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Romano JD, Truong V, Kumar R, Venkatesan M, Graham BE, Hao Y, Matsumoto N, Li X, Wang Z, Ritchie MD, Shen L, Moore JH. The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research. J Med Internet Res 2024; 26:e46777. [PMID: 38635981 PMCID: PMC11066745 DOI: 10.2196/46777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 06/23/2023] [Accepted: 11/07/2023] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. OBJECTIVE We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. METHODS We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. RESULTS AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. CONCLUSIONS AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.
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Affiliation(s)
- Joseph D Romano
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Center of Excellence in Environmental Toxicology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Van Truong
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Rachit Kumar
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Mythreye Venkatesan
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Britney E Graham
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Yun Hao
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nick Matsumoto
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Xi Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Zhiping Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Marylyn D Ritchie
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Li Shen
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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Ghorbanali Z, Zare-Mirakabad F, Salehi N, Akbari M, Masoudi-Nejad A. DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing. BMC Bioinformatics 2023; 24:374. [PMID: 37789314 PMCID: PMC10548718 DOI: 10.1186/s12859-023-05479-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/12/2023] [Indexed: 10/05/2023] Open
Abstract
BACKGROUND Drug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs. RESULTS This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.63%, a BS of 0.119, and an MCC of 69.31%. CONCLUSION We demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing. The code and data for DrugRep-HeSiaGraph are publicly available at https://github.com/CBRC-lab/DrugRep-HeSiaGraph .
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Affiliation(s)
- Zahra Ghorbanali
- Computational Biology Research Center (CBRC), Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
| | - Fatemeh Zare-Mirakabad
- Computational Biology Research Center (CBRC), Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran.
| | - Najmeh Salehi
- School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Mohammad Akbari
- Computational Biology Research Center (CBRC), Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Li F, Nian Y, Sun Z, Tao C. Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions. Yearb Med Inform 2023; 32:215-224. [PMID: 38147863 PMCID: PMC10751115 DOI: 10.1055/s-0043-1768735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Abstract
OBJECTIVES Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research. METHODS We conducted a comprehensive search of multiple databases, including PubMed, Web of Science, IEEE Xplore, and Google Scholar, to collect relevant publications from the past two years (2021-2022). The studies selected for review were based on their relevance to the topic and the publication quality. RESULTS A total of 78 articles were included in our analysis. We identified three main categories of GRL methods and summarized their methodological foundations and notable models. In terms of GRL applications, we focused on two main topics: drug and disease. We analyzed the study frameworks and achievements of the prominent research. Based on the current state-of-the-art, we discussed the challenges and future directions. CONCLUSIONS GRL methods applied in the biomedical field demonstrated several key characteristics, including the utilization of attention mechanisms to prioritize relevant features, a growing emphasis on model interpretability, and the combination of various techniques to improve model performance. There are also challenges needed to be addressed, including mitigating model bias, accommodating the heterogeneity of large-scale knowledge graphs, and improving the availability of high-quality graph data. To fully leverage the potential of GRL, future efforts should prioritize these areas of research.
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Affiliation(s)
- Fang Li
- McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yi Nian
- McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Zenan Sun
- McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Cui Tao
- McWilliams School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA
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