and unstructured data into a machine-friendly format, fostering efficient utilization for the generation of novel scientific discoveries in a faster and more costeffective manner. This dissertation presents a comprehensive exploration of automated knowledge management and discovery across various domains using advanced machine learning techniques. We first address the challenges associated with the manual creation and maintenance of food ontologies. A semi-supervised framework employing word embeddings is proposed, demonstrating an 89.7% improvement in precision compared to the expert-curated FoodOn ontology. Second, a machine learning framework is introduced for automated knowledge discovery through the construction of a comprehensive Escherichia coli antibiotic resistance knowledge graph. Iterative link prediction and wet-lab validation led to the identification of 15 antibiotic-resistant genes, including 6 previously unassociated with antibiotic resistance. Third, the Knowledge Graph Language Model (KGLM), which incorporates a novel entity/relation embedding layer, achieves state-of-the-art performance in link prediction tasks on benchmark datasets. Finally, an integrated pipeline is presented for the automated generation of large-scale knowledge graphs in","cbCain2cG4clltp4","https://ap.wps.com/l/cbCain2cG4clltp4","pdf",8100023,6,1,255,"English","en",105,"# Chapter 1 Introduction\n## 1.1 Introduction to knowledge graphs and ontologies\n## 1.2 Current efforts for knowledge graph construction\n## 1.3 Limitations and challenges\n## 1.4 Knowledge graph completion\n## 1.5 Overview of the dissertation\n# Chapter 2 Using word embeddings to learn a better food ontology\n## 2.1 Introduction\n## 2.2 Methods\n## 2.2.1 Data preprocessing and training of word embeddings\n## 2.2.2 Ontology population\n## 2.2.3 Evaluation metrics of the ontology structure\n## 2.2.4 Success metric of ontology population","[{\"question\":\"What problem does the dissertation address in life sciences data?\",\"answer\":\"It targets the noise and machine-unfriendly nature of life sciences data that makes transformation into machine-ready formats difficult, slowing discovery of scientific hypotheses.\"},{\"question\":\"How does the dissertation improve food ontology construction?\",\"answer\":\"It proposes a semi-supervised framework using word embeddings for ontology population, reporting an 89.7% precision improvement over the expert-curated FoodOn ontology.\"},{\"question\":\"What are the main components of the knowledge discovery workflow described?\",\"answer\":\"The work includes constructing domain knowledge graphs, using iterative link prediction with wet-lab validation to identify antibiotic-resistant genes, and presenting models and pipelines such as KGLM and an active-learning graph generation pipeline.\"}]","Unlocking Insights in the Life Sciences Domain through Knowledge Graph Construction and Hypothesis Generation Using Machine Learning | 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problem does the dissertation address in life sciences data?","Question",{"text":77,"@type":78},"It targets the noise and machine-unfriendly nature of life sciences data that makes transformation into machine-ready formats difficult, slowing discovery of scientific hypotheses.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How does the dissertation improve food ontology construction?",{"text":82,"@type":78},"It proposes a semi-supervised framework using word embeddings for ontology population, reporting an 89.7% precision improvement over the expert-curated FoodOn ontology.",{"name":84,"@type":75,"acceptedAnswer":85},"What are the main components of the knowledge discovery workflow described?",{"text":86,"@type":78},"The work includes constructing domain knowledge graphs, using iterative link prediction with wet-lab validation to identify antibiotic-resistant genes, and presenting models and pipelines such as KGLM and an active-learning graph generation 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