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Role of Bioinformatics in Integrating Various Data and Biomarker Discovery

There is a need for integration of high-throughput biological data with less structured clinical data for discovery of biomarkers. A data integration strategy has been described that implements a clinical and biological database and a wiki interface (Sorani et al. 2010). The authors integrated parameters across clinical trials and associated genetic, gene expression and protein data to explore disease heterogeneity and develop predictive biomarkers. They undertook extensive clinical data standardization and biological data summarization to support biomarker discovery in rheumatoid arthritis. According to the authors of this study, a key enabler for future integration efforts will be the prospective adoption of standard clinical trial data nomenclature using a controlled vocabulary and ontology. Such standardization could facilitate future data loading, integration and cross-trial analysis and, ultimately, biomarker and drug discovery efforts.

Ariana Pharma’s KEM® (knowledge extraction and management) rules-based data mining analytics software can be applied to identify predictive biomarkers of clinical adverse events, safety and efficacy. It can also identify biomarkers for potential applications in prognostic and companion diagnostic assays.

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System-Subsystem Dependency Network for Integrating Multicomponent Data and Application to Health Sciences
Abstract. Two features are commonly observed in large and complex systems. First, a system is made up of multiple subsystems. Second there exists fragmented data. A methodological challenge is to reconcile the potential parametric inconsistency across individually calibrated subsystems. This study aims...
(Social Computing, Behavioral-Cultural Modeling and Prediction)
Role of the Patient and its Data
As described in the previous section, data is of high importance. This data cannot be collected without patients and their cooperation is crucial on several levels. This leads to a very central role for the patient and, in addition, to a special nature of medical data and its acquisition compared to...
(Opening Science)
Integrity in data and systems
For a techno-starter, the effectiveness of the development function is key to success and safeguarding the integrity of the research and development data is essential. It is important that the data created during experiments and testing are correct and fit for purpose. But this does not happen by itself....
(Success in Innovation: Improving the Odds by Understanding the Factors for Unsuccess)
Experiment-Model Integration with Data from Global Change Experiments and Observational Networks
As mentioned earlier, traditionally, process-based models are usually validated by comparing model simulations with data from ecological observations or experiments. This validation, however, can be biased by limited availability of observations or experiments. Recently, large-scale benchmarking, which...
(Terrestrial ecosystem research infrastructures : challenges and opportunities)
Role of Bioinformatics in Discovery of Protein Biomarkers
Developments in proteomic technology offer tremendous potential to yield novel biomarkers that are translatable to routine clinical use but major hurdles remain for translation into clinical application. There is a need for rigorous experimental design and methods to validate some of the unproven methods...
(The handbook of biomarkers)
Role of Bioinformatics in Detection of Cancer Biomarkers
Cancer biomarkers are described in Chap. 13. Bioinformatics is applied for the exploration of cancer-related biomarkers, such as predisposition markers, diagnostics markers, prognostics markers, and therapeutics markers. Because quite large amounts of data are produced by the whole genome SNP scanning,...
(The handbook of biomarkers)
Role of Bioinformatics in Search for Biomarkers of Aging
System wide functional and structural changes caused by the aging process encourage the implementation of new bioinformatics search strategies for markers of aging. Combinatorial biomarkers should be particularly favored, as they can quantify processes on multiple levels of biological organization and...
(The handbook of biomarkers)
Integrating Time-Series Data with Geospatial Systems
In addition to using time-series monitoring of critical infrastructure assets, it has recently become possible to monitor the performance of wide-area critical assets and facilities in real time by integrating real-time operations data into geospatial analytics and displays. Esri, the worldwide leader...
(Digital Transformation for the Process Industries: A Roadmap)
Integrated Safety Data Analysis
Crash causation data analysis as related to transportation infrastructure design and operations is complex. Often such analysis requires the integration of traffic data (e.g., flow rate and flow speed), roadway geometric (e.g., lane width, shoulder types, pavement condition), weather (e.g., precipitation),...
(Principles and Practices of Transportation Planning and Engineering:A Foundational Reference for Professionals and Textbook for Students Focusing on Civil & Transportation Engineering, Planning, Environmental Science, and Project Management)