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Article ; Online: Reply to "Post-COVID 19 neurological syndrome: A new risk factor that modifies the prognosis of patients with dementia".

Davis, Pamela B / Wang, QuanQiu / Xu, Rong

Alzheimer's & dementia : the journal of the Alzheimer's Association

2022 Volume 18, Issue 3, Page(s) 544

MeSH term(s)	COVID-19 ; Dementia ; Humans ; Prognosis ; Risk Factors ; Syndrome
Language	English
Publishing date	2022-01-03
Publishing country	United States
Document type	Letter ; Comment
ZDB-ID	2211627-8
ISSN	1552-5279 ; 1552-5260
ISSN (online)	1552-5279
ISSN	1552-5260
DOI	10.1002/alz.12460
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Article ; Online: Risk, Racial Disparity, and Outcomes Among Patients With Cancer and COVID-19 Infection-Reply.

Xu, Rong / Berger, Nathan A / Wang, QuanQiu

JAMA oncology

2021 Volume 7, Issue 7, Page(s) 1065–1066

MeSH term(s)	COVID-19 ; Continental Population Groups ; European Continental Ancestry Group ; Humans ; Neoplasms/epidemiology ; Neoplasms/therapy ; SARS-CoV-2
Language	English
Publishing date	2021-05-06
Publishing country	United States
Document type	Journal Article ; Comment
ISSN	2374-2445
ISSN (online)	2374-2445
DOI	10.1001/jamaoncol.2021.0771
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Prediction and evaluation of combination pharmacotherapy using natural language processing, machine learning and patient electronic health records.

Ding, Pingjian / Pan, Yiheng / Wang, Quanqiu / Xu, Rong

Journal of biomedical informatics

2022 Volume 133, Page(s) 104164

Abstract: Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large ... ...

Abstract	Combination pharmacotherapy targets key disease pathways in a synergistic or additive manner and has high potential in treating complex diseases. Computational methods have been developed to identifying combination pharmacotherapy by analyzing large amounts of biomedical data. Existing computational approaches are often underpowered due to their reliance on our limited understanding of disease mechanisms. On the other hand, observable phenotypic inter-relationships among thousands of diseases often reflect their underlying shared genetic and molecular underpinnings, therefore can offer unique opportunities to design computational models to discover novel combinational therapies by automatically transferring knowledge among phenotypically related diseases. We developed a novel phenome-driven drug discovery system, named TuSDC, which leverages knowledge of existing drug combinations, disease comorbidities, and disease treatments of thousands of disease and drug entities extracted from over 31.5 million biomedical research articles using natural language processing techniques. TuSDC predicts combination pharmacotherapy by extracting representations of diseases and drugs using tensor factorization approaches. In external validation, TuSDC achieved an average precision of 0.77 for top ranked candidates, outperforming a state of art mechanism-based method for discovering drug combinations in treating hypertension. We evaluated top ranked anti-hypertension drug combinations using electronic health records of 84.7 million unique patients and showed that a novel drug combination hydrochlorothiazide-digoxin was associated with significantly lower hazards of subsequent hypertension as compared to the monotherapy hydrochlorothiazide alone (HR: 0.769, 95% CI [0.732, 0.807]) and digoxin alone (0.857, 95% CI [0.785, 0.936]). Data-driven informatics analyses reveal that the renin-angiotensin system is involved in the synergistical interactions of hydrochlorothiazide and digoxin on regulating hypertension. The prediction model's code with PyTorch version 1.5 is available at http://nlp.case.edu/public/data/TuSDC/.
MeSH term(s)	Digoxin ; Drug Combinations ; Electronic Health Records ; Humans ; Hydrochlorothiazide ; Hypertension/drug therapy ; Machine Learning ; Natural Language Processing ; Phenotype
Chemical Substances	Drug Combinations ; Hydrochlorothiazide (0J48LPH2TH) ; Digoxin (73K4184T59)
Language	English
Publishing date	2022-08-17
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2022.104164
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Automatic extraction, prioritization and analysis of gut microbial metabolites from biomedical literature.

Wang, QuanQiu / Xu, Rong

Scientific reports

2020 Volume 10, Issue 1, Page(s) 9996

Abstract: Many diseases are driven by gene-environment interactions. One important environmental factor is the metabolic output of human gut microbiota. A comprehensive catalog of human metabolites originated in microbes is critical for data-driven approaches to ... ...

Abstract	Many diseases are driven by gene-environment interactions. One important environmental factor is the metabolic output of human gut microbiota. A comprehensive catalog of human metabolites originated in microbes is critical for data-driven approaches to understand how microbial metabolism contributes to human health and diseases. Here we present a novel integrated approach to automatically extract and analyze microbial metabolites from 28 million published biomedical records. First, we classified 28,851,232 MEDLINE records into microbial metabolism-related or not. Second, candidate microbial metabolites were extracted from the classified texts. Third, we developed signal prioritization algorithms to further differentiate microbial metabolites from metabolites originated from other resources. Finally, we systematically analyzed the interactions between extracted microbial metabolites and human genes. A total of 11,846 metabolites were extracted from 28 million MEDLINE articles. The combined text classification and signal prioritization significantly enriched true positives among top: manual curation of top 100 metabolites showed a true precision of 0.55, representing a significant 38.3-fold enrichment as compared to the precision of 0.014 for baseline extraction. More importantly, 29% extracted microbial metabolites have not been captured by existing databases. We performed data-driven analysis of the interactions between the extracted microbial metabolite and human genetics. This study represents the first effort towards automatically extracting and prioritizing microbial metabolites from published biomedical literature, which can set a foundation for future tasks of microbial metabolite relationship extraction from literature and facilitate data-driven studies of how microbial metabolism contributes to human diseases.
MeSH term(s)	Data Mining ; Databases, Factual ; Gastrointestinal Microbiome/physiology ; Humans ; MEDLINE
Language	English
Publishing date	2020-06-19
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-67075-6
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: CoMNRank: An integrated approach to extract and prioritize human microbial metabolites from MEDLINE records.

Wang, QuanQiu / Xu, Rong

Journal of biomedical informatics

2020 Volume 109, Page(s) 103524

Abstract: Motivation: Trillions of bacteria in human body (human microbiota) affect human health and diseases by controlling host functions through small molecule metabolites.An accurate and comprehensive catalog of the metabolic output from human microbiota is ... ...

Abstract	Motivation: Trillions of bacteria in human body (human microbiota) affect human health and diseases by controlling host functions through small molecule metabolites.An accurate and comprehensive catalog of the metabolic output from human microbiota is critical for our deep understanding of how microbial metabolism contributes to human health.The large number of published biomedical research articles is a rich resource of microbiome studies.However, automatically extracting microbial metabolites from free-text documents and differentiating them from other human metabolites is a challenging task.Here we developed an integrated approach called Co-occurrence Metabolite Network Ranking (CoMNRank) by combining named entity extraction, network construction and topic sensitive network-based prioritization to extract and prioritize microbial metabolites from biomedical articles. Methods: The text data included 28,851,232 MEDLINE records.CoMNRank consists of three steps: (1) extraction of human metabolites from MEDLINE records; (2) construction of a weighted co-occurrence metabolite network (CoMN); (3) prioritization and differentiation of microbial metabolites from other human metabolites. Results: For the first step of CoMNRank, we extracted 11,846 human metabolites from MEDLINE articles, with a baseline performance of precision of 0.014, recall of 0.959 and F1 of 0.028.We then constructed a weighted CoMN of 6,996 nodes and 986,186 edges.CoMNRank effectively prioritized microbial metabolites: the precision of top ranked metabolites is 0.45, a 31-fold enrichment as compared to the overall precision of 0.014.Manual curation of top 100 metabolites showed a true precision of 0.67, among which 48% true positives are not captured by existing databases. Conclusion: Our study sets the foundation for future tasks of microbial entity and relationship extractions as well as data-driven studies of how microbial metabolism contributes to human health and diseases.
MeSH term(s)	Data Mining ; Databases, Factual ; Humans ; MEDLINE ; Publications
Language	English
Publishing date	2020-08-11
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2057141-0
ISSN	1532-0480 ; 1532-0464
ISSN (online)	1532-0480
ISSN	1532-0464
DOI	10.1016/j.jbi.2020.103524
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: COVID-19 risk, disparities and outcomes in patients with chronic liver disease in the United States

QuanQiu Wang / Pamela B. Davis / Rong Xu

EClinicalMedicine, Vol 31, Iss , Pp 100688- (2021)

2021

Abstract: Background: Scientific evidence is lacking regarding the risk of patients with chronic liver disease (CLD) for COVID-19, and how these risks are affected by age, gender and race. Methods: We performed a case-control study of electronic health records of ... ...

Abstract	Background: Scientific evidence is lacking regarding the risk of patients with chronic liver disease (CLD) for COVID-19, and how these risks are affected by age, gender and race. Methods: We performed a case-control study of electronic health records of 62.2 million patients (age >18 years) in the US up to October 1st, 2020, including 1,034,270 patients with CLD, 16,530 with COVID-19, and 820 with both COVID-19 and CLD. We assessed the risk, disparities, and outcomes of COVID-19 in patients with six major CLDs. Findings: Patients with a recent medical encounter for CLD were at significantly increased risk for COVID-19 compared with patients without CLD, with the strongest effect in patients with chronic non-alcoholic liver disease [adjusted odd ratio (AOR)=13.11, 95% CI: 12.49–13.76, p < 0.001] and non-alcoholic cirrhosis (AOR=11.53, 95% CI: 10.69–12.43, p < 0.001), followed by chronic hepatitis C (AOR=8.93, 95% CI:8.25–9.66, p < 0.001), alcoholic liver damage (AOR=7.05, 95% CI:6.30–7.88, p < 0.001), alcoholic liver cirrhosis (AOR=7.00, 95% CI:6.15–7.97, p < 0.001) and chronic hepatitis B (AOR=4.37, 95% CI:3.35–5.69, p < 0.001). African Americans with CLD were twice more likely to develop COVID-19 than Caucasians. Patients with COVID-19 and a recent encounter for CLD had a death rate of 10.3% (vs. 5.5% among COVID-19 patients without CLD, p < 0.001) and a hospitalization rate of 41.0% (vs. 23.9% among COVID-19 patients without CLD, p < 0.001). Interpretation: Patients with CLD, especially African Americans, were at increased risk for COVID-19, highlighting the need to protect these patients from exposure to virus infection. Funding: National Institutes of Health (AG057557, AG061388, AG062272, 1UL1TR002548-01), American Cancer Society (RSG-16-049-01-MPC).
Keywords	SARS-CoV-2 ; COVID-19 ; Chronic liver disease ; Alcoholic cirrhosis ; Non-alcoholic cirrhosis ; Alcoholic liver damage ; Medicine (General) ; R5-920
Subject code	610
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis.

Wang, QuanQiu / Xu, Rong

BMC genomics

2019 Volume 20, Issue 1, Page(s) 124

Abstract: Background: Rheumatoid arthritis (RA) is the most common autoimmune disease and affects about 1% of the population. The cause of RA remains largely unknown and could result from a complex interaction between genes and environment factors. Recent studies ...

Abstract	Background: Rheumatoid arthritis (RA) is the most common autoimmune disease and affects about 1% of the population. The cause of RA remains largely unknown and could result from a complex interaction between genes and environment factors. Recent studies suggested that gut microbiota and their collective metabolic outputs exert profound effects on the host immune system and are implicated in RA. However, which and how gut microbial metabolites interact with host genetics in contributing to RA pathogenesis remains unknown. In this study, we present a data-driven study to understand how gut microbial metabolites contribute to RA at the genetic, functional and phenotypic levels. Results: We used publicly available disease genetics, chemical genetics, human metabolome, genetic signaling pathways, mouse genome-wide mutation phenotypes, and mouse phenotype ontology data. We identified RA-associated microbial metabolites and prioritized them based on their genetic, functional and phenotypic relevance to RA. We evaluated the prioritization methods using short-chain fatty acids (SCFAs), which were previously shown to be involved in RA etiology. We validate the algorithms by showing that SCFAs are highly associated with RA at genetic, functional and phenotypic levels: SCFAs ranked at top 3.52% based on shared genes with RA, top 5.69% based on shared genetic pathways, and top 16.94% based on shared phenotypes. Based on the genetic-level analysis, human gut microbial metabolites directly interact with many RA-associated genes (as many as 18.1% of all 166 RA genes). Based on the functional-level analysis, human gut microbial metabolites participate in many RA-associated genetic pathways (as many as 71.4% of 311 genetic pathways significantly enriched for RA), including immune system pathways. Based on the phenotypic-level analysis, gut microbial metabolites affect many RA-related phenotypes (as many as 51.3% of 978 phenotypes significantly enriched for RA), including many immune system phenotypes. Conclusions: Our study demonstrates strong gut-microbiome-immune-joint interactions in RA, which converged on both genetic, functional and phenotypic levels.
MeSH term(s)	Arthritis, Rheumatoid/immunology ; Arthritis, Rheumatoid/metabolism ; Arthritis, Rheumatoid/microbiology ; Arthritis, Rheumatoid/pathology ; Computational Biology ; Gastrointestinal Microbiome ; Humans ; Joints/metabolism ; Joints/pathology ; Metabolomics ; Mutation ; Phenotype ; Signal Transduction
Language	English
Publishing date	2019-02-11
Publishing country	England
Document type	Journal Article
ISSN	1471-2164
ISSN (online)	1471-2164
DOI	10.1186/s12864-019-5510-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: COVID-19 risk, disparities and outcomes in patients with chronic liver disease in the United States.

Wang, QuanQiu / Davis, Pamela B / Xu, Rong

EClinicalMedicine

2020 Volume 31, Page(s) 100688

Abstract: Background: Scientific evidence is lacking regarding the risk of patients with chronic liver disease (CLD) for COVID-19, and how these risks are affected by age, gender and race.: Methods: We performed a case-control study of electronic health ... ...

Abstract	Background: Scientific evidence is lacking regarding the risk of patients with chronic liver disease (CLD) for COVID-19, and how these risks are affected by age, gender and race. Methods: We performed a case-control study of electronic health records of 62.2 million patients (age >18 years) in the US up to October 1st, 2020, including 1,034,270 patients with CLD, 16,530 with COVID-19, and 820 with both COVID-19 and CLD. We assessed the risk, disparities, and outcomes of COVID-19 in patients with six major CLDs. Findings: Patients with a recent medical encounter for CLD were at significantly increased risk for COVID-19 compared with patients without CLD, with the strongest effect in patients with chronic non-alcoholic liver disease [adjusted odd ratio (AOR)=13.11, 95% CI: 12.49-13.76, Interpretation: Patients with CLD, especially African Americans, were at increased risk for COVID-19, highlighting the need to protect these patients from exposure to virus infection. Funding: National Institutes of Health (AG057557, AG061388, AG062272, 1UL1TR002548-01), American Cancer Society (RSG-16-049-01-MPC).
Language	English
Publishing date	2020-12-22
Publishing country	England
Document type	Journal Article
ISSN	2589-5370
ISSN (online)	2589-5370
DOI	10.1016/j.eclinm.2020.100688
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: When hematologic malignancies meet COVID-19 in the United States

Wang, QuanQiu / Berger, Nathan A. / Xu, Rong

Blood Reviews

Infections, death and disparities

2020 , Page(s) 100775

Keywords	Oncology ; Hematology ; covid19
Language	English
Publisher	Elsevier BV
Publishing country	us
Document type	Article ; Online
ZDB-ID	639015-8
ISSN	1532-1681 ; 0268-960X
ISSN (online)	1532-1681
ISSN	0268-960X
DOI	10.1016/j.blre.2020.100775
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Immunotherapy-related adverse events (irAEs): extraction from FDA drug labels and comparative analysis.

Wang, QuanQiu / Xu, Rong

JAMIA open

2018 Volume 2, Issue 1, Page(s) 173–178

Abstract: Objectives: Immune checkpoint inhibitors (ICIs) have dramatically improved outcomes in cancer patients. However, ICIs are associated with significant immune-related adverse events (irAEs) and the underlying biological mechanisms are not well-understood. ...

Abstract	Objectives: Immune checkpoint inhibitors (ICIs) have dramatically improved outcomes in cancer patients. However, ICIs are associated with significant immune-related adverse events (irAEs) and the underlying biological mechanisms are not well-understood. To ensure safe cancer treatment, research efforts are needed to comprehensively detect and understand irAEs. Materials and methods: We manually extracted and standardized irAEs from The U.S Food and Drug Administration (FDA) drug labels for six FDA-approved ICIs. We compared irAE profile similarities among ICIs and 1507 FDA-approved non-ICI drugs. We investigated how irAEs have differential effects on human organs by classifying irAEs based on their targeted organ systems. Finally, we identified broad-spectrum (nontarget-specific) and narrow-spectrum (target-specific) irAEs. Results: A total of 893 irAEs were extracted. 31.4% irAEs were shared among ICIs as compared to the 8.0% between ICIs and non-ICI drugs. irAEs were resulted from both on- and off-target effects: irAE profiles were more similar for ICIs with same target than different targets, demonstrating the on-target effects; irAE profile similarity for ICIs with the same target is less than 50%, demonstrating unknown off-target effects. ICIs significantly target many organ systems, including endocrine system (3.4-fold enrichment), metabolism (3.7-fold enrichment), immune system (3.6-fold enrichment), and autoimmune system (4.8-fold enrichment). We identified 21 broad-spectrum irAEs shared among all ICIs, 20 irAEs specific for PD-L1/PD-1 inhibition, and 28 irAEs specific for CTLA-4 inhibition. Discussion and conclusion: Our study presents the first effort toward building a standardized database of irAEs. The extracted irAEs can serve as the gold standard for automatic irAE extractions from other data resources and set a foundation to understand biological mechanisms of irAEs.
Language	English
Publishing date	2018-10-15
Publishing country	United States
Document type	Journal Article
ISSN	2574-2531
ISSN (online)	2574-2531
DOI	10.1093/jamiaopen/ooy045
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