@conference {360, title = {W01-05: Uncertainty analysis and visualization in Large-Scale Vector Fields}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, Oregon, USA}, abstract = {

Vector fields are omnipresent in science, engineering, and medicine. Due to the big-data nature of simulated vector field data, uncertainty in the data can lead to difficulties in their physical interpretations. In this talk, we will describe how Morse-decomposition can serve as a means to quantify and control the uncertainty in the data.

}, keywords = {Big data, data uncertainty, data visualization}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Eugene Zhang} } @conference {350, title = {W01-04: Telling a genome{\textquoteright}s story graphically}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, Oregon, USA}, abstract = {

Scientific research is inherently a collaborative task; a dialog among different researchers to reach a consensus understanding of the underlying biology. Information graphics facilitate this dialog because humans are visually wired and can absorb well-executed graphics more quickly than they can absorb the written word or columns of numbers. Visual representations communicate complex ideas quickly and clearly.

This talk will briefly introduce basic principles of designing interactive graphics, with examples from the genomics community illustrating various points. It will also discuss the lessons learned with our own annotation tools: PAINT{\textemdash}a phylogenetically based functional annotation tool (https://github.com/geneontology/paint) , Apollo{\textemdash}a genomic feature editor designed to support structural annotation of gene models (http://genomearchitect.org/),\  and Noctua{\textemdash}a biological-process model builder designed for describing the functional roles of gene products (http://noctua.berkeleybop.org/).

In addition to the graphical elements we summarize the requirements that enable any annotation tool to meet the needs of the research community, including: Real time updates to allow geographically dispersed researchers to conduct joint efforts; Parallel chat mechanisms; Maintaining an evidence trail for all assertions; Well supported history mechanisms and browsing of past versions; Providing different levels of permissions; Facilitating publication; Responsiveness to users{\textquoteright} requests; Documentation and dedicated resources for training and support.

}, keywords = {BIG-data, data visualization, genome, ontology}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Suzanna Lewis} } @conference {358, title = {W01-03: Computer Vision for Next Generation Phenomics and Tree of Life}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, Oregon, USA}, abstract = {

To build the Tree of Life, scientists collect data on all heritable features {\textendash} both genotypes (e.g., DNA sequences) and phenotypes (e.g., anatomy, behavior, physiology) for all living and extinct species.\  The collection of phenomic data for tree-building has lagged far behind the collection of genomic data. Advances in computer vision have the potential to change this situation. In this talk, I will present a computer vision system developed in our lab for extending phenomic matrices, and in this way\ building the Tree of Life.\ Rows of a phenomic matrix represent images of specimens belonging to various species of interest, and columns represent scores of their phenomic characters. Given a\ phenomic matrix where only few rows are manually annotated with character scores, our vision system extends the matrix row-wise by populating missing character scores of the remaining species in the matrix. The talk will present our experimental results on scoring phenomic characters in images of bat skulls, nematocysts, and leaves, available in the Morphobank and Bisque data repositories.

}, keywords = {Bisque data repository, data visualization, Morphobank, Phenotypes, Tree of Life}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Sinisa Todorovic} } @conference {359, title = {W01-02: Topology-Driven Data Visualization of Large-Scale Tensor Fields}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, Oregon, USA}, abstract = {

Spatial-temporally varying simulation data sets are growing at a scale that traditional\ visualization techniques are inadequate to handle. In this talk, we review topology-driven techniques which focus on extracting the key (topological) features in 3D symmetric tensor fields, which can provide a more compact visualization of the data.

}, keywords = {data visualization, tensor fields, topology}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Yue Zhang} } @conference {361, title = {W01-01: Big Data Visual Analysis}, booktitle = {International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016)}, year = {2016}, month = {11/30/16}, publisher = {CEUR-ws.org Volume 1747}, organization = {CEUR-ws.org Volume 1747}, address = {Corvallis, Oregon, USA}, abstract = {

We live in an era in which the creation of new data is growing exponentially such\ that every two days we create as much new data as we\ did from the beginning of\ mankind until the year 2003. One of the greatest scientific challenges of the 21st\ century is to effectively\ understand and make use of the vast amount of information\ being produced. Visual data analysis will be among our most important tools\ to\ understand such large and often complex data. In this talk, I will present state-of-the-art visualization techniques, applied to important Big Data problems in science, engineering, and\ medicine.

}, keywords = {BIG-data, data visualization}, url = {http://icbo.cgrb.oregonstate.edu/}, author = {Christopher Johnson} }