OBJECTIVE: This study sought to describe gender representation in leadership and recognition within the U.S. biomedical informatics community. MATERIALS AND METHODS: Data were collected from public websites or provided by American Medical Informatics Association (AMIA) personnel from 2017 to 2019, including gender of membership, directors of academic informatics programs, clinical informatics subspecialty fellowships, AMIA leadership (2014-2019), and AMIA awardees (1993-2019). Differences in gender proportions were calculated using chi-square tests. RESULTS: Men were more often in leadership positions and award recipients (P < .01). Men led 74.7% (n = 71 of 95) of academic informatics programs and 83.3% (n = 35 of 42) of clinical informatics fellowships. Within AMIA, men held 56.8% (n = 1086 of 1913) of leadership roles and received 64.1% (n = 59 of 92) of awards. DISCUSSION: As in other STEM fields, leadership and recognition in biomedical informatics is lower for women. CONCLUSIONS: Quantifying gender inequity should inform data-driven strategies to foster diversity and inclusion. Standardized collection and surveillance of demographic data within biomedical informatics is necessary.

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.

Pragmatic clinical trials often entail the use of electronic health record (EHR) and claims data, but bias and quality issues associated with these data can limit their fitness for research purposes particularly for study end points. Patient-reported health (PRH) data can be used to confirm or supplement EHR and claims data in pragmatic trials, but these data can bring their own biases. Moreover, PRH data can complicate analyses if they are discordant with other sources. Using experience in the design and conduct of multi-site pragmatic trials, we itemize the strengths and limitations of PRH data and identify situational criteria for determining when PRH data are appropriate or ideal to fill gaps in the evidence collected from EHRs. To provide guidance for the scientific rationale and appropriate use of patient-reported data in pragmatic clinical trials, we describe approaches for ascertaining and classifying study end points and addressing issues of incomplete data, data alignment, and concordance. We conclude by identifying areas that require more research.

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.

The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM's vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.

In the era of Big Data, omic-scale technologies, and increasing calls for data sharing, it is generally agreed that the use of community-developed, open data standards is critical. Far less agreed upon is exactly which data standards should be used, the criteria by which one should choose a standard, or even what constitutes a data standard. It is impossible simply to choose a domain and have it naturally follow which data standards should be used in all cases. The 'right' standards to use is often dependent on the use case scenarios for a given project. Potential downstream applications for the data, however, may not always be apparent at the time the data are generated. Similarly, technology evolves, adding further complexity. Would-be standards adopters must strike a balance between planning for the future and minimizing the burden of compliance. Better tools and resources are required to help guide this balancing act.

Clinical integrated data repositories (IDRs) are poised to become a foundational element of biomedical and translational research by providing the coordinated data sources necessary to conduct retrospective analytic research and to identify and recruit prospective research subjects. The Clinical and Translational Science Award (CTSA) consortium's Informatics IDR Group conducted a survey of 2010 consortium members to evaluate recent trends in IDR implementation and use to support research between 2008 and 2010. A web-based survey based in part on a prior 2008 survey was developed and deployed to 46 national CTSA centers. A total of 35 separate organizations completed the survey (74%), representing 28 CTSAs and the National Institutes of Health Clinical Center. Survey results suggest that individual organizations are progressing in their approaches to the development, management, and use of IDRs as a means to support a broad array of research. We describe the major trends and emerging practices below.

Last year, in 2011, we argued that biomedical informatics stands ready to revolutionize human health and healthcare using large-scale measurements on a large number of individuals.1 We anticipated that, with the coming changes in the amount and diversity of datasets, data-centric approaches that compute on massive amounts of data (often called ‘Big Data’2,3) to discover patterns and to make clinically relevant predictions would be increasingly common in translational bioinformatics. Given these trends, we programmed the 2012 Summit on Translational Bioinformatics to focus on research that takes us from base pairs to the bedside,4 with a particular emphasis on clinical implications of mining massive datasets, and bridging the latest multimodal measurement technologies with the large amounts of electronic healthcare data that are increasingly available. The coming year did turn out to be the year of Big Data for the Summit, with multiple submissions on managing and interpreting large datasets (figure 1). Among the 35 full paper submissions to the Summit, four stood out for their innovation, and hence the authors were invited to expand the work for this special issue of JAMIA—adding to the growing presence of translational bioinformatics in the journal.5–9 A tag cloud generated from the title and abstracts of the submissions made to the AMIA Translational Bioinformatics Summit 2012. The more frequently used the words are, the larger they appear. ‘Data’ was the most commonly mentioned word across all submissions for 2012. Liu et al10 demonstrated how the ability to predict adverse drug reactions can be increased by integrating chemical, biological, and phenotypic properties of drugs. They demonstrated that prediction accuracy increased from 0.9054 (when only chemical structures were used) to 0.9524 (when chemical structures along with biological and phenotypic features were used). They conclude that data fusion approaches are promising for large-scale adverse drug reaction predictions in both preclinical and post-marketing phases. Bhavnani et al11 assert that existing methods to analyze ancestral informative single-nucleotide polymorphisms (SNPs) (ie, SNPs that have large differences in genotype frequencies between two or more ancestral populations) identify a parsimonious set of SNPs that can identify distinct population clusters. However, existing methods do not directly visualize which clusters of subjects are related to which clusters of SNPs, or allow visualization of the genotypes that determine the cluster memberships. In an attempt to reveal such hidden relationships, they used three bipartite analytical representations (a bipartite network, a heat map with dendrograms, and a Circos ideogram) to simultaneously visualize clusters of subjects, SNPs, and the attributes that cause them to cluster. Seeking to maximize the utility of the abundance of available genome-wide association study (GWAS) data, Russu et al12 introduced a novel Bayesian model search algorithm, binary outcome stochastic search, for model selection when the number of predictors (eg, SNPs) far exceeds the number of observations. They propose an innovative stochastic model search technique where the relationship between the observed responses and the available predictors is described by a latent variable model with a probit link. They compare binary outcome stochastic search with three established methods (stepwise regression, logistic lasso, and elastic net) in a simulated study and in two real world studies to demonstrate higher precision (while preserving recall) in identifying SNPs associated with the observed outcome than the one obtained from established methods. Morgan et al,13 recipient of the Marco Ramoni Best Paper Award, constructed genomic disease risk summaries for 55 common diseases using reported gene–disease associations in the research literature. They constructed risk profiles based on the SNPs as well as on 187 whole-genome sequences and show that risk predictions derived from sequencing differ substantially from those obtained from the SNPs for several different non-monogenic diseases. When a large fraction of associated variants for a given disease is not covered by the genotyping array, the overall risk predictions can vary dramatically—by as much as a factor of 20 times in some instances. Beyond this year's conference papers, in the larger informatics community, researchers have demonstrated that GWAS can now be performed by leveraging large amounts of electronic medical record (EMR) data. For example, Kho et al showed that, by using commonly available data from five different EMRs, it is possible to accurately identify type 2 diabetes cases and controls for genetic study across multiple institutions.14 In addition, genomic sequencing has moved out of the research realm and established itself in the clinic. For example, at the Medical College of Wisconsin, Dr Howard Jacob's team used genome sequencing to identify a novel causal mutation that led to successful treatment of a 6-year-old boy with an extreme form of inflammatory bowel disease.15,16 Currently, the discussion of Big Data in translational informatics often connotes next-generation sequencing data.3,17,18 However, this is beginning to change: in 2011, the use of large public datasets of various kinds increased dramatically. The research activity around data mining for predicting adverse drug events (ADEs) using public data is an excellent example.19 Drug safety surveillance is currently based on spontaneous reporting systems, which contain reports of suspected ADEs seen in clinical practice. In the USA, the primary database for such reports is the Adverse Event Reporting System (AERS) database at the Food and Drug Administration. This resource has been successfully mined using ‘disproportionality measures’, which quantify the magnitude of difference between observed and expected rates of particular drug–ADE pairs.20,21 Given the amount of data available in AERS,22 researchers are developing methods for detecting new or latent multi-drug adverse events. Examples include using side effect profiles from AERs' reports to infer the presence of unreported adverse events,23–25 and creating a network of known drug–ADE relationships to predict as yet unknown ADEs before they are found in post-market evidence.26 Going beyond reported adverse events and making use of molecular level data, Pouliot et al27 generated logistic regression models to correlate and predict post-marketing ADEs based on screening data from PubChem, a public database of chemical structures of small organic molecules along with information about their biological activities. In a related effort, Vilar et al28 devised a way to enhance existing, data-mining algorithms with chemical information using molecular fingerprints—which represent molecules through a bit vector that codifies the existence of particular structural features or functional groups—to enhance ADE signals generated from adverse event reports. There have been increasing efforts to use other data sources, such as EMRs, for the purpose of detecting ADEs29–31 and to discover multi-drug ADEs.32 Researchers have also used billing and claims data for active drug safety surveillance33–35 and applied literature mining for drug safety.36 Recently, Chee et al37 explored the use of online health forums as a source of data to identify drugs for further scrutiny. They aggregate individuals' opinions of drugs in roughly 12 million personal health messages using natural language processing and are able to identify drugs withdrawn from the market based on messages discussing them before their removal. Looking ahead, we believe that Big Data in biomedical informatics will be far more than genome sequence data.38–40 We argue that Big Data must be considered in a comprehensive manner, including both large amounts of ‘molecular measurements' on a person (eg, sequencing) and small amounts of ‘routine measurements' on a large number of people (eg, clinical notes, laboratory measurements, claims data and adverse event reports). In contrast with the buzz around genomic-data-in-the-clinic or adverse event predictions, consider the example by Frankovich et al.41 When the existing literature and a survey of colleagues was insufficient to guide the clinical care of a patient, Frankovich et al applied trend analysis to the EMR data from 98 patients to ‘learn’ a data-driven guideline on how to provide care for a 13-year-old girl with systemic lupus erythematosus.41 Such data-centric approaches are particularly useful when derivation of a formal guideline is not feasible from a practical standpoint. It is tantalizing to imagine how scientific inquiry would be performed differently if we collect and share access to lots of data—both genomic and ‘routine’. How will the kinds of questions we ask change when we cross a certain data threshold?42,43 For example, researchers at Carnegie Mellon University built a scene completion tool by scraping millions of other images on the web from public sources. After the system accumulated a corpus of millions of photos, completed scenes were indistinguishable to the naked eye. The case for Big Data analytics has already won over the legal domain in at least one application, replacing armies of lawyers with computer algorithms designed for ‘e-discovery’—that is, retrieval of relevant materials for a legal case.44 Even the liberal arts are embracing Big Data: capitalizing on Google's efforts to digitize books, researchers in the humanities are blazing new trails in ‘culturomics' by examining language based on the analysis of word combinations occurring in millions of digitized books through time.45 In 2013, we will have the sixth Summit on Translational Bioinformatics and the third year of the AMIA Joint Summits on Translational Science. Translational research has become integral to the biomedical research enterprise, as evidenced by the creation of a National Center for Advancing Translational Science at the NIH. The Joint Summits continue to be a venue to facilitate dramatic changes that are underway to deliver quality, personalized healthcare in the USA without increasing spending at a rate exceeding the growth of the GDP.46 Reflecting this priority, the 2013 TBI Summit will have new tracks that will showcase the ways in which the translational sciences are having a significant impact on the way clinical care, biomedical research, and drug discovery are performed. We believe that the time is ripe for medicine to embrace Big Data, to usher in the age of data-driven medicine—and to truly enable proactive, predictive, preventive, participatory, and patient-centered health.47 Data-driven medicine will enable the discovery of new treatment options based on the multi-model molecular measurements on patients and learning from the trends hidden among the diagnoses, prescriptions, and discharge summaries of millions of patient encounters logged by clinical practitioners.48,49 The increasing synergy between the Translational Bioinformatics Summit and the Clinical Research Informatics Summit is an indication of this impending convergence. This is an exciting time when medicine begins utilizing massive amounts of data to discover patterns and trends and to make predictions in a manner that is a mainstay of web-scale computing.42 NHS is funded by the US National Institute of Health Roadmap (U54 HG004028 and U54 LM008748). JDT is funded by a Clinical and Translational Science Award (UL1 RR024128) and a gift from David H Murdock. None. Commissioned; internally peer reviewed.

The biomedical research community relies on a diverse set of resources, both within their own institutions and at other research centers. In addition, an increasing number of shared electronic resources have been developed. Without effective means to locate and query these resources, it is challenging, if not impossible, for investigators to be aware of the myriad resources available, or to effectively perform resource discovery when the need arises. In this paper, we describe the development and use of the Biomedical Resource Ontology (BRO) to enable semantic annotation and discovery of biomedical resources. We also describe the Resource Discovery System (RDS) which is a federated, inter-institutional pilot project that uses the BRO to facilitate resource discovery on the Internet. Through the RDS framework and its associated Biositemaps infrastructure, the BRO facilitates semantic search and discovery of biomedical resources, breaking down barriers and streamlining scientific research that will improve human health.

BACKGROUND: This article addresses the problem of interoperation of heterogeneous bioinformatics databases. RESULTS: We introduce BioWarehouse, an open source toolkit for constructing bioinformatics database warehouses using the MySQL and Oracle relational database managers. BioWarehouse integrates its component databases into a common representational framework within a single database management system, thus enabling multi-database queries using the Structured Query Language (SQL) but also facilitating a variety of database integration tasks such as comparative analysis and data mining. BioWarehouse currently supports the integration of a pathway-centric set of databases including ENZYME, KEGG, and BioCyc, and in addition the UniProt, GenBank, NCBI Taxonomy, and CMR databases, and the Gene Ontology. Loader tools, written in the C and JAVA languages, parse and load these databases into a relational database schema. The loaders also apply a degree of semantic normalization to their respective source data, decreasing semantic heterogeneity. The schema supports the following bioinformatics datatypes: chemical compounds, biochemical reactions, metabolic pathways, proteins, genes, nucleic acid sequences, features on protein and nucleic-acid sequences, organisms, organism taxonomies, and controlled vocabularies. As an application example, we applied BioWarehouse to determine the fraction of biochemically characterized enzyme activities for which no sequences exist in the public sequence databases. The answer is that no sequence exists for 36% of enzyme activities for which EC numbers have been assigned. These gaps in sequence data significantly limit the accuracy of genome annotation and metabolic pathway prediction, and are a barrier for metabolic engineering. Complex queries of this type provide examples of the value of the data warehousing approach to bioinformatics research. CONCLUSION: BioWarehouse embodies significant progress on the database integration problem for bioinformatics.