DATA SCIENCE RESEARCH BACKGROUND AND SIGNIFICANCE Biology in the 21st century has emerged as a """"""""big data"""""""" science on par with physics or astronomy. Beginning with the landmark sequencing projects over a decade ago [1, 2], there have been successive waves of technological breakthroughs in probing cellular information on a genome-wide scale: microarrays [3], next generation sequencing [4], large-scale proteomics [5] and their many derivatives [6, 7]. Quick and widespread adoption of high throughput technologies has created massive amounts of data, yet there is a consensus that the floodgates have only barely opened [8]. The explosive growth of data volume has fostered intense research in the development of informatics tools to store, manage and analyze such data [9]. However, the scale and efficiency of the analysis is lagging behind the generation of data, a fact recognized by the major national funding agencies, with the result that the true potential of the data to accelerate biological discovery is not being realized. Analysis of biological data today is hampered by two major bottlenecks: (1) Integration: Different biotechnological tools record different kinds of cellular activities that provide complementary views of the same underlying biological phenomena. However, it has proved extremely difficult to integrate those partial descriptions into a well-organized whole, even though the advantages of such an integrative analysis of diverse data types are well recognized [10]. (2) Scalability: The challenge of data integration is generally met with the most heavy-duty machine learning techniques of the day [10], which typically do not scale well with data size. Biology needs analysis tools that can handle the data deluge of its modern """"""""omics"""""""" era. We propose to develop an E-science framework that will address the issues of integrative analysis and scalability associated with big data analysis in biology. We will build this environment from the ground up, laying its algorithmic foundations, engineering the scalable systems that form its skeleton frame, and creating the human-computer interface that makes it hospitable.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Specialized Center--Cooperative Agreements (U54)
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Special Emphasis Panel (ZRG1-BST-R (52))
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Lyster, Peter
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University of Illinois Urbana-Champaign
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Athreya, Arjun; Iyer, Ravishankar; Neavin, Drew et al. (2018) Augmentation of Physician Assessments with Multi-Omics Enhances Predictability of Drug Response: A Case Study of Major Depressive Disorder. IEEE Comput Intell Mag 13:20-31
Zhang, Yi; Manjunath, Mohith; Kim, Yeonsung et al. (2018) SequencEnG: an Interactive Knowledge Base of Sequencing Techniques. Bioinformatics :
Shi, Yu; Gui, Huan; Zhu, Qi et al. (2018) AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks. Proc SIAM Int Conf Data Min 2018:144-152
Baheti, Saurabh; Tang, Xiaojia; O'Brien, Daniel R et al. (2018) HGT-ID: an efficient and sensitive workflow to detect human-viral insertion sites using next-generation sequencing data. BMC Bioinformatics 19:271
Tabe-Bordbar, Shayan; Emad, Amin; Zhao, Sihai Dave et al. (2018) A closer look at cross-validation for assessing the accuracy of gene regulatory networks and models. Sci Rep 8:6620
Ho, Ming-Fen; Correia, Cristina; Ingle, James N et al. (2018) Ketamine and ketamine metabolites as novel estrogen receptor ligands: Induction of cytochrome P450 and AMPA glutamate receptor gene expression. Biochem Pharmacol 152:279-292
Adami, Guy R; Tangney, Christy C; Tang, Jessica L et al. (2018) Effects of green tea on miRNA and microbiome of oral epithelium. Sci Rep 8:5873
Xiao, Jinfeng; Blatti, Charles; Sinha, Saurabh (2018) SigMat: a classification scheme for gene signature matching. Bioinformatics 34:i547-i554
Saul, Michael C; Blatti, Charles; Yang, Wei et al. (2018) Cross-species systems analysis of evolutionary toolkits of neurogenomic response to social challenge. Genes Brain Behav :e12502
Hanson, Casey; Cairns, Junmei; Wang, Liewei et al. (2018) Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation. Genome Res 28:1207-1216

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