@conference {ICBO_2018_48,
	title = {ICBO_2018_48: Evidence-based medicine,Knowledge Modeling Framework},
	booktitle = {International Conference on Biomedical Ontology (ICBO 2018)},
	series = {Proceedings of the International Conference on Biological Ontology (2018)},
	year = {2018},
	month = {08/06/2018},
	publisher = {International Conference on Biological Ontology},
	organization = {International Conference on Biological Ontology},
	abstract = {Members within the Healthcare Collaborative Network are confronting major changes at work as well as the growing body of information, much of it invalid or irrelevant to clinical practice. Evidence-based medicine is used to analyze clinical data objectively in terms of quality from different studies. The knowledge modeling is important in knowledge management for understanding the source of knowledge, the inputs and outputs, the flow of knowledge and the dependents are necessary to model the knowledge.This article discusses a proposed evidence based medicine, knowledge modeling framework. The challenge is in representation, integration, analysis, interpretation of the available knowledge and data, management of heterogeneous data, the integration and transfer of enriched data, the effective use of knowledge-based decision systems, sharing of knowledge automatically from huge volumes of data. The above challenge arise the need for Evidence-based medicine Knowledge Modeling Framework, which should provide an answer for the question "How collection, sharing and re-uses of biomedical Big Data should be regulated?{\textquotedblright}.},
	keywords = {Bioinformatics, Evidence-based medicine, Knowledge Modeling Framework},
	url = {http://icbo2018.cgrb.oregonstate.edu/},
	author = {Morcous Massoud}
}
@conference {ICBO_2018_72,
	title = {ICBO_2018_72: Ontology-based Comparative Transcriptomics: Novel Drought Stress-Induced Genes and Pathways in Rice},
	booktitle = {International Conference on Biomedical Ontology (ICBO 2018)},
	series = {Proceedings of the International Conference on Biological Ontology (2018)},
	year = {2018},
	month = {08/06/2018},
	publisher = {International Conference on Biological Ontology},
	organization = {International Conference on Biological Ontology},
	abstract = {Rice is an important crop that feeds almost half the world population. As climate change models predict floods, drought and extreme temperatures in rice production areas, the need to better understand the genetic basis of adaptation and tolerance mechanisms to abiotic stresses is vital. To better understand tolerance mechanisms and responses under drought, we designed a time-series transcriptomic experiment with two different genotypes of Oryza sativa subspecies indica. These indica genotypes are grown in their center of diversity and are phenotyped as tolerant or susceptible to common abiotic stressors: submergence, saline, and/or drought. Using systems approach, our goal was to identify the stress tolerant candidate genes and genetic polymorphism to help accelerate the genetic gains in plant breeding efforts. We generated RNA-Seq transcriptome data for treated and untreated samples of the two indica genotypes, with three biological replicates, per time point in a drought-stress induced experiment. The sequence data generated was analyzed by calling polymorphisms, transcript isoforms, expression levels, assembling transcriptomes and identifying stress-induced pathways specific to each genetic background and tolerance level. The statistically significant results of these analyses were then annotated using various ontologies and by aligning against the quantitative trait loci and phenotypes annotated with trait ontology, SNP consequences annotated with sequence ontology, and gene ontologies, to identify function, process role and cellular localization of genes of interest. Collating these ontologies proved useful in identifying stress-induced genes overlapping the QTLs overlapping with drought phenotype and characterizing thousands of interesting genetic changes that may help us understand mechanism of drought response in rice.},
	keywords = {Abiotic Stress, Bioinformatics, Biological Ontologies, Comparative Transcriptomics, Oryza sativa, Stress-Biology},
	url = {http://icbo2018.cgrb.oregonstate.edu/},
	author = {Noor Al-Bader and Pankaj Jaiswal}
}
@conference {ICBO_2018_73,
	title = {ICBO_2018_73: Taxa, metacoder, poppr and vcfR: Four packages for parsing, visualization, and manipulation of genetic, genomic and metagenomic data in R},
	booktitle = {International Conference on Biomedical Ontology (ICBO 2018)},
	series = {Proceedings of the International Conference on Biological Ontology (2018)},
	year = {2018},
	month = {08/06/2018},
	publisher = {International Conference on Biological Ontology},
	organization = {International Conference on Biological Ontology},
	abstract = {Contemporary population genomic microbiome research are producing complex and large datasets that are difficult to manipulate and visualize. High throughput DNA sequencing projects typically result in files containing genetic variants in the Variant Call Format (VCF). These files are large and not strictly tabular, presenting an issue to investigators who wish to work with this data in the R environment. We created the package vcfR to facilitate exploration of VCF data in R. vcfR uses Rcpp to implement C++ functions, allowing R users to take advantage of the performance of compiled code without the user needing to know about the compiled code. This facilitates efficient use of VCF data in R. New users who are unfamiliar with VCF data can now explore the data to learn about their data. Quality control steps can be conducted. In addition, manipulations, such as subsetting samples or variants, can be performed. We provide conversion functions so analyses in the popular R genetics packages adegenet, poppr, ape and pegas. Analysis of population differentiation and copy number variation can be performed directly in vcfR. Our package vcfR facilitates efficient use, manipulation, and analysis of VCF data and integrates this data into the existing ecosystem of R genetic analysis and graphics packages. The taxa package provides a set of classes for the storage and manipulation of taxonomic data. Classes range from simple building blocks to project-level objects storing multiple user-defined datasets mapped to a taxonomy. It includes parsers that can read in taxonomic information in nearly any form. We have also developed the metacoder package for visualizing hierarchical data. Metacoder implements a novel visualization called heat trees that use the color and size of nodes and edges on a taxonomic tree to quantitatively depict up to 4 statistics. This allows for rapid exploration of data and information-dense, publication-quality graphics. This is an alternative to the stacked barcharts typically used in microbiome research. These complementary tools provide a new resource for analyzing hierarchical population genomic and microbiome data in R.},
	keywords = {Bioinformatics, Metagenome, Microbiome, Population genetics, Population genomics, Taxonomy, Variant Call},
	url = {http://icbo2018.cgrb.oregonstate.edu/},
	author = {Niklaus Grunwald}
}
@conference {ICBO_2018_74,
	title = {ICBO_2018_74: The many faces of ontological data},
	booktitle = {International Conference on Biomedical Ontology (ICBO 2018)},
	series = {Proceedings of the International Conference on Biological Ontology (2018)},
	year = {2018},
	month = {08/06/2018},
	publisher = {International Conference on Biological Ontology},
	organization = {International Conference on Biological Ontology},
	abstract = {To be added},
	keywords = {Bioinformatics, data visualization, Ontology},
	url = {http://icbo2018.cgrb.oregonstate.edu/},
	author = {Melissa Haendel}
}
