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}

@ARTICLE{ivo-pipeline2,
  AUTHOR={Dinov, Ivo D. and Petrosyan, Petros and Liu, Zhizhong and Eggert, Paul and Hobel, Sam and Vespa, Paul and Woo Moon, Seok and Van Horn, John D. and Franco, Joseph and Toga, Arthur W.},    
  TITLE={High-throughput neuroimaging-genetics computational infrastructure},      	
  JOURNAL={Frontiers in Neuroinformatics},      	
  VOLUME={8},      
  PAGES={41},     	
  YEAR={2014},	  
  URL={https://www.frontiersin.org/article/10.3389/fninf.2014.00041},       
  DOI={10.3389/fninf.2014.00041},      	
  ISSN={1662-5196},    
ABSTRACT={Many contemporary neuroscientific investigations face significant challenges in terms of data management, computational processing, data mining and results interpretation. These four pillars define the core infrastructure necessary to plan, organize, orchestrate, validate and disseminate novel scientific methods, computational resources and translational healthcare findings. Data management includes protocols for data acquisition, archival, query, transfer, retrieval and aggregation. Computational processing involves the necessary software, hardware and networking infrastructure required to handle large amounts of heterogeneous neuroimaging, genetics, clinical and phenotypic data and meta-data. In this manuscript we describe the novel high-throughput neuroimaging-genetics computational infrastructure available at the Institute for Neuroimaging and Informatics (INI) and the Laboratory of Neuro Imaging (LONI) at University of Southern California (USC). INI and LONI include ultra-high-field and standard-field MRI brain scanners along with an imaging-genetics database for storing the complete provenance of the raw and derived data and meta-data. A unique feature of this architecture is the Pipeline environment, which integrates the data management, processing, transfer and visualization. Through its client-server architecture, the Pipeline environment provides a graphical user interface for designing, executing, monitoring validating, and disseminating of complex protocols that utilize diverse suites of software tools and web-services. These pipeline workflows are represented as portable XML objects which transfer the execution instructions and user specifications from the client user machine to remote pipeline servers for distributed computing. Using Alzheimer’s and Parkinson’s data, we provide several examples of translational applications using this infrastructure.}
}

@article{ivo-pipeline,
    author = {Dinov, Ivo D. AND Heavner, Ben AND Tang, Ming AND Glusman, Gustavo AND Chard, Kyle AND Darcy, Mike AND Madduri, Ravi AND Pa, Judy AND Spino, Cathie AND Kesselman, Carl AND Foster, Ian AND Deutsch, Eric W. AND Price, Nathan D. AND Van Horn, John D. AND Ames, Joseph AND Clark, Kristi AND Hood, Leroy AND Hampstead, Benjamin M. AND Dauer, William AND Toga, Arthur W.},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations},
    year = {2016},
    month = {08},
    volume = {11},
    url = {https://doi.org/10.1371/journal.pone.0157077},
    pages = {1-28},
    abstract = {Background A unique archive of Big Data on Parkinson’s Disease is collected, managed and disseminated by the Parkinson’s Progression Markers Initiative (PPMI). The integration of such complex and heterogeneous Big Data from multiple sources offers unparalleled opportunities to study the early stages of prevalent neurodegenerative processes, track their progression and quickly identify the efficacies of alternative treatments. Many previous human and animal studies have examined the relationship of Parkinson’s disease (PD) risk to trauma, genetics, environment, co-morbidities, or life style. The defining characteristics of Big Data–large size, incongruency, incompleteness, complexity, multiplicity of scales, and heterogeneity of information-generating sources–all pose challenges to the classical techniques for data management, processing, visualization and interpretation. We propose, implement, test and validate complementary model-based and model-free approaches for PD classification and prediction. To explore PD risk using Big Data methodology, we jointly processed complex PPMI imaging, genetics, clinical and demographic data.   Methods and Findings Collective representation of the multi-source data facilitates the aggregation and harmonization of complex data elements. This enables joint modeling of the complete data, leading to the development of Big Data analytics, predictive synthesis, and statistical validation. Using heterogeneous PPMI data, we developed a comprehensive protocol for end-to-end data characterization, manipulation, processing, cleaning, analysis and validation. Specifically, we (i) introduce methods for rebalancing imbalanced cohorts, (ii) utilize a wide spectrum of classification methods to generate consistent and powerful phenotypic predictions, and (iii) generate reproducible machine-learning based classification that enables the reporting of model parameters and diagnostic forecasting based on new data. We evaluated several complementary model-based predictive approaches, which failed to generate accurate and reliable diagnostic predictions. However, the results of several machine-learning based classification methods indicated significant power to predict Parkinson’s disease in the PPMI subjects (consistent accuracy, sensitivity, and specificity exceeding 96%, confirmed using statistical n-fold cross-validation). Clinical (e.g., Unified Parkinson's Disease Rating Scale (UPDRS) scores), demographic (e.g., age), genetics (e.g., rs34637584, chr12), and derived neuroimaging biomarker (e.g., cerebellum shape index) data all contributed to the predictive analytics and diagnostic forecasting.   Conclusions Model-free Big Data machine learning-based classification methods (e.g., adaptive boosting, support vector machines) can outperform model-based techniques in terms of predictive precision and reliability (e.g., forecasting patient diagnosis). We observed that statistical rebalancing of cohort sizes yields better discrimination of group differences, specifically for predictive analytics based on heterogeneous and incomplete PPMI data. UPDRS scores play a critical role in predicting diagnosis, which is expected based on the clinical definition of Parkinson’s disease. Even without longitudinal UPDRS data, however, the accuracy of model-free machine learning based classification is over 80%. The methods, software and protocols developed here are openly shared and can be employed to study other neurodegenerative disorders (e.g., Alzheimer’s, Huntington’s, amyotrophic lateral sclerosis), as well as for other predictive Big Data analytics applications.},
    number = {8},
    doi = {10.1371/journal.pone.0157077}
}

@misc{culich17binder,
author = "Aaron Culich and Brian Granger and Tim Head and Chris Holdgraf and Yuvi Panda and Fernando Perez and Min Ragan-Kelley and Carol Willing",
title = "Binder: Enabling sharing and publication of reproducible computational research",
year = "2017",
month = "12",
url = "https://doi.org/10.6084/m9.figshare.5671840.v1",
doi = "10.6084/m9.figshare.5671840.v1"
}

@article{brinckman17wholetale,
author = "Adam Brinckman and Kyle Chard and Niall Gaffney and Mihael Hategan and Matthew B. Jones and Kacper Kowalik and Sivakumar Kulasekaran and Bertram Ludascher and Bryce D. Mecum and Jaroslaw Nabrzyski and Victoria Stodden and Ian J. Taylor and Matthew J. Turk and Kandace Turner",
title = {Computing Environments for Reproducibility: Capturing the ``Whole Tale''},
year = "2017",
journal={Future Generation Computer Systems},
}

@misc{cwl,
 doi = {10.6084/m9.figshare.3115156.v2},
 url = {http://dx.doi.org/10.6084/m9.figshare.3115156.v2},
 author = {Peter Amstutz and Michael R. Crusoe and Neboj\usa Tijani\'c and Brad Chapman and
John Chilton and Michael Heuer and Andrey Kartashov and John Kern and Dan Leehr and
Herv\'e M\'enager and Maya Nedeljkovich and Matt Scales and Stian Soiland-Reyes and
Luka Stojanovic
},
 publisher = {Figshare},
 institution = {Common Workflow Language working group},
 title = {Common Workflow Language, v1.0},
 year = {2016}
}