We live in the genomic era of medicine, where a patient's genomic/molecular data is becoming increasingly important for disease diagnosis, identification of targeted therapy, and risk assessment for adverse reactions. However, decoding the genomic test results and integrating it with clinical data for retrospective studies and cohort identification for prospective clinical trials is still a challenging task. In order to overcome these barriers, we developed an overarching enterprise informatics framework for translational research and personalized medicine called Synergistic Patient and Research Knowledge Systems (SPARKS) and a suite of tools called Oncology Data Retrieval Systems (OncDRS). OncDRS enables seamless data integration, secure and self-navigated query and extraction of clinical and genomic data from heterogeneous sources. Within a year of release, the system has facilitated more than 1500 research queries and has delivered data for more than 50 research studies.

OncDRS: An integrative clinical and genomic data platform for enabling translational research and precision medicine

Response to Comment on 'Nutrition Screening vs Nutrition Assessment: What's the Difference?'

Translational research is a buzzword which dominates discussions about the quality, the utilization, and the benefits of (bio)medical research. Yet, although translational research has become a prominent topic, no commonly agreed definition of this terminology exists. Instead, experts from different contexts such as biomedical research, clinical practice or nursing discuss translational research in multiple ways depending on how they define the problem that translational research is supposed to be the solution to. In this paper, we do not seek to find a 'correct' definition of translational research, but instead ask how actors using this terminology for describing their own research make sense of it. To do so, we asked three questions: Which actors are engaged in the debate about translational research? What kind of different meanings of translational research exist? And, which actors refer to which meaning of translational research when using the term? In answering these questions, we highlight the role this terminology plays in defining what medical science is about today.

The multiple meanings of translational research in (bio)medical research.

Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform.

/pdf/lynx-web-services-for-annotations-and-systems-analysis-of-5fwm8p6mw1.pdf

Lynx web services for annotations and systems analysis of multi-gene disorders

Multi-assignment clustering: Machine learning from a biological perspective.

Recent technological advances in genomics now allow producing biological data at unprecedented tera- and petabyte scales. Yet, the extraction of useful knowledge from this voluminous data presents a significant challenge to a scientific community. Efficient mining of vast and complex data sets for the needs of biomedical research critically depends on seamless integration of clinical, genomic, and experimental information with prior knowledge about genotype–phenotype relationships accumulated in a plethora of publicly available databases. Furthermore, such experimental data should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining. Translational projects require sophisticated approaches that coordinate and perform various analytical steps involved in the extraction of useful knowledge from accumulated clinical and experimental data in an orderly semiautomated manner. It presents a number of challenges such as (1) high-throughput data management involving data transfer, data storage, and access control; (2) scalable computational infrastructure; and (3) analysis of large-scale multidimensional data for the extraction of actionable knowledge.

High-Throughput Translational Medicine: Challenges and Solutions

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High-Throughput Translational Medicine: Challenges and Solutions