Achieving Inclusive Healthcare through Integrating Education and Research with AI and Personalized Curricula.

Precision medicine promises significant health benefits but faces challenges such as the need for complex data management and analytics, interdisciplinary collaboration, and education of researchers, healthcare professionals, and participants. Addressing these needs requires the integration of computational experts, engineers, designers, and healthcare professionals to develop user-friendly systems and shared terminologies. The widespread adoption of large language models (LLMs) like GPT-4 and Claude 3 highlights the importance of making complex data accessible to non-specialists. The Stanford Data Ocean (SDO) strives to mitigate these challenges through a scalable, cloud-based platform that supports data management for various data types, advanced research, and personalized learning in precision medicine. SDO provides AI tutors and AI-powered data visualization tools to enhance educational and research outcomes and make data analysis accessible for users from diverse educational backgrounds. By extending engagement and cutting-edge research capabilities globally, SDO particularly benefits economically disadvantaged and historically marginalized communities, fostering interdisciplinary biomedical research and bridging the gap between education and practical application in the biomedical field.

The Biomedical Resource Ontology (BRO) to enable resource discovery in clinical and translational research.

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.

Network analysis of genes regulated in renal diseases: implications for a molecular-based classification.

BACKGROUND: Chronic renal diseases are currently classified based on morphological similarities such as whether they produce predominantly inflammatory or non-inflammatory responses. However, such classifications do not reliably predict the course of the disease and its response to therapy. In contrast, recent studies in diseases such as breast cancer suggest that a classification which includes molecular information could lead to more accurate diagnoses and prediction of treatment response. This article describes how we extracted gene expression profiles from biopsies of patients with chronic renal diseases, and used network visualizations and associated quantitative measures to rapidly analyze similarities and differences between the diseases. RESULTS: The analysis revealed three main regularities: (1) Many genes associated with a single disease, and fewer genes associated with many diseases. (2) Unexpected combinations of renal diseases that share relatively large numbers of genes. (3) Uniform concordance in the regulation of all genes in the network. CONCLUSION: The overall results suggest the need to define a molecular-based classification of renal diseases, in addition to hypotheses for the unexpected patterns of shared genes and the uniformity in gene concordance. Furthermore, the results demonstrate the utility of network analyses to rapidly understand complex relationships between diseases and regulated genes.

BSD: the Biodegradative Strain Database.

The Biodegradative Strain Database (BSD) is a freely-accessible, web-based database providing detailed information on degradative bacteria and the hazardous substances that they degrade, including corresponding literature citations, relevant patents and links to additional web-based biological and chemical data. The BSD (http://bsd.cme.msu.edu) is being developed within the phylogenetic framework of the Ribosomal Database Project II (RDPII: http://rdp.cme.msu.edu/html) to provide a biological complement to the chemical and degradative pathway data of the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD: http://umbbd.ahc.umn.edu). Data is accessible through a series of strain, chemical and reference lists or by keyword search. The web site also includes on-line data submission and user survey forms to solicit user contributions and suggestions. The current release contains information on over 250 degradative bacterial strains and 150 hazardous substances. The transformation of xenobiotics and other environmentally toxic compounds by microorganisms is central to strategies for biocatalysis and the bioremediation of contaminated environments. However, practical, comprehensive, strain-level information on biocatalytic/biodegradative microbes is not readily available and is often difficult to compile. Similarly, for any given environmental contaminant, there is no single resource that can provide comparative information on the array of identified microbes capable of degrading the chemical. A web site that consolidates and cross-references strain, chemical and reference data related to biocatalysis, biotransformation, biodegradation and bioremediation would be an invaluable tool for academic and industrial researchers and environmental engineers.

User requirements for exploring a resource inventory for clinical research.

The CTSA Inventory of Resources Explorer facilitates searching and finding relevant biomedical resources in this rich, federated inventory. We used efficient and non-traditional formal usability methods to define requirements and to design the Explorer, which may be extended to similar web-based tools.

VISAGE: A Query Interface for Clinical Research.

We present the design and implementation of VISAGE (VISual AGgregator and Explorer), a query interface for clinical research. We follow a user-centered development approach and incorporate visual, ontological, searchable and explorative features in three interrelated components: Query Builder, Query Manager and Query Explorer. The Query Explorer provides novel on-line data mining capabilities for purposes such as hypothesis generation or cohort identification. The VISAGE query interface has been implemented as a significant component of Physio-MIMI, an NCRR-funded, multi-CTSA-site pilot project. Preliminary evaluation results show that VISAGE is more efficient for query construction than the i2b2 web-client.

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations.

BACKGROUND: In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods. FINDINGS: The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. CONCLUSIONS: The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.

The University of Michigan Honest Broker: a Web-based service for clinical and translational research and practice.

For the success of clinical and translational science, a seamless interoperation is required between clinical and research information technology. Addressing this need, the Michigan Clinical Research Collaboratory (MCRC) was created. The MCRC employed a standards-driven Web Services architecture to create the U-M Honest Broker, which enabled sharing of clinical and research data among medical disciplines and separate institutions. Design objectives were to facilitate sharing of data, maintain a master patient index (MPI), deidentification of data, and routing data to preauthorized destination systems for use in clinical care, research, or both. This article describes the architecture and design of the U-M HB system and the successful demonstration project. Seventy percent of eligible patients were recruited for a prospective study examining the correlation between interventional cardiac catheterizations and depression. The U-M Honest Broker delivered on the promise of using structured clinical knowledge shared among providers to help clinical and translational research.

A novel system for rapidly identifying toxic chemicals.

First-responders have a critical need to rapidly identify toxic chemicals during emergencies. However, current systems such as WISER require a large number of inputs before a chemical can be identified. Here we present a novel system which significantly reduces the number of inputs required to identify a toxic chemical.

Casepedia: a Web 2.0 case repository enabling collaborative learning.

Clinical cases play an instrumental role in medical school education and the continuing education of practicing physicians. To date, few case repositories are available that are interdisciplinary, searchable, or have properties enabling user contribution and feedback. In this research, we explored the design of such a system called Casepedia, using contextual inquiry and rapid prototyping techniques. Casepedia provides a Web 2.0 platform that will allow medical professionals to publish, comment on, and classify authentic cases.

The "Honest Broker" method of integrating interdisciplinary research data.

Multiple clinical informatics systems have been developed within separate departments of the University of Michigan Medical School. We are in the process of creating an "Honest Broker" method of safely and securely linking together data from different clinical systems for a research project studying the co-morbidity of depression and cardiovascular disease. The Michigan Clinical Research Collaboratory (MCRC) is an NIH/NHLBI Roadmap initiative funded to re-engineer the clinical research enterprise.