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(Almost) Big Data in Biology: the Arising of a New Ecosystem
Transcript of (Almost) Big Data in Biology: the Arising of a New Ecosystem
Biological data analysis
Genomics (RNA Seq, Whole Exome, Whole Genome, Microarrays)
"Small" data (biostatistics)
Heterogeneous data integration
Biostatistics and Bioinformatics Platform
Sparsity and Structure
Non smooth convex penalty
Proximal operator too complex or not known
--> Known gradient
--> Lipschitz continuous gradient
Professor Terence Speed
University of California at Berkeley,
Department of Statistics
a a a a
The term 'Big Data' is meant to capture the opportunities and challenges facing all biomedical researchers in accessing, managing, analyzing, and integrating datasets of diverse data types [e.g., imaging, phenotypic, molecular (including various '– omics'), exposure, health, behavioral, and the many other types of biological and biomedical and behavioral data] that are increasingly larger, more diverse, and more complex, and that exceed the abilities of currently used approaches to manage and analyze effectively.
What is Big Data
for the US NIH?
Difficult to integrate results
to the data and software tools.
data and metadata.
and practices for data and software sharing.
Organizing, managing, and processing
biomedical Big Data.
Developing new methods
analyzing & integrating
who can use biomedical Big Data effectively.
Major challenges in using
biomedical Big Data include:
Groups or networks
Add a constraint to...
make a more general model, (RGCCA)
introduce sparsity (SGCCA)
or structure, (PLS'14)
generalize the "link" (PLS'14).
with non-smooth penalties