Lewis J. Frey

An emerging theory about racial differences in cancer risk and outcomes is that psychological and social stressors influence cellular stress responses; however, limited empirical data are available on racial differences in cellular stress responses among men who are at risk for adverse prostate cancer outcomes. In this study, we undertook a systems approach to examine molecular profiles and cellular stress responses in an important segment of African American (AA) and European American (EA) men: men undergoing prostate biopsy. We assessed the prostate transcriptome with a single biopsy core via high throughput RNA sequencing (RNA-Seq). Transcriptomic analyses uncovered impacted biological pathways including PI3K-Akt signaling pathway, Neuroactive ligand-receptor interaction pathway, and ECM-receptor interaction. Additionally, 187 genes mapping to the Gene Ontology (GO) terms RNA binding, structural constituent of ribosome, SRP-dependent co-translational protein targeting to membrane and the biological pathways, translation, L13a-mediated translational silencing of Ceruloplasmin expression were differentially expressed (DE) between EA and AA. This signature allowed separation of AA and EA patients, and AA patients with the most severe clinical characteristics. AA patients with elevated expression levels of this genomic signature presented with higher Gleason scores, a greater number of positive core biopsies, elevated dehydroepiandrosterone sulfate levels and serum vitamin D deficiency. Protein-protein interaction (PPI) network analysis revealed a high degree of connectivity between these 187 proteins.

Glycomics researchers have identified the need for integrated database systems for collecting glycomics information in a consistent format. The goal is to create a resource for knowledge discovery and dissemination to wider research communities. This has the potential and has exhibited initial success, to extend the research community to include biologists, clinicians, chemists, and computer scientists. This chapter discusses the technology and approach needed to create integrated data resources and informatics ecosystems to empower the broader community to leverage extant glycomics data. The focus is on glycosaminoglycan (GAGs) and proteoglycan research, but the approach can be generalized. The methods described span the development of glycomics standards from CarbBank to Glyco Connection Tables. Integrated data sets provide a foundation for novel methods of analysis such as machine learning and deep learning for knowledge discovery. The implications of predictive analysis are examined in relation to disease biomarker to expand the target audience of GAG and proteoglycan research.

Precision medicine informatics is a field of research that incorporates learning systems that generate new knowledge to improve individualized treatments using integrated data sets and models. Given the ever-increasing volumes of data that are relevant to patient care, artificial intelligence (AI) pipelines need to be a central component of such research to speed discovery. Applying AI methodology to complex multidisciplinary information retrieval can support efforts to discover bridging concepts within collaborating communities. This dovetails with precision medicine research, given the information rich multi-omic data that are used in precision medicine analysis pipelines. In this perspective article we define a prototype AI pipeline to facilitate discovering research connections between bioinformatics and clinical researchers. We propose building knowledge representations that are iteratively improved through AI and human-informed learning feedback loops supported through crowdsourcing. To illustrate this, we will explore the specific use case of nonalcoholic fatty liver disease, a growing health care problem. We will examine AI pipeline construction and utilization in relation to bench-to-bedside bridging concepts with interconnecting knowledge representations applicable to bioinformatics researchers and clinicians.

The integration of phenotypes and genotypes is at an unprecedented level and offers new opportunities to establish deep phenotypes. There are a number of challenges to overcome, specifically, accelerated growth of data, data silos, incompleteness, inaccuracies, and heterogeneity within and across data sources. This perspective report discusses artificial intelligence (AI) approaches that hold promise in addressing these challenges by automating computable phenotypes and integrating them with genotypes. Collaborations between biomedical and AI researchers will be highlighted in order to describe initial successes with an eye toward the future.

With the rapid growth of health-related data including genomic, proteomic, imaging and clinical, the arduous task of data integration can be overwhelmed by the complexity of the environment includi...

This special issue on precision medicine informatics flowed from the AMIA 2015 Translational Bioinformatics Summit theme of “Accelerating Precision Medicine”1 and President Obama’s 2015 State of the Union call “to give all of us access to the personalized information we need to keep ourselves and our families healthier.”2 The goal is to focus on the inherent translational informatics challenges, concerns, and opportunities afforded by precision medicine to provide an accurate, personalized characterization of patient populations based on various characteristics including molecular (eg, genomic, proteomic), clinical (eg, comorbidities), environmental exposures, lifestyle, patient preferences, and other information.4–13 Informatics is a necessary component in a comprehensive initiative to tackle precision medicine. Its roles include (1) managing big data,15–17 (2) creating learning systems for knowledge generation,18–21 (3) providing access for individual involvement,22–25 and (4) ultimately supporting optimal delivery of precision treatments derived from translational research.26–29 The papers submitted for this special issue cover these 4 areas and expand on the themes of informatics emerging in the field of precision medicine. Data become “big” when the scale of the data and analyses challenge our traditional systems in size, velocity, or complexity.8 The large scale of the Million Veterans Program is an example, with systems and analysis methods being developed for application in a repository that has established policies and procedures. The Million Veterans Program can serve as a model for involving thousands of individuals in precision medicine research.30 Similarly, the Precision Medicine Initiative Cohort …

Objectives To examine the feasibility of deploying a virtual web service for sharing data within a research network, and to evaluate the impact on data consistency and quality. Material and Methods Virtual machines (VMs) encapsulated an open-source, semantically and syntactically interoperable secure web service infrastructure along with a shadow database. The VMs were deployed to 8 Collaborative Pediatric Critical Care Research Network Clinical Centers. Results Virtual web services could be deployed in hours. The interoperability of the web services reduced format misalignment from 56% to 1% and demonstrated that 99% of the data consistently transferred using the data dictionary and 1% needed human curation. Conclusions Use of virtualized open-source secure web service technology could enable direct electronic abstraction of data from hospital databases for research purposes.

Entity-attribute-value (EAV) tables are widely used to store data in electronic medical records and clinical study data management systems. Before they can be used by various analytical (e.g., data mining and machine learning) programs, EAV-modeled data usually must be transformed into conventional relational table format through pivot operations. This time-consuming and resource-intensive process is often performed repeatedly on a regular basis, e.g., to provide a daily refresh of the content in a clinical data warehouse. Thus, it would be beneficial to make pivot operations as efficient as possible. In this paper, we present three techniques for improving the efficiency of pivot operations: 1) filtering out EAV tuples related to unneeded clinical parameters early on; 2) supporting pivoting across multiple EAV tables; and 3) conducting multi-query optimization. We demonstrate the effectiveness of our techniques through implementation. We show that our optimized execution method of pivoting using these techniques significantly outperforms the current basic execution method of pivoting. Our techniques can be used to build a data extraction tool to simplify the specification of and improve the efficiency of extracting data from the EAV tables in electronic medical records and clinical study data management systems.

Glycomics researchers have identified the need for integrated database systems for collecting glycomics information in a consistent format. The goal is to create a resource for knowledge discovery and dissemination to wider research communities. This has the potential to extend the research community to include biologists, clinicians, chemists, and computer scientists. This chapter discusses the technology and approach needed to create integrated data resources to empower the broader community to leverage extant glycomics data. The focus is on glycosaminoglycan (GAGs) and proteoglycan research, but the approach can be generalized. The methods described span the development of glycomics standards from CarbBank to Glyco Connection Tables. The existence of integrated data sets provides a foundation for novel methods of analysis such as machine learning for knowledge discovery. The implications of predictive analysis are examined in relation to disease biomarker to expand the target audience of GAG and proteoglycan research.

Summary: Objectives: Given the quickening speed of discovery of variant disease drivers from combined patient genotype and phenotype data, the objective is to provide methodology using big data technology to support the definition of deep phenotypes in medical records. Methods: As the vast stores of genomic information increase with next generation sequencing, the importance of deep phenotyping increases. The growth of genomic data and adoption of Electronic Health Records (EHR) in medicine provides a unique opportunity to integrate phenotype and genotype data into medical records. The method by which collections of clinical findings and other health related data are leveraged to form meaningful phenotypes is an active area of research. Longitudinal data stored in EHRs provide a wealth of information that can be used to construct phenotypes of patients. We focus on a practical problem around data integration for deep phenotype identification within EHR data. The use of big data approaches are described that enable scalable markup of EHR events that can be used for semantic and temporal similarity analysis to support the identification of phenotype and genotype relationships. Conclusions: Stead and colleagues’ 2005 concept of using light standards to increase the productivity of software systems by riding on the wave of hardware/processing power is described as a harbinger for designing future healthcare systems. The big data solution, using flexible markup, provides a route to improved utilization of processing power for organizing patient records in genotype and phenotype research.