Publications by authors named "Philip R O Payne"

106 Publications

Pattern recognition in lymphoid malignancies using CytoGPS and Mercator.

BMC Bioinformatics 2021 Mar 1;22(1):100. Epub 2021 Mar 1.

Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.

Background: There have been many recent breakthroughs in processing and analyzing large-scale data sets in biomedical informatics. For example, the CytoGPS algorithm has enabled the use of text-based karyotypes by transforming them into a binary model. However, such advances are accompanied by new problems of data sparsity, heterogeneity, and noisiness that are magnified by the large-scale multidimensional nature of the data. To address these problems, we developed the Mercator R package, which processes and visualizes binary biomedical data. We use Mercator to address biomedical questions of cytogenetic patterns relating to lymphoid hematologic malignancies, which include a broad set of leukemias and lymphomas. Karyotype data are one of the most common form of genetic data collected on lymphoid malignancies, because karyotyping is part of the standard of care in these cancers.

Results: In this paper we combine the analytic power of CytoGPS and Mercator to perform a large-scale multidimensional pattern recognition study on 22,741 karyotype samples in 47 different hematologic malignancies obtained from the public Mitelman database.

Conclusion: Our findings indicate that Mercator was able to identify both known and novel cytogenetic patterns across different lymphoid malignancies, furthering our understanding of the genetics of these diseases.
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http://dx.doi.org/10.1186/s12859-021-03992-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7923511PMC
March 2021

Spot the difference: comparing results of analyses from real patient data and synthetic derivatives.

JAMIA Open 2020 Dec 14;3(4):557-566. Epub 2020 Dec 14.

Division of General Medical Sciences, Department of Medicine, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA.

Background: Synthetic data may provide a solution to researchers who wish to generate and share data in support of precision healthcare. Recent advances in data synthesis enable the creation and analysis of synthetic derivatives as if they were the original data; this process has significant advantages over data deidentification.

Objectives: To assess a big-data platform with data-synthesizing capabilities (MDClone Ltd., Beer Sheva, Israel) for its ability to produce data that can be used for research purposes while obviating privacy and confidentiality concerns.

Methods: We explored three use cases and tested the robustness of synthetic data by comparing the results of analyses using synthetic derivatives to analyses using the original data using traditional statistics, machine learning approaches, and spatial representations of the data. We designed these use cases with the purpose of conducting analyses at the observation level (Use Case 1), patient cohorts (Use Case 2), and population-level data (Use Case 3).

Results: For each use case, the results of the analyses were sufficiently statistically similar ( > 0.05) between the synthetic derivative and the real data to draw the same conclusions.

Discussion And Conclusion: This article presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in clinical research for faster insights and improved data sharing in support of precision healthcare.
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http://dx.doi.org/10.1093/jamiaopen/ooaa060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7886551PMC
December 2020

Comparison of Sepsis Definitions as Automated Criteria.

Crit Care Med 2021 04;49(4):e433-e443

Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO.

Objectives: Assess the impact of heterogeneity among established sepsis criteria (Sepsis-1, Sepsis-3, Centers for Disease Control and Prevention Adult Sepsis Event, and Centers for Medicare and Medicaid severe sepsis core measure 1) through the comparison of corresponding sepsis cohorts.

Design: Retrospective analysis of data extracted from electronic health record.

Setting: Single, tertiary-care center in St. Louis, MO.

Patients: Adult, nonsurgical inpatients admitted between January 1, 2012, and January 6, 2018.

Interventions: None.

Measurements And Main Results: In the electronic health record data, 286,759 encounters met inclusion criteria across the study period. Application of established sepsis criteria yielded cohorts varying in prevalence: Centers for Disease Control and Prevention Adult Sepsis Event (4.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (4.8%), International Classification of Disease code (7.2%), Sepsis-3 (7.5%), and Sepsis-1 (11.3%). Between the two modern established criteria, Sepsis-3 (n = 21,550) and Centers for Disease Control and Prevention Adult Sepsis Event (n = 12,494), the size of the overlap was 7,763. The sepsis cohorts also varied in time from admission to sepsis onset (hr): Sepsis-1 (2.9), Sepsis-3 (4.1), Centers for Disease Control and Prevention Adult Sepsis Event (4.6), and Centers for Medicare and Medicaid severe sepsis core measure 1 (7.6); sepsis discharge International Classification of Disease code rate: Sepsis-1 (37.4%), Sepsis-3 (40.1%), Centers for Medicare and Medicaid severe sepsis core measure 1 (48.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (54.5%); and inhospital mortality rate: Sepsis-1 (13.6%), Sepsis-3 (18.8%), International Classification of Disease code (20.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (22.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (24.1%).

Conclusions: The application of commonly used sepsis definitions on a single population produced sepsis cohorts with low agreement, significantly different baseline demographics, and clinical outcomes.
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http://dx.doi.org/10.1097/CCM.0000000000004875DOI Listing
April 2021

Computational analysis to repurpose drugs for COVID-19 based on transcriptional response of host cells to SARS-CoV-2.

BMC Med Inform Decis Mak 2021 01 7;21(1):15. Epub 2021 Jan 7.

Institute for Informatics (I2), Washington University in St. Louis School of Medicine, St. Louis, MO, USA.

Background: The Coronavirus Disease 2019 (COVID-19) pandemic has infected over 10 million people globally with a relatively high mortality rate. There are many therapeutics undergoing clinical trials, but there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), molecular signaling pathways of host cells play critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within the host cells. Drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment.

Methods: In this study, we developed a novel integrative analysis approach to identify the related molecular signaling pathways within host cells, and repurposed drugs as potentially effective treatments for COVID-19, based on the transcriptional response of host cells.

Results: We identified activated signaling pathways associated with the infection caused SARS-CoV-2 in human lung epithelial cells through integrative analysis. Then, the activated gene ontologies (GOs) and super GOs were identified. Signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly activated. Based on the identified signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. In addition to many drugs being evaluated in clinical trials, the dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support.

Conclusions: The integrative genomics data analysis and results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment.
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http://dx.doi.org/10.1186/s12911-020-01373-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7789899PMC
January 2021

Conceptual considerations for using EHR-based activity logs to measure clinician burnout and its effects.

J Am Med Inform Assoc 2020 Dec 22. Epub 2020 Dec 22.

Institute for Informatics, Washington University School of Medicine, St Louis, Missouri, USA.

Electronic health records (EHR) use is often considered a significant contributor to clinician burnout. Informatics researchers often measure clinical workload using EHR-derived audit logs and use it for quantifying the contribution of EHR use to clinician burnout. However, translating clinician workload measured using EHR-based audit logs into a meaningful burnout metric requires an alignment with the conceptual and theoretical principles of burnout. In this perspective, we describe a systems-oriented conceptual framework to achieve such an alignment and describe the pragmatic realization of this conceptual framework using 3 key dimensions: standardizing the measurement of EHR-based clinical work activities, implementing complementary measurements, and using appropriate instruments to assess burnout and its downstream outcomes. We discuss how careful considerations of such dimensions can help in augmenting EHR-based audit logs to measure factors that contribute to burnout and for meaningfully assessing downstream patient safety outcomes.
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http://dx.doi.org/10.1093/jamia/ocaa305DOI Listing
December 2020

Use of electronic health records to support a public health response to the COVID-19 pandemic in the United States: a perspective from 15 academic medical centers.

J Am Med Inform Assoc 2021 02;28(2):393-401

Department of Biomedical Informatics, University of California San Diego Health, La Jolla, California, USA.

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.
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http://dx.doi.org/10.1093/jamia/ocaa287DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7665546PMC
February 2021

Ten principles for data sharing and commercialization.

J Am Med Inform Assoc 2021 Mar;28(3):646-649

Cornell Tech, Cornell University, New York, New York, USA.

Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves. Through discussions with multiple stakeholders at various institutions, we have generated a set of guidelines with 10 key principles to guide the responsible and appropriate use and sharing of clinical data for the purposes of care and discovery. Industry, universities, and healthcare institutions can build upon these guidelines toward creating a responsible, ethical, and practical response to data sharing.
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http://dx.doi.org/10.1093/jamia/ocaa260DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7936510PMC
March 2021

CytoGPS: A large-scale karyotype analysis of CML data.

Cancer Genet 2020 10 2;248-249:34-38. Epub 2020 Oct 2.

Department of Biomedical Informatics, Wexner Medical Center, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH 43210, USA. Electronic address:

Karyotyping, the practice of visually examining and recording chromosomal abnormalities, is commonly used to diagnose diseases of genetic origin, including cancers. Karyotypes are recorded as text written in the International System for Human Cytogenetic Nomenclature (ISCN). Downstream analysis of karyotypes is conducted manually, due to the visual nature of analysis and the linguistic structure of the ISCN. The ISCN has not been computer-readable and, as such, prevents the full potential of these genomic data from being realized. In response, we developed CytoGPS, a platform to analyze large volumes of cytogenetic data using a Loss-Gain-Fusion model that converts the human-readable ISCN karyotypes into a machine-readable binary format. As proof of principle, we applied CytoGPS to cytogenetic data from the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer, a National Cancer Institute hosted database of over 69,000 karyotypes of human cancers. Using the Jaccard coefficient to determine similarity between karyotypes structured as binary vectors, we were able to identify novel patterns from 4,968 Mitelman CML karyotypes, such as the co-occurrence of trisomy 19 and 21. The CytoGPS platform unlocks the potential for large-scale, comparative analysis of cytogenetic data. This methodological platform is freely available at CytoGPS.org.
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http://dx.doi.org/10.1016/j.cancergen.2020.09.005DOI Listing
October 2020

Transcriptomics-Based Drug Repurposing Approach Identifies Novel Drugs against Sorafenib-Resistant Hepatocellular Carcinoma.

Cancers (Basel) 2020 Sep 23;12(10). Epub 2020 Sep 23.

Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43210, USA.

Hepatocellular carcinoma (HCC) is frequently diagnosed in patients with late-stage disease who are ineligible for curative surgical therapies. The majority of patients become resistant to sorafenib, the only approved first-line therapy for advanced cancer, underscoring the need for newer, more effective drugs. The purpose of this study is to expedite identification of novel drugs against sorafenib resistant (SR)-HCC. We employed a transcriptomics-based drug repurposing method termed connectivity mapping using gene signatures from in vitro-derived SR Huh7 HCC cells. For proof of concept validation, we focused on drugs that were FDA-approved or under clinical investigation and prioritized two anti-neoplastic agents (dasatinib and fostamatinib) with targets associated with HCC. We also prospectively validated predicted gene expression changes in drug-treated SR Huh7 cells as well as identified and validated the targets of Fostamatinib in HCC. Dasatinib specifically reduced the viability of SR-HCC cells that correlated with up-regulated activity of SRC family kinases, its targets, in our SR-HCC model. However, fostamatinib was able to inhibit both parental and SR HCC cells in vitro and in xenograft models. Ingenuity pathway analysis of fostamatinib gene expression signature from LINCS predicted JAK/STAT, PI3K/AKT, ERK/MAPK pathways as potential targets of fostamatinib that were validated by Western blot analysis. Fostamatinib treatment reversed the expression of genes that were deregulated in SR HCC. We provide proof of concept evidence for the validity of this drug repurposing approach for SR-HCC with implications for personalized medicine.
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http://dx.doi.org/10.3390/cancers12102730DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7598246PMC
September 2020

Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.

Methods Mol Biol 2021 ;2194:223-238

Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.

Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic drug combinations could help to overcome drug resistance. However, the number of possible drug combinations is enormous, and it is infeasible to experimentally screen all drug combinations with limited resources. Therefore, computational models to predict and prioritize effective drug combinations are important for combination therapy discovery. Compared with existing models, we propose a novel deep learning model, AuDNNsynergy, to predict the synergy of pairwise drug combinations by integrating multiomics data. Specifically, three autoencoders are trained using the gene expression, copy number, and genetic mutation data of tumor samples from The Cancer Genome Atlas (TCGA). Then the gene expression, copy number, and mutation of individual cancer cell lines are coded using the three trained autoencoders. The physicochemical features of individual drugs and the encoded omics data of individual cancer cell lines are used as the input features of a deep neural network that predicts the synergy score of given pairwise drug combinations against the specific cancer cell lines. The comparison results showed the proposed AuDNNsynergy model outperforms, specifically in terms of rank correlation metric, four state-of-the-art approaches, namely, DeepSynergy, Gradient Boosting Machines, Random Forests, and Elastic Nets.
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http://dx.doi.org/10.1007/978-1-0716-0849-4_12DOI Listing
March 2021

Transmission dynamics: Data sharing in the COVID-19 era.

Learn Health Syst 2020 Jun 28:e10235. Epub 2020 Jun 28.

Institute for Informatics Washington University School of Medicine St. Louis Missouri.

Problem: The current coronavirus disease 2019 (COVID-19) pandemic underscores the need for building and sustaining public health data infrastructure to support a rapid local, regional, national, and international response. Despite a historical context of public health crises, data sharing agreements and transactional standards do not uniformly exist between institutions which hamper a foundational infrastructure to meet data sharing and integration needs for the advancement of public health.

Approach: There is a growing need to apply population health knowledge with technological solutions to data transfer, integration, and reasoning, to improve health in a broader learning health system ecosystem. To achieve this, data must be combined from healthcare provider organizations, public health departments, and other settings. Public health entities are in a unique position to consume these data, however, most do not yet have the infrastructure required to integrate data sources and apply computable knowledge to combat this pandemic.

Outcomes: Herein, we describe lessons learned and a framework to address these needs, which focus on: (a) identifying and filling technology "gaps"; (b) pursuing collaborative design of data sharing requirements and transmission mechanisms; (c) facilitating cross-domain discussions involving legal and research compliance; and (d) establishing or participating in multi-institutional convening or coordinating activities.

Next Steps: While by no means a comprehensive evaluation of such issues, we envision that many of our experiences are universal. We hope those elucidated can serve as the catalyst for a robust community-wide dialogue on what steps can and should be taken to ensure that our regional and national health care systems can truly learn, in a rapid manner, so as to respond to this and future emergent public health crises.
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http://dx.doi.org/10.1002/lrh2.10235DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7323052PMC
June 2020

The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment.

J Am Med Inform Assoc 2021 03;28(3):427-443

National Center for Advancing Translational Science, Bethesda, Maryland, USA.

Objective: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.

Materials And Methods: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.

Results: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.

Conclusions: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.
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http://dx.doi.org/10.1093/jamia/ocaa196DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7454687PMC
March 2021

Mining reported adverse events induced by potential opioid-drug interactions.

JAMIA Open 2020 Apr 26;3(1):104-112. Epub 2020 Apr 26.

Institute for Informatics (I2), Washington University School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA.

Objective: Opioid-based analgesia is routinely used in clinical practice for the management of pain and alleviation of suffering at the end of life. It is well-known that opioid-based medications can be highly addictive, promoting not only abuse but also life-threatening overdoses. The scope of opioid-related adverse events (AEs) beyond these well-known effects remains poorly described. This exploratory analysis investigates potential AEs from drug-drug interactions between opioid and nonopioid medications (ODIs).

Materials And Methods: In this study, we conduct an initial exploration of the association between ODIs and severe AEs using millions of AE reports available in FDA Adverse Event Reporting System (FAERS). The odds ratio (OR)-based analysis and visualization are proposed for single drugs and pairwise ODIs to identify associations between AEs and ODIs of interest. Moreover, the multilabel (multi-AE) learning models are employed to evaluate the feasibility of AE prediction of polypharmacy.

Results: The top 12 most prescribed opioids in the FAERS are identified. The OR-based analysis identifies a diverse set of AEs associated with individual opioids. Moreover, the results indicate many ODIs can increase the risk of severe AEs dramatically. The area under the curve values of multilabel learning models of ODIs for oxycodone varied between 0.81 and 0.88 for 5 severe AEs.

Conclusions: The proposed data analysis and visualization are useful for mining FAERS data to identify novel polypharmacy associated AEs, as shown for ODIs. This approach was successful in recapitulating known drug interactions and also identified new opioid-specific AEs that could impact prescribing practices.
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http://dx.doi.org/10.1093/jamiaopen/ooz073DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7309259PMC
April 2020

Cognitive plausibility in voice-based AI health counselors.

NPJ Digit Med 2020 15;3:72. Epub 2020 May 15.

5Department of Medicine, University of Illinois at Chicago, Chicago, IL USA.

Voice-based personal assistants using artificial intelligence (AI) have been widely adopted and used in home-based settings. Their success has created considerable interest for its use in healthcare applications; one area of prolific growth in AI is that of voice-based virtual counselors for mental health and well-being. However, in spite of its promise, building realistic virtual counselors to achieve higher-order maturity levels beyond task-based interactions presents considerable conceptual and pragmatic challenges. We describe one such conceptual challenge-cognitive plausibility, defined as the ability of virtual counselors to emulate the human cognitive system by simulating how a skill or function is accomplished. An important cognitive plausibility consideration for voice-based agents is its ability to engage in meaningful and seamless interactive communication. Drawing on a broad interdisciplinary research literature and based on our experiences with developing two voice-based (voice-only) prototypes that are in the early phases of testing, we articulate two conceptual considerations for their design and use-conceptualizing voice-based virtual counselors as communicative agents and establishing virtual co-presence. We discuss why these conceptual considerations are important and how it can lead to the development of voice-based counselors for real-world use.
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http://dx.doi.org/10.1038/s41746-020-0278-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7229176PMC
May 2020

When past is not a prologue: Adapting informatics practice during a pandemic.

J Am Med Inform Assoc 2020 07;27(7):1142-1146

Institute for Informatics, Washington University School of Medicine, Washington University in St. Louis, St. Louis, Missouri, USA.

Data and information technology are key to every aspect of our response to the current coronavirus disease 2019 (COVID-19) pandemic-including the diagnosis of patients and delivery of care, the development of predictive models of disease spread, and the management of personnel and equipment. The increasing engagement of informaticians at the forefront of these efforts has been a fundamental shift, from an academic to an operational role. However, the past history of informatics as a scientific domain and an area of applied practice provides little guidance or prologue for the incredible challenges that we are now tasked with performing. Building on our recent experiences, we present 4 critical lessons learned that have helped shape our scalable, data-driven response to COVID-19. We describe each of these lessons within the context of specific solutions and strategies we applied in addressing the challenges that we faced.
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http://dx.doi.org/10.1093/jamia/ocaa073DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7188126PMC
July 2020

Language matters: precision health as a cross-cutting care, research and policy agenda.

J Am Med Inform Assoc 2020 04;27(4):658-661

Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA.

The biomedical research and healthcare delivery communities have increasingly come to focus their attention on the role of data and computation in order to improve the quality, safety, costs, and outcomes of both wellness promotion and care delivery. Depending on the scale of such efforts, and the environments in which they are situated, they are referred to variably as personalized or precision medicine, population health, clinical transformation, value-driven care, or value-based transformation. Despite the original intent of many efforts and publications that have sought to define personalized, precision, or data-driven approaches to improving health and wellness, the use of such terminology in current practice often treats said activities as discrete areas of endeavor within minimal cross-linkage across or between scales of inquiry. We believe that this current state creates numerous barriers that are preventing the advancement of relevant science, practice, and policy. As such, we believe that it is necessary to amplify and reaffirm our collective understanding that these fields share common means of inquiry, differentiated only by the units of measure being utilized, their sources of data, and the manner in which they are executed. Therefore, in this perspective, we explore and focus attention on such commonalities and then present a conceptual framework that links constituent activities into an integrated model that we refer to as a precision healthcare system. The presentation of this framework is intended to provide the basis for the types of shared, broad-based, and descriptive language needed to reference and realize such a framework.
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http://dx.doi.org/10.1093/jamia/ocaa009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7647305PMC
April 2020

A protocol to evaluate RNA sequencing normalization methods.

BMC Bioinformatics 2019 Dec 20;20(Suppl 24):679. Epub 2019 Dec 20.

Department Biomedical Informatics, Ohio State University, 250 Lincoln Tower, 1800 Cannon Dr. Columbus, Columbus, OH, 43210, USA.

Background: RNA sequencing technologies have allowed researchers to gain a better understanding of how the transcriptome affects disease. However, sequencing technologies often unintentionally introduce experimental error into RNA sequencing data. To counteract this, normalization methods are standardly applied with the intent of reducing the non-biologically derived variability inherent in transcriptomic measurements. However, the comparative efficacy of the various normalization techniques has not been tested in a standardized manner. Here we propose tests that evaluate numerous normalization techniques and applied them to a large-scale standard data set. These tests comprise a protocol that allows researchers to measure the amount of non-biological variability which is present in any data set after normalization has been performed, a crucial step to assessing the biological validity of data following normalization.

Results: In this study we present two tests to assess the validity of normalization methods applied to a large-scale data set collected for systematic evaluation purposes. We tested various RNASeq normalization procedures and concluded that transcripts per million (TPM) was the best performing normalization method based on its preservation of biological signal as compared to the other methods tested.

Conclusion: Normalization is of vital importance to accurately interpret the results of genomic and transcriptomic experiments. More work, however, needs to be performed to optimize normalization methods for RNASeq data. The present effort helps pave the way for more systematic evaluations of normalization methods across different platforms. With our proposed schema researchers can evaluate their own or future normalization methods to further improve the field of RNASeq normalization.
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http://dx.doi.org/10.1186/s12859-019-3247-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6923842PMC
December 2019

Using REDCap and Apple ResearchKit to integrate patient questionnaires and clinical decision support into the electronic health record to improve sexually transmitted infection testing in the emergency department.

J Am Med Inform Assoc 2020 02;27(2):265-273

Department of Medicine, Washington School of Medicine University in St. Louis, St. Louis, Missouri, USA.

Objective: Audio-enhanced computer-assisted self-interviews (ACASIs) are useful adjuncts for clinical care but are rarely integrated into the electronic health record (EHR). We created a flexible framework for integrating an ACASIs with clinical decision support (CDS) into the EHR. We used this program to identify adolescents at risk for sexually transmitted infections (STIs) in the emergency department (ED). We provide an overview of the software platform and qualitative user acceptance.

Materials And Methods: We created an ACASI with a CDS algorithm to identify adolescents in need of STI testing. We offered it to 15- to 21-year-old patients in our ED, regardless of ED complaint. We collected user feedback via the ACASI. These were programmed into REDCap (Research Electronic Data Capture), and an iOS application utilizing Apple ResearchKit generated a tablet compatible representation of the ACASI for patients. A custom software program created an HL7 (Health Level Seven) message containing a summary of responses, CDS recommendations, and STI test orders, which were transmitted to the EHR.

Results: In the first year, 1788 of 6227 (28.7%) eligible adolescents completed the survey. Technical issues led to decreased use for several months. Patients rated the system favorably, with 1583 of 1787 (88.9%) indicating that they were "somewhat" or "very comfortable" answering questions electronically and 1291 of 1787 (72.2%) preferring this format over face-to-face interviews or paper questionnaires.

Conclusions: We present a novel use for REDCap to combine patient-answered questionnaires and CDS to improve care for adolescents at risk for STIs. Our program was well received and the platform can be used across disparate patients, topics, and information technology infrastructures.
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http://dx.doi.org/10.1093/jamia/ocz182DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7025361PMC
February 2020

CytoGPS: a web-enabled karyotype analysis tool for cytogenetics.

Bioinformatics 2019 12;35(24):5365-5366

Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.

Summary: Karyotype data are the most common form of genetic data that is regularly used clinically. They are collected as part of the standard of care in many diseases, particularly in pediatric and cancer medicine contexts. Karyotypes are represented in a unique text-based format, with a syntax defined by the International System for human Cytogenetic Nomenclature (ISCN). While human-readable, ISCN is not intrinsically machine-readable. This limitation has prevented the full use of complex karyotype data in discovery science use cases. To enhance the utility and value of karyotype data, we developed a tool named CytoGPS. CytoGPS first parses ISCN karyotypes into a machine-readable format. It then converts the ISCN karyotype into a binary Loss-Gain-Fusion (LGF) model, which represents all cytogenetic abnormalities as combinations of loss, gain, or fusion events, in a format that is analyzable using modern computational methods. Such data is then made available for comprehensive 'downstream' analyses that previously were not feasible.

Availability And Implementation: Freely available at http://cytogps.org.
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http://dx.doi.org/10.1093/bioinformatics/btz520DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954647PMC
December 2019

Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes.

NPJ Syst Biol Appl 2019 26;5. Epub 2019 Feb 26.

Institute for Informatics, Washington University in St. Louis, St. Louis, MO, USA.

Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for -mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of -mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.
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http://dx.doi.org/10.1038/s41540-019-0085-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6391384PMC
April 2020

Questions for Artificial Intelligence in Health Care.

JAMA 2019 Jan;321(1):31-32

Institute for Informatics, Washington University School of Medicine, St Louis, Missouri.

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http://dx.doi.org/10.1001/jama.2018.18932DOI Listing
January 2019

The 'full stack' of healthcare innovation skills: combining clinical informatics with care delivery innovation.

Per Med 2019 01 22;16(1):9-14. Epub 2018 Nov 22.

Institute for Informatics (I2); Robert J. Terry Professor of General Medical Sciences, Washington University School of Medicine; Professor of Computer Science & Engineering, School of Engineering & Applied Science, Washington University St. Louis, MO 63110, USA.

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http://dx.doi.org/10.2217/pme-2018-0118DOI Listing
January 2019

Are Synthetic Data Derivatives the Future of Translational Medicine?

JACC Basic Transl Sci 2018 Oct 12;3(5):716-718. Epub 2018 Nov 12.

Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri.

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http://dx.doi.org/10.1016/j.jacbts.2018.08.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234614PMC
October 2018

Biomedical informatics meets data science: current state and future directions for interaction.

JAMIA Open 2018 Oct 9;1(2):136-141. Epub 2018 Aug 9.

Northwestern University, Feinberg School of Medicine, Center for Data Science and Informatics, Chicago, Illinois, USA.

There are an ever-increasing number of reports and commentaries that describe the challenges and opportunities associated with the use of big data and data science (DS) in the context of biomedical education, research, and practice. These publications argue that there are substantial benefits resulting from the use of data-centric approaches to solve complex biomedical problems, including an acceleration in the rate of scientific discovery, improved clinical decision making, and the ability to promote healthy behaviors at a population level. In addition, there is an aligned and emerging body of literature that describes the ethical, legal, and social issues that must be addressed to responsibly use big data in such contexts. At the same time, there has been growing recognition that the challenges and opportunities being attributed to the expansion in DS often parallel those experienced by the biomedical informatics community. Indeed, many informaticians would consider some of these issues relevant to the core theories and methods incumbent to the field of biomedical informatics science and practice. In response to this topic area, during the 2016 American College of Medical Informatics Winter Symposium, a series of presentations and focus group discussions intended to define the current state and identify future directions for interaction and collaboration between people who identify themselves as working on big data, DS, and biomedical informatics were conducted. We provide a perspective concerning these discussions and the outcomes of that meeting, and also present a set of recommendations that we have generated in response to a thematic analysis of those same outcomes. Ultimately, this report is intended to: (1) summarize the key issues currently being discussed by the biomedical informatics community as it seeks to better understand how to constructively interact with the emerging biomedical big data and DS fields; and (2) propose a framework and agenda that can serve to advance this type of constructive interaction, with mutual benefit accruing to both fields.
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http://dx.doi.org/10.1093/jamiaopen/ooy032DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951903PMC
October 2018

The diversity and disparity in biomedical informatics (DDBI) workshop.

Pac Symp Biocomput 2018 ;23:614-617

Department of Biochemistry & Molecular Biology, Howard University College of Medicine Washington, DC, USA,

The Diversity and Disparity in Biomedical Informatics (DDBI) workshop will be focused on complementary and critical issues concerned with enhancing diversity in the informatics workforce as well as diversity in patient cohorts. According to the National Institute of Minority Health and Health Disparities (NIMHD) at the NIH, diversity refers to the inclusion of the following traditionally underrepresented groups: African Americans/Blacks, Asians (>30 countries), American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, Latino or Hispanic (20 countries). Gender, culture, and socioeconomic status are also important dimensions of diversity, which may define some underrepresented groups. The under-representation of specific groups in both the biomedical informatics workforce as well as in the patient-derived data that is being used for research purposes has contributed to an ongoing disparity; these groups have not experienced equity in contributing to or benefiting from advancements in informatics research. This workshop will highlight innovative efforts to increase the pool of minority informaticians and discuss examples of informatics research that addresses the health concerns that impact minority populations. This workshop topics will provide insight into overcoming pipeline issues in the development of minority informaticians while emphasizing the importance of minority participation in health related research. The DDBI workshop will occur in two parts. Part I will discuss specific minority health & health disparities research topics and Part II will cover discussions related to overcoming pipeline issues in the training of minority informaticians.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5964987PMC
August 2018

Democratizing Health Data for Translational Research.

Pac Symp Biocomput 2018 ;23:240-246

Institute for Informatics, Washington University School of Medicine, St. Louis, MO, USA,

There is an expanding and intensive focus on the accessibility, reproducibility, and rigor of basic, clinical, and translational research. This focus complements the need to identify sustainable ways to generate actionable research results that improve human health. The principles and practices of open science offer a promising path to address both issues by facilitating: 1) increased transparency of data and methods which promotes research reproducibility and rigor; and 2) cumulative efficiencies wherein research tools and the output of research are combined to accelerate the delivery of new knowledge. While great strides have been in made in terms of enabling the open science paradigm in the biological sciences, progress in sharing of patient-derived health data has been more moderate. This lack of widespread access to common and well characterized health data is a substantial impediment to the timely, efficient, and multi-disciplinary conduct of translational research, particularly in those instances where hypotheses spanning multiple scales (from molecules to patients to populations) are being developed and tested. To address such challenges, we review current best practices and lessons learned, and explore the need for policy changes and technical innovation that can enhance the sharing of health data for translational research.
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August 2018

Diffusion mapping of drug targets on disease signaling network elements reveals drug combination strategies.

Pac Symp Biocomput 2018 ;23:92-103

Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, U.S.A.,

The emergence of drug resistance to traditional chemotherapy and newer targeted therapies in cancer patients is a major clinical challenge. Reactivation of the same or compensatory signaling pathways is a common class of drug resistance mechanisms. Employing drug combinations that inhibit multiple modules of reactivated signaling pathways is a promising strategy to overcome and prevent the onset of drug resistance. However, with thousands of available FDA-approved and investigational compounds, it is infeasible to experimentally screen millions of possible drug combinations with limited resources. Therefore, computational approaches are needed to constrain the search space and prioritize synergistic drug combinations for preclinical studies. In this study, we propose a novel approach for predicting drug combinations through investigating potential effects of drug targets on disease signaling network. We first construct a disease signaling network by integrating gene expression data with disease-associated driver genes. Individual drugs that can partially perturb the disease signaling network are then selected based on a drug-disease network "impact matrix", which is calculated using network diffusion distance from drug targets to signaling network elements. The selected drugs are subsequently clustered into communities (subgroups), which are proposed to share similar mechanisms of action. Finally, drug combinations are ranked according to maximal impact on signaling sub-networks from distinct mechanism-based communities. Our method is advantageous compared to other approaches in that it does not require large amounts drug dose response data, drug-induced "omics" profiles or clinical efficacy data, which are not often readily available. We validate our approach using a BRAF-mutant melanoma signaling network and combinatorial in vitro drug screening data, and report drug combinations with diverse mechanisms of action and opportunities for drug repositioning.
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August 2018

MD-Miner: a network-based approach for personalized drug repositioning.

BMC Syst Biol 2017 Oct 3;11(Suppl 5):86. Epub 2017 Oct 3.

Department of BioMedical Informatics (BMI), The Ohio State University, Columbus, OH, 43210, USA.

Background: Due to advances in next generation sequencing technologies and corresponding reductions in cost, it is now attainable to investigate genome-wide gene expression and variants at a patient-level, so as to better understand and anticipate heterogeneous responses to therapy. Consequently, it is feasible to inform personalized drug treatment decisions using personal genomics data. However, these efforts are limited due to a lack of reliable computational approaches for predicting effective drugs for individual patients. The reverse gene set enrichment analysis (i.e., connectivity mapping) approach and its variants have been widely and successfully used for drug prediction. However, the performance of these methods is limited by undefined mechanism of action (MoA) of drugs and reliance on cohorts of patients rather than personalized predictions for individual patients.

Results: In this study, we have developed and evaluated a computational approach, known as Mechanism and Drug Miner (MD-Miner), using a network-based computational approach to predict effective drugs and reveal potential drug mechanisms of action at the level of signaling pathways. Specifically, the patient-specific signaling network is constructed by integrating known disease associated genes with patient-derived gene expression profiles. In parallel, a drug mechanism of action network is constructed by integrating drug targets and z-score profiles of drug-induced gene expression (pre vs. post-drug treatment). Potentially effective candidate drugs are prioritized according to the number of common genes between the patient-specific dysfunctional signaling network and drug MoA network. We evaluated the MD-Miner method on the PC-3 prostate cancer cell line, and showed that it significantly improved the success rate of discovering effective drugs compared with the random selection, and could provide insight into potential mechanisms of action.

Conclusions: This work provides a signaling network-based drug repositioning approach. Compared with the reverse gene signature based drug repositioning approaches, the proposed method can provide clues of mechanism of action in terms of signaling transduction networks.
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http://dx.doi.org/10.1186/s12918-017-0462-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5629618PMC
October 2017

Integrative network and transcriptomics-based approach predicts genotype- specific drug combinations for melanoma.

AMIA Jt Summits Transl Sci Proc 2017 26;2017:247-256. Epub 2017 Jul 26.

Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.

Computational methods for drug combination predictions are needed to identify effective therapies that improve durability and prevent drug resistance in an efficient manner. In this paper, we present SynGeNet, a computational method that integrates transcriptomics data characterizing disease and drug z-score profiles with network mining algorithms in order to predict synergistic drug combinations. We compare SynGeNet to other available transcriptomics-based tools to predict drug combinations validated across melanoma cell lines in three genotype groups: BRAF-mutant, NRAS-mutant and combined. We showed that SynGeNet outperforms other available tools in predicting validated drug combinations and single agents tested as part of additional drug pairs. Interestingly, we observed that the performance of SynGeNet decreased when the network construction step was removed and improved when the proportion of matched-genotype validation cell lines increased. These results suggest that delineating functional information from transcriptomics data via network mining and genomic features can improve drug combination predictions.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5543336PMC
July 2017

OPEN DATA FOR DISCOVERY SCIENCE.

Pac Symp Biocomput 2017 ;22:649-652

Washington University Institute for Informatics, Washington University in St. Louis School of Medicine, St. Louis, MO 63130, United States of America,

The modern healthcare and life sciences ecosystem is moving towards an increasingly open and data-centric approach to discovery science. This evolving paradigm is predicated on a complex set of information needs related to our collective ability to share, discover, reuse, integrate, and analyze open biological, clinical, and population level data resources of varying composition, granularity, and syntactic or semantic consistency. Such an evolution is further impacted by a concomitant growth in the size of data sets that can and should be employed for both hypothesis discovery and testing. When such open data can be accessed and employed for discovery purposes, a broad spectrum of high impact end-points is made possible. These span the spectrum from identification of de novo biomarker complexes that can inform precision medicine, to the repositioning or repurposing of extant agents for new and cost-effective therapies, to the assessment of population level influences on disease and wellness. Of note, these types of uses of open data can be either primary, wherein open data is the substantive basis for inquiry, or secondary, wherein open data is used to augment or enrich project-specific or proprietary data that is not open in and of itself. This workshop is concerned with the key challenges, opportunities, and methodological best practices whereby open data can be used to drive the advancement of discovery science in all of the aforementioned capacities.
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http://dx.doi.org/10.1142/9789813207813_0061DOI Listing
March 2017