Implementation of a graph-embedded topic model for analysis of population-level electronic health records
Summary: To address the need for systematic investigation of the phenome enabled by ever-growing genotype and phenotype data, we describe our step-by-step software implementation of a graph-embedded topic model, including data preprocessing, graph learning, topic inference, and phenotype prediction....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | STAR Protocols |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166722008462 |
| _version_ | 1797961122101329920 |
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| author | Yuening Wang Audrey V. Grant Yue Li |
| author_facet | Yuening Wang Audrey V. Grant Yue Li |
| author_sort | Yuening Wang |
| collection | DOAJ |
| description | Summary: To address the need for systematic investigation of the phenome enabled by ever-growing genotype and phenotype data, we describe our step-by-step software implementation of a graph-embedded topic model, including data preprocessing, graph learning, topic inference, and phenotype prediction. As a demonstration, we use simulated data that mimic the UK Biobank data as in our original study. We will demonstrate topic analysis to discover disease comorbidities and computational phenotyping via the inferred topic mixture for each subject.For complete details on the use and execution of this protocol, please refer to Wang et al. (2022).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. |
| first_indexed | 2024-04-11T00:54:28Z |
| format | Article |
| id | doaj.art-72fbed7ceb0e46ff95db89ac4042f8db |
| institution | Directory Open Access Journal |
| issn | 2666-1667 |
| language | English |
| last_indexed | 2024-04-11T00:54:28Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | STAR Protocols |
| spelling | doaj.art-72fbed7ceb0e46ff95db89ac4042f8db2023-01-05T06:24:45ZengElsevierSTAR Protocols2666-16672023-03-0141101966Implementation of a graph-embedded topic model for analysis of population-level electronic health recordsYuening Wang0Audrey V. Grant1Yue Li2School of Computer Science, McGill University, Montreal, QC H3A 0G4, CanadaDepartment of Anesthesia, McGill University, Montreal, QC H2A 0G4, CanadaSchool of Computer Science, McGill University, Montreal, QC H3A 0G4, Canada; Corresponding authorSummary: To address the need for systematic investigation of the phenome enabled by ever-growing genotype and phenotype data, we describe our step-by-step software implementation of a graph-embedded topic model, including data preprocessing, graph learning, topic inference, and phenotype prediction. As a demonstration, we use simulated data that mimic the UK Biobank data as in our original study. We will demonstrate topic analysis to discover disease comorbidities and computational phenotyping via the inferred topic mixture for each subject.For complete details on the use and execution of this protocol, please refer to Wang et al. (2022).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.http://www.sciencedirect.com/science/article/pii/S2666166722008462Health SciencesSystems biologyComputer sciences |
| spellingShingle | Yuening Wang Audrey V. Grant Yue Li Implementation of a graph-embedded topic model for analysis of population-level electronic health records STAR Protocols Health Sciences Systems biology Computer sciences |
| title | Implementation of a graph-embedded topic model for analysis of population-level electronic health records |
| title_full | Implementation of a graph-embedded topic model for analysis of population-level electronic health records |
| title_fullStr | Implementation of a graph-embedded topic model for analysis of population-level electronic health records |
| title_full_unstemmed | Implementation of a graph-embedded topic model for analysis of population-level electronic health records |
| title_short | Implementation of a graph-embedded topic model for analysis of population-level electronic health records |
| title_sort | implementation of a graph embedded topic model for analysis of population level electronic health records |
| topic | Health Sciences Systems biology Computer sciences |
| url | http://www.sciencedirect.com/science/article/pii/S2666166722008462 |
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