Separating Wheat from Chaff: Joining Biomedical Knowledge and Patient Data for Repurposing Medications

We present a system that jointly harnesses large-scale electronic health records data and a concept graph mined from the medical literature to guide drug repurposing—the process of applying known drugs in new ways to treat diseases. Our study is unique in methods and scope, per the scale of the concept graph and the quantity of data. We harness 10 years of nation-wide medical records of more than 1.5 million people and extract medical knowledge from all of PubMed, the world’s largest corpus of online biomedical literature. We employ links on the concept graph to provide causal signals to prioritize candidate influences between medications and target diseases. We show results of the system on studies of drug repurposing for hypertension and diabetes. In both cases, we present drug families identified by the algorithm which were previously unknown. We verify the results via clinical expert opinion and by prospective clinical trials on hypertension.

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‘Female Nectar’: A Study of Hybridity and Gender in Contemporary Tibetan Medical Literature on Menstruation

Asian Medicine ◽

10.1163/15734218-12341239 ◽

2012 ◽

Vol 6 (2) ◽

pp. 387-420 ◽

Cited By ~ 1

Author(s):

Jenny Bright

Keyword(s):

Medical Literature ◽

Medical Knowledge ◽

Tibetan Medicine ◽

Biomedical Knowledge ◽

Women And Gender ◽

Cultural Perceptions ◽

And Gender ◽

Key Aspects ◽

Medical Writers

Abstract This essay examines contemporary Tibetan medical literature that deals with menstruation, focusing on the relations among medical, religious and cultural perceptions of women and gender. Present-day medical writers present a hybrid account of menstruation, incorporating key aspects of Tibetan medicine, such as the refining processes of digestion and the red element, with biomedical knowledge, notably the role of hormones. The integration of biomedical thought by Tibetan writers works to substantiate and bolster the validity of Tibetan medical claims, rather than discredit them. Consequently, contemporary writers are able to articulate medical knowledge about women that is as much about Tibetan religious and cultural perceptions of gender and sexed-bodies, as it is ‘scientific’.

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Five analytic challenges in working with electronic health records data to support clinical trials with some solutions

Clinical Trials ◽

10.1177/1740774520931211 ◽

2020 ◽

Vol 17 (4) ◽

pp. 370-376

Author(s):

Benjamin A Goldstein

Keyword(s):

Clinical Trials ◽

Electronic Health Records ◽

Large Scale ◽

Point Of Care ◽

Cross Sectional ◽

Health Records ◽

Data Resource ◽

Electronic Health ◽

Data Elements ◽

Data Efficiency

Electronic health records data are becoming a key data resource in clinical research. Owing to issues of data efficiency, electronic health records data are being used for clinical trials. This includes both large-scale pragmatic trails and smaller—more focused—point-of-care trials. While electronic health records data open up a number of scientific opportunities, they also present a number of analytic challenges. This article discusses five particular challenges related to organizing electronic health records data for analytic purposes. These are as follows: (1) data are not organized for research purposes, (2) data are both densely and irregularly observed, (3) we don’t have all data elements we may want or need, (4) data are both cross-sectional and longitudinal, and (5) data may be informatively observed. While laying out these challenges, the article notes how many of these challenges can be addressed by careful and thoughtful study design as well as by integration of clinicians and informaticians into the analytic team.

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The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation

Journal of Medical Internet Research ◽

10.2196/21169 ◽

2020 ◽

Vol 22 (8) ◽

pp. e21169

Author(s):

Lyndsey Elaine Gates ◽

Ahmed Abdeen Hamed

Keyword(s):

Clinical Trials ◽

Language Processing ◽

Antiviral Drug ◽

Drug Repurposing ◽

Biomedical Literature ◽

Drug Candidates ◽

Fast Development ◽

Drug Knowledge ◽

Biomedical Publications ◽

Multiple Drugs

Background Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug, we explored the landscape of the SARS-CoV-2 biomedical publications to identify potential treatments. Objective The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials. Methods To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called “diversity.” A diversity score for a given drug was calculated by measuring how “diverse” a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease. Results From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. The top 10 candidates according to our algorithm are hydroxychloroquine, azithromycin, chloroquine, ritonavir, losartan, remdesivir, favipiravir, methylprednisolone, rapamycin, and tilorone dihydrochloride. Conclusions The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.

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Drug Repurposing for Parkinson’s Disease by Integrating Knowledge Graph Completion Model and Knowledge Fusion of Medical Literature

Future Internet ◽

10.3390/fi13010014 ◽

2021 ◽

Vol 13 (1) ◽

pp. 14

Author(s):

Xiaolin Zhang ◽

Chao Che

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Knowledge Base ◽

Medical Literature ◽

Medical Knowledge ◽

Drug Repurposing ◽

New Drugs ◽

Knowledge Graph ◽

Knowledge Fusion ◽

Effective Drugs

The prevalence of Parkinson’s disease increases a tremendous medical and economic burden to society. Therefore, the effective drugs are urgently required. However, the traditional development of effective drugs is costly and risky. Drug repurposing, which identifies new applications for existing drugs, is a feasible strategy for discovering new drugs for Parkinson’s disease. Drug repurposing is based on sufficient medical knowledge. The local medical knowledge base with manually labeled data contains a large number of accurate, but not novel, medical knowledge, while the medical literature containing the latest knowledge is difficult to utilize, because of unstructured data. This paper proposes a framework, named Drug Repurposing for Parkinson’s disease by integrating Knowledge Graph Completion method and Knowledge Fusion of medical literature data (DRKF) in order to make full use of a local medical knowledge base containing accurate knowledge and medical literature with novel knowledge. DRKF first extracts the relations that are related to Parkinson’s disease from medical literature and builds a medical literature knowledge graph. After that, the literature knowledge graph is fused with a local medical knowledge base that integrates several specific medical knowledge sources in order to construct a fused medical knowledge graph. Subsequently, knowledge graph completion methods are leveraged to predict the drug candidates for Parkinson’s disease by using the fused knowledge graph. Finally, we employ classic machine learning methods to repurpose the drug for Parkinson’s disease and compare the results with the method only using the literature-based knowledge graph in order to confirm the effectiveness of knowledge fusion. The experiment results demonstrate that our framework can achieve competitive performance, which confirms the effectiveness of our proposed DRKF for drug repurposing against Parkinson’s disease. It could be a supplement to traditional drug discovery methods.

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Network Life of Non-biomedical Knowledge

Journal of Digital Social Research ◽

10.33621/jdsr.v3i2.82 ◽

2021 ◽

Vol 3 (2) ◽

pp. 10-43

Author(s):

Dang Nguyen

Keyword(s):

Traditional Medicine ◽

Online Social Networks ◽

Large Scale ◽

Latent Dirichlet Allocation ◽

Social Dynamics ◽

Medical Knowledge ◽

Small World ◽

Biomedical Knowledge ◽

Indigenous Plants ◽

Network Characteristics

Traditional medicine is hugely popular throughout Southeast Asia and other parts of the world. The development of the internet and online social networks in these contexts has enabled a significant proliferation of non-biomedical knowledge and practices via platforms such as Facebook. People use Facebook to advocate for non- biomedical alternatives to unaffordable biomedicine, share family medical recipes, discuss medicinal properties of indigenous plants, buy and sell these plants, and even crowdsource disease diagnoses. This paper examines the network characteristics of, and discourses present within, three popular Vietnamese non-biomedical knowledge Facebook sites over a period of five years. These large-scale datasets are studied using social network analysis and generative statistical models for topic analysis (Latent Dirichlet allocation). Forty-nine unique topics were quantitatively identified and qualitatively interpreted. Among these topics, themes of religion and philanthropy, critical discussions of traditional medicine, and negotiations involving overseas Vietnamese were particularly notable. Although non-biomedical networks on Facebook are growing both in terms of scale and popularity, sub-network comment activities within these networks exhibit ‘small world’ characteristics. This suggests that social media seem to be replicating existing social dynamics that historically enable the maintenance of traditional forms of medical knowledge, rather than transforming them here.

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A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development (Preprint)

10.2196/preprints.17645 ◽

2019 ◽

Author(s):

Linfeng Li ◽

Peng Wang ◽

Yao Wang ◽

Shenghui Wang ◽

Jun Yan ◽

...

Keyword(s):

Medical Records ◽

Large Scale ◽

Semantic Representation ◽

Medical Knowledge ◽

Mapping Function ◽

Graph Algorithm ◽

Knowledge Graph ◽

Knowledge Graphs ◽

Representation Method ◽

Better Than

BACKGROUND Knowledge graph embedding is an effective semantic representation method for entities and relations in knowledge graphs. Several translation-based algorithms, including TransE, TransH, TransR, TransD, and TranSparse, have been proposed to learn effective embedding vectors from typical knowledge graphs in which the relations between head and tail entities are deterministic. However, in medical knowledge graphs, the relations between head and tail entities are inherently probabilistic. This difference introduces a challenge in embedding medical knowledge graphs. OBJECTIVE We aimed to address the challenge of how to learn the probability values of triplets into representation vectors by making enhancements to existing TransX (where X is E, H, R, D, or Sparse) algorithms, including the following: (1) constructing a mapping function between the score value and the probability, and (2) introducing probability-based loss of triplets into the original margin-based loss function. METHODS We performed the proposed PrTransX algorithm on a medical knowledge graph that we built from large-scale real-world electronic medical records data. We evaluated the embeddings using link prediction task. RESULTS Compared with the corresponding TransX algorithms, the proposed PrTransX performed better than the TransX model in all evaluation indicators, achieving a higher proportion of corrected entities ranked in the top 10 and normalized discounted cumulative gain of the top 10 predicted tail entities, and lower mean rank. CONCLUSIONS The proposed PrTransX successfully incorporated the uncertainty of the knowledge triplets into the embedding vectors.

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Expert-Augmented Computational Drug Repurposing Identified Baricitinib as a Treatment for COVID-19

Frontiers in Pharmacology ◽

10.3389/fphar.2021.709856 ◽

2021 ◽

Vol 12 ◽

Author(s):

Daniel P. Smith ◽

Olly Oechsle ◽

Michael J. Rawling ◽

Ed Savory ◽

Alix M.B. Lacoste ◽

...

Keyword(s):

Visual Analytics ◽

Drug Repurposing ◽

Standard Of Care ◽

Biomedical Literature ◽

Biological Research ◽

Knowledge Graph ◽

Neglected Diseases ◽

Biomedical Knowledge ◽

Computational Tools ◽

Approved Drugs

The onset of the 2019 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated the identification of approved drugs to treat the disease, before the development, approval and widespread administration of suitable vaccines. To identify such a drug, we used a visual analytics workflow where computational tools applied over an AI-enhanced biomedical knowledge graph were combined with human expertise. The workflow comprised rapid augmentation of knowledge graph information from recent literature using machine learning (ML) based extraction, with human-guided iterative queries of the graph. Using this workflow, we identified the rheumatoid arthritis drug baricitinib as both an antiviral and anti-inflammatory therapy. The effectiveness of baricitinib was substantiated by the recent publication of the data from the ACTT-2 randomised Phase 3 trial, followed by emergency approval for use by the FDA, and a report from the CoV-BARRIER trial confirming significant reductions in mortality with baricitinib compared to standard of care. Such methods that iteratively combine computational tools with human expertise hold promise for the identification of treatments for rare and neglected diseases and, beyond drug repurposing, in areas of biological research where relevant data may be lacking or hidden in the mass of available biomedical literature.

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A Comperehensive Drug Repurposing Study for Covid19 Treatment: Novel Putative DHODH Inhibitors Show Association to Serotonin-Dopamine Receptors

10.26434/chemrxiv.12751100 ◽

2020 ◽

Author(s):

Burak Berber ◽

Osman Doluca

Keyword(s):

Clinical Trials ◽

Dopamine Receptors ◽

Large Scale ◽

Drug Repurposing ◽

Dihydroorotate Dehydrogenase ◽

Docking Analysis ◽

Drug Candidates ◽

Dehydrogenase Enzyme

Here we discuss the potential of targeting Dihydroorotate dehydrogenase enzyme to help treat Covid 19. Next, we present a very large scale of docking analysis using 7900 drug candidates and 20 Dihydroorotate dehydrogenase structures. Our findings not only identify 28-FDA approved candidate molecules, but also show common characteristics among the candidates, especially their association with serotonin-dopamine receptors. In continuation, we discuss the existing clinical trials for Covid 19 treatment of some of the drug candidates we have identified, supporting that the rest are good candidates.

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Unlocking the Potential of Electronic Health Records for Translational Research

Yearbook of Medical Informatics ◽

10.1055/s-0038-1639444 ◽

2012 ◽

Vol 21 (01) ◽

pp. 135-138 ◽

Cited By ~ 1

Author(s):

Y. L. Yip ◽

Keyword(s):

Electronic Health Records ◽

Translational Research ◽

Large Scale ◽

Association Studies ◽

System Level ◽

Biomedical Knowledge ◽

Health Records ◽

Excellent Research ◽

Electronic Health ◽

Translational Informatics

SummaryTo review current excellent research and trend in the field of bioinformatics and translational informatics with direct application in the medical domain.Synopsis of the articles selected for the IMIA Yearbook 2012.Six excellent articles were selected in this Yearbook’s section on Bioinformatics and Translational Informatics. They exemplify current key advances in the use of patient information for translational research and health surveillance. First, two proof-of-concept studies demonstrated the cross-institutional and -geographic use of Electronic Health Records (EHR) for clinical trial subjects identification and drug safety signals detection. These reports pave ways to global large-scale population monitoring. Second, there is further evidence on the importance of coupling phenotypic information in EHR with genotypic information (either in biobank or in gene association studies) for new biomedical knowledge discovery. Third, patient data gathered via social media and self-reporting was found to be comparable to existent data and less labor intensive. This alternative means could potentially overcome data collection challenge in cohort and prospective studies. Finally, it can be noted that metagenomic studies are gaining momentum in bioinformatics and system-level analysis of human microbiome sheds important light on certain human diseases.The current literature showed that the traditional bench to bedside translational research is increasing being complemented by the reverse approach, in which bedside information can be used to provide novel biomedical insights.

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Multi-disease Predictive Analytics: A Clinical Knowledge-aware Approach

ACM Transactions on Management Information Systems ◽

10.1145/3447942 ◽

2021 ◽

Vol 12 (3) ◽

pp. 1-34

Author(s):

Lin Qiu ◽

Sruthi Gorantla ◽

Vaibhav Rajan ◽

Bernard C. Y. Tan

Keyword(s):

Rare Diseases ◽

Large Scale ◽

Predictive Analytics ◽

Predictive Performance ◽

Biomedical Literature ◽

Data Driven ◽

Clinical Knowledge ◽

Health Records ◽

Novel Method ◽

Clinical Correlations

Multi-Disease Predictive Analytics (MDPA) models simultaneously predict the risks of multiple diseases in patients and are valuable in early diagnoses. Patients tend to have multiple diseases simultaneously or develop multiple complications over time, and MDPA models can learn and effectively utilize such correlations between diseases. Data from large-scale Electronic Health Records (EHR) can be used through Multi-Label Learning (MLL) methods to develop MDPA models. However, data-driven approaches for MDPA face the challenge of data imbalance, because rare diseases tend to have much less data than common diseases. Insufficient data for rare diseases makes it difficult to leverage correlations with other diseases. These correlations are studied and recorded in biomedical literature but are rarely utilized in predictive analytics. This article presents a novel method called Knowledge-Aware Approach (KAA) that learns clinical correlations from the rapidly growing body of clinical knowledge. KAA can be combined with any data-driven MLL model for MDPA to refine the predictions of the model. Our extensive experiments, on real EHR data, show that the use of KAA improves the predictive performance of commonly used MDPA models, particularly for rare diseases. KAA is also found to be superior to existing general approaches of combining clinical knowledge with data-driven models. Further, a counterfactual analysis shows the efficacy of KAA in improving physicians’ ability to prescribe preventive treatments.

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