The inception of the BD2K Initiative is a testament to the foresight of NIH and our community. Clearly, the future of biomedicine rests on our collective ability to transform Big Data into intelligible scientific facts. In line with the BD2K objectives,our goal is to revolutionize how we address the universal challenge to discern meaning from unruly data. Capitalizing on our investigators'complementary strengths in computational biology and cardiovascular medicine, we will present a fusion of cutting-edge innovations that are grounded in a cardiovascular research focus, encompassing: (i) on-the-cloud data processing, (ii) crowd sourcing and text-mining data annotation, (iii) protein spatiotemporal dynamics, (iv) multi-omic integration, and (v) multiscale clinical data modeling. Drawing from our decade of experience in creating and refining bioinformatics tools, we propose to amalgamate established Big Data resources into a generalizable model for data annotation and collaborative research, through a new query system and cloud infrastructure for accessing multiple omics repositories, and through computational-supported crowdsourcing initiatives for mining the biomedical literature. We propose to interweave diverse data types for revealing biological networks that coalesce from molecular entities at multiple scales, through machine learning methods for structuring molecular data and defining relationships with drugs and diseases, and through novel algorithms for on-the-cloud integration and pathway visualization of multi-dimensional molecular data. Moreover, we propose to innovate advanced modeling tools to resolve protein dynamics and spatiotemporal molecular mechanisms, through mechanistic modeling of protein properties and 3D protein expression maps, and through Bayesian algorithms that correlate patient phenotypes, health histories, and multi-scale molecular profiles. The utility and customizability o our tools to the broader research population is clearly demonstrated using three archetypical workflows that enable annotations of large lists of genes, transcripts, proteins, or metabolites;powerful analysis of complex protein datasets acquired over time;and seamless aQoregation of diverse molecular, textual and literature data. These workflows will be rigorously validated using data from two significant clinical cohorts, the Jackson Heart Study and the Healthy Elderly Longevity (Wellderly). In parallel, a multifaceted strategy will be implemented to educate and train biomedical investigators, and to engage the public for promoting the overall BD2K initiative. We are convinced that a community-driven BD2K initiative will best realize its scientific potential and transform the research culture in a sustainable manner, exhibiting lasting success beyond the current funding period.
The challenges of biomedical Big Data are multifaceted. Biomedical investigators face daunting tasks of storing, analyzing, and distributing large-scale omics data, and aggregating all information to discern mechanistic insights. A coherent effort is required to harness disarrayed Big Data and transform them into intelligible scientific facts, whil engaging the global community via education and outreach programs. This Big Data Science Research proposal is designed to address these challenges by formulating a federated architecture of community-supported tools for enhancing data management, integration and analysis.
| Lindsey, Merry L (2018) Assigning matrix metalloproteinase roles in ischaemic cardiac remodelling. Nat Rev Cardiol 15:471-479 |
| Lindsey, Merry L; Mouton, Alan J; Ma, Yonggang (2018) Adding Reg3? to the acute coronary syndrome prognostic marker list. Int J Cardiol 258:24-25 |
| Brooks, Heddwen L; Lindsey, Merry L (2018) Guidelines for authors and reviewers on antibody use in physiology studies. Am J Physiol Heart Circ Physiol 314:H724-H732 |
| Yates 3rd, John R (2018) Content Is King: Databases Preserve the Collective Information of Science. J Biomol Tech 29:1-3 |
| Sallam, Tamer; Sandhu, Jaspreet; Tontonoz, Peter (2018) Long Noncoding RNA Discovery in Cardiovascular Disease: Decoding Form to Function. Circ Res 122:155-166 |
| Lindsey, Merry L; Bolli, Roberto; Canty Jr, John M et al. (2018) Guidelines for experimental models of myocardial ischemia and infarction. Am J Physiol Heart Circ Physiol 314:H812-H838 |
| Ma, Yonggang; Mouton, Alan J; Lindsey, Merry L (2018) Cardiac macrophage biology in the steady-state heart, the aging heart, and following myocardial infarction. Transl Res 191:15-28 |
| Fabregat, Antonio; Sidiropoulos, Konstantinos; Viteri, Guilherme et al. (2018) Reactome diagram viewer: data structures and strategies to boost performance. Bioinformatics 34:1208-1214 |
| Lindsey, Merry L; Gray, Gillian A; Wood, Susan K et al. (2018) Statistical considerations in reporting cardiovascular research. Am J Physiol Heart Circ Physiol 315:H303-H313 |
| Liem, David A; Murali, Sanjana; Sigdel, Dibakar et al. (2018) Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease. Am J Physiol Heart Circ Physiol 315:H910-H924 |
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