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Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0
Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNe...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351549/ https://www.ncbi.nlm.nih.gov/pubmed/35936510 http://dx.doi.org/10.3390/bdcc6010027 |
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author | Kirkpatrick, Anna Onyeze, Chidozie Kartchner, David Allegri, Stephen An, Davi Nakajima McCoy, Kevin Davalbhakta, Evie Mitchell, Cassie S. |
author_facet | Kirkpatrick, Anna Onyeze, Chidozie Kartchner, David Allegri, Stephen An, Davi Nakajima McCoy, Kevin Davalbhakta, Evie Mitchell, Cassie S. |
author_sort | Kirkpatrick, Anna |
collection | PubMed |
description | Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer’s disease and metabolic co-morbidities. |
format | Online Article Text |
id | pubmed-9351549 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-93515492022-08-04 Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0 Kirkpatrick, Anna Onyeze, Chidozie Kartchner, David Allegri, Stephen An, Davi Nakajima McCoy, Kevin Davalbhakta, Evie Mitchell, Cassie S. Big Data Cogn Comput Article Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer’s disease and metabolic co-morbidities. 2022-03 2022-03-01 /pmc/articles/PMC9351549/ /pubmed/35936510 http://dx.doi.org/10.3390/bdcc6010027 Text en https://creativecommons.org/licenses/by/4.0/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kirkpatrick, Anna Onyeze, Chidozie Kartchner, David Allegri, Stephen An, Davi Nakajima McCoy, Kevin Davalbhakta, Evie Mitchell, Cassie S. Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0 |
title | Optimizations for Computing Relatedness in Biomedical Heterogeneous
Information Networks: SemNet 2.0 |
title_full | Optimizations for Computing Relatedness in Biomedical Heterogeneous
Information Networks: SemNet 2.0 |
title_fullStr | Optimizations for Computing Relatedness in Biomedical Heterogeneous
Information Networks: SemNet 2.0 |
title_full_unstemmed | Optimizations for Computing Relatedness in Biomedical Heterogeneous
Information Networks: SemNet 2.0 |
title_short | Optimizations for Computing Relatedness in Biomedical Heterogeneous
Information Networks: SemNet 2.0 |
title_sort | optimizations for computing relatedness in biomedical heterogeneous
information networks: semnet 2.0 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9351549/ https://www.ncbi.nlm.nih.gov/pubmed/35936510 http://dx.doi.org/10.3390/bdcc6010027 |
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