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Li Q, Li J, Chen J, Zhao X, Zhuang J, Zhong G, Song Y, Lei L. A machine learning-based prediction model for postoperative delirium in cardiac valve surgery using electronic health records. BMC Cardiovasc Disord 2024; 24:56. [PMID: 38238677 PMCID: PMC10795338 DOI: 10.1186/s12872-024-03723-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 01/11/2024] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Previous models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients. METHODS The electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (Random Forest Classifier, Logistic Regression, Support Vector Machine Classifier, K-nearest Neighbors Classifier, Gaussian Naive Bayes, Gradient Boosting Decision Tree, and Perceptron.) were used to develop delirium prediction models under full (q = 31) and selected (q = 19) feature sets, respectively. RESULT The Random Forest classifier performs exceptionally well in both feature datasets, with an Area Under the Curve (AUC) of 0.92 for the full feature dataset and an AUC of 0.86 for the selected feature dataset. Additionally, it achieves a relatively lower Expected Calibration Error (ECE) and the highest Average Precision (AP), with an AP of 0.80 for the full feature dataset and an AP of 0.73 for the selected feature dataset. To further evaluate the best-performing Random Forest classifier, SHAP (Shapley Additive Explanations) was used, and the importance matrix plot, scatter plots, and summary plots were generated. CONCLUSIONS We established machine learning-based prediction models to predict POD in patients undergoing cardiac valve surgery. The random forest model has the best predictive performance in prediction and can help improve the prognosis of patients with POD.
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Affiliation(s)
- Qiuying Li
- Department of Cardiac Surgical Intensive Care Unit, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China
- Shantou University Medical College (SUMC), Shantou, 515041, China
| | - Jiaxin Li
- Department of Cardiac Surgical Intensive Care Unit, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China
| | - Jiansong Chen
- Department of Cardiovascular Surgery, Guangdong General Hospital's Nanhai Hospital, The Second People's Hospital of Nanhai District, Foshan, Guangdong, 528251, China
| | - Xu Zhao
- Institute of Clinical Pharmacology, Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Jian Zhuang
- Department of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China
| | - Guoping Zhong
- Institute of Clinical Pharmacology, Guangdong Provincial Key Laboratory of New Drug Design and Evaluation, School of Pharmaceutical Sciences, Sun Yat-Sen University, Guangzhou, Guangdong, China.
| | - Yamin Song
- Department of Cardiac Surgical Intensive Care Unit, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China.
| | - Liming Lei
- Department of Cardiac Surgical Intensive Care Unit, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China.
- Shantou University Medical College (SUMC), Shantou, 515041, China.
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2
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Nayak SS, Naidu A, Sudhakaran SL, Vino S, Selvaraj G. Prospects of Novel and Repurposed Immunomodulatory Drugs against Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19 Disease. J Pers Med 2023; 13:664. [PMID: 37109050 PMCID: PMC10142859 DOI: 10.3390/jpm13040664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/09/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Acute respiratory distress syndrome (ARDS) is intricately linked with SARS-CoV-2-associated disease severity and mortality, especially in patients with co-morbidities. Lung tissue injury caused as a consequence of ARDS leads to fluid build-up in the alveolar sacs, which in turn affects oxygen supply from the capillaries. ARDS is a result of a hyperinflammatory, non-specific local immune response (cytokine storm), which is aggravated as the virus evades and meddles with protective anti-viral innate immune responses. Treatment and management of ARDS remain a major challenge, first, because the condition develops as the virus keeps replicating and, therefore, immunomodulatory drugs are required to be used with caution. Second, the hyperinflammatory responses observed during ARDS are quite heterogeneous and dependent on the stage of the disease and the clinical history of the patients. In this review, we present different anti-rheumatic drugs, natural compounds, monoclonal antibodies, and RNA therapeutics and discuss their application in the management of ARDS. We also discuss on the suitability of each of these drug classes at different stages of the disease. In the last section, we discuss the potential applications of advanced computational approaches in identifying reliable drug targets and in screening out credible lead compounds against ARDS.
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Affiliation(s)
- Smruti Sudha Nayak
- Department of Bio-Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Akshayata Naidu
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Sajitha Lulu Sudhakaran
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Sundararajan Vino
- Department of Bio-Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Gurudeeban Selvaraj
- Centre for Research in Molecular Modeling, Department of Chemistry and Biochemistry, Concordia University-Loyola Campus, Montreal, QC H4B 1R6, Canada
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3
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Ben Guebila M, Wang T, Lopes-Ramos CM, Fanfani V, Weighill D, Burkholz R, Schlauch D, Paulson JN, Altenbuchinger M, Shutta KH, Sonawane AR, Lim J, Calderer G, van IJzendoorn DGP, Morgan D, Marin A, Chen CY, Song Q, Saha E, DeMeo DL, Padi M, Platig J, Kuijjer ML, Glass K, Quackenbush J. The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks. Genome Biol 2023; 24:45. [PMID: 36894939 PMCID: PMC9999668 DOI: 10.1186/s13059-023-02877-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 02/15/2023] [Indexed: 03/11/2023] Open
Abstract
Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection of open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, and explore the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages and between methods to allow better integration of these tools into analytical pipelines. We demonstrate the utility using multi-omic data from the Cancer Cell Line Encyclopedia. We will continue to expand the netZoo to incorporate additional methods.
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Affiliation(s)
- Marouen Ben Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tian Wang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Biology Department, Boston College, Chestnut Hill, MA, USA
| | - Camila M Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Viola Fanfani
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Des Weighill
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebekka Burkholz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: CISPA Helmholtz Center for Information Security, Saarbrücken, Germany
| | - Daniel Schlauch
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Genospace, LLC, Boston, MA, USA
| | - Joseph N Paulson
- Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | - Michael Altenbuchinger
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Present Address: Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Katherine H Shutta
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Abhijeet R Sonawane
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - James Lim
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
- Present Address: Monoceros Biosystems, LLC, San Diego, CA, USA
| | - Genis Calderer
- Center for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo, Oslo, Norway
| | - David G P van IJzendoorn
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Present Address: Department of Pathology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Daniel Morgan
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: School of Biomedical Sciences, Hong Kong University, Pokfulam, Hong Kong
| | | | - Cho-Yi Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
- Present Address: Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Qi Song
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Present Address: Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Enakshi Saha
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Megha Padi
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Marieke L Kuijjer
- Center for Molecular Medicine Norway, Nordic EMBL Partnership, University of Oslo, Oslo, Norway
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
- Leiden Center for Computational Oncology, Leiden University, Leiden, The Netherlands
| | - Kimberly Glass
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Dana-Farber Cancer Institute, Boston, MA, USA.
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Lopes-Ramos CM, Belova T, Brunner TH, Ben Guebila M, Osorio D, Quackenbush J, Kuijjer ML. Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme. Cancer Res 2021; 81:5401-5412. [PMID: 34493595 PMCID: PMC8563450 DOI: 10.1158/0008-5472.can-21-0730] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 07/20/2021] [Accepted: 09/02/2021] [Indexed: 01/07/2023]
Abstract
Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma 'omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer. SIGNIFICANCE: Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.
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Affiliation(s)
- Camila M. Lopes-Ramos
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Tatiana Belova
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | | | - Marouen Ben Guebila
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Daniel Osorio
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.,Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts.,Channing Division of Network Medicine, Harvard Medical School, Boston, Massachusetts
| | - Marieke L. Kuijjer
- Centre for Molecular Medicine Norway, University of Oslo, Oslo, Norway.,Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands.,Corresponding Author: Marieke L. Kuijjer, Centre for Molecular Medicine Norway, University of Oslo, Guastadalléen 21, Oslo 0318, Norway. Phone: 47-22840528; E-mail:
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5
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Nguyen T, Lee SC, Quinn TP, Truong B, Li X, Tran T, Venkatesh S, Le TD. PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2841-2847. [PMID: 33909569 DOI: 10.1109/tcbb.2021.3076422] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The classification of clinical samples based on gene expression data is an important part of precision medicine. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graph-based methods to improve classifier performance. Existing approaches to personalized gene networks have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced. Here, we propose a novel method, called Personalized Annotation-based Networks (PAN), that avoids this limitation by using curated annotation databases to transform gene expression data into a graph. Unlike competing methods, PANs are calculated for each sample independent of the population, making it a more efficient way to obtain single-sample networks. Using three breast cancer datasets as a case study, we show that PAN classifiers not only predict cancer relapse better than gene features alone, but also outperform PPI (protein-protein interactions) and population-level graph-based classifiers. This work demonstrates the practical advantages of graph-based classification for high-dimensional genomic data, while offering a new approach to making sample-specific networks. Supplementary information: PAN and the baselines are implemented in Python. Source code and data are available at https://github.com/thinng/PAN.
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Kuijjer ML, Hsieh PH, Quackenbush J, Glass K. lionessR: single sample network inference in R. BMC Cancer 2019; 19:1003. [PMID: 31653243 PMCID: PMC6815019 DOI: 10.1186/s12885-019-6235-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 10/01/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In biomedical research, network inference algorithms are typically used to infer complex association patterns between biological entities, such as between genes or proteins, using data from a population. This resulting aggregate network, in essence, averages over the networks of those individuals in the population. LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) is a method that can be used together with a network inference algorithm to extract networks for individual samples in a population. The method's key characteristic is that, by modeling networks for individual samples in a data set, it can capture network heterogeneity in a population. LIONESS was originally made available as a function within the PANDA (Passing Attributes between Networks for Data Assimilation) regulatory network reconstruction framework. However, the LIONESS algorithm is generalizable and can be used to model single sample networks based on a wide range of network inference algorithms. RESULTS In this software article, we describe lionessR, an R implementation of LIONESS that can be applied to any network inference method in R that outputs a complete, weighted adjacency matrix. As an example, we provide a vignette of an application of lionessR to model single sample networks based on correlated gene expression in a bone cancer dataset. We show how the tool can be used to identify differential patterns of correlation between two groups of patients. CONCLUSIONS We developed lionessR, an open source R package to model single sample networks. We show how lionessR can be used to inform us on potential precision medicine applications in cancer. The lionessR package is a user-friendly tool to perform such analyses. The package, which includes a vignette describing the application, is freely available at: https://github.com/kuijjerlab/lionessR and at: http://bioconductor.org/packages/lionessR .
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Affiliation(s)
- Marieke L Kuijjer
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Gaustadalléen 21, Oslo, 0318, Norway.
| | - Ping-Han Hsieh
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, Gaustadalléen 21, Oslo, 0318, Norway
| | - John Quackenbush
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02215, USA.,Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, 02215, USA.,Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, 02215, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women's Hospital, 181 Longwood Avenue, Boston, 02215, USA.,Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, 02215, USA
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Hurgobin B, de Jong E, Bosco A. Insights into respiratory disease through bioinformatics. Respirology 2018; 23:1117-1126. [PMID: 30218470 DOI: 10.1111/resp.13401] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 08/18/2018] [Accepted: 08/22/2018] [Indexed: 12/21/2022]
Abstract
Respiratory diseases such as asthma, chronic obstructive pulmonary disease and lung cancer represent a critical area for medical research as millions of people are affected globally. The development of new strategies for treatment and/or prevention, and the identification of biomarkers for patient stratification and early detection of disease inception are essential to reducing the impact of lung diseases. The successful translation of research into clinical practice requires a detailed understanding of the underlying biology. In this regard, the advent of next-generation sequencing and mass spectrometry has led to the generation of an unprecedented amount of data spanning multiple layers of biological regulation (genome, epigenome, transcriptome, proteome, metabolome and microbiome). Dealing with this wealth of data requires sophisticated bioinformatics and statistical tools. Here, we review the basic concepts in bioinformatics and genomic data analysis and illustrate the application of these tools to further our understanding of lung diseases. We also highlight the potential for data integration of multi-omic profiles and computational drug repurposing to define disease subphenotypes and match them to targeted therapies, paving the way for personalized medicine.
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Affiliation(s)
- Bhavna Hurgobin
- Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
| | - Emma de Jong
- Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
| | - Anthony Bosco
- Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia
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Jones AC, Bosco A. Using Network Analysis to Understand Severe Asthma Phenotypes. Am J Respir Crit Care Med 2017; 195:1409-1411. [PMID: 28569573 DOI: 10.1164/rccm.201612-2572ed] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Anya C Jones
- 1 Telethon Kids Institute The University of Western Australia Perth, Australia
| | - Anthony Bosco
- 1 Telethon Kids Institute The University of Western Australia Perth, Australia
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