@conference {ICBO_2018_11, title = {ICBO_2018_11: Planteome \& BisQue: Automating Image Annotation with Ontologies using Deep-Learning Networks}, 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 = {

The field of computer vision has recently experienced tremendous progress due to advances in deep learning. This development holds particular promise in applications for plant research, due to a significant increase in the scale of image data harvesting and a strong field-driven interest in the automated processing of observable phenotypes and visible traits within agronomically important species. Parallel developments have occurred in semantic computing; for example, new ontologies have been initiated to capture plant traits and disease indicators. When combined with existing segmentation capabilities, it is possible to conceptualize software applications that give researchers the ability to analyze large quantities of plant phenotype image data, and to auto-annotate that data with meaningful, computable semantic terminology. We have previously reported on a software application that integrates segmentation and ontologies, but lacked the ability to manage very high-resolution images, and also lacked a database platform to allow for high-volume storage requirements. We have also previously reported our migration of the AISO user-guided segmentation feature to a BisQue (Bio-Image Semantic Query User Environment) module to take advantage of its increased power, ability to scale, secure data management environment, and collaborative software ecosystem. Neither AISO nor our initial BisQue implementation possessed a machine-learning component for interpreting (parts of) images. Plant researchers could benefit greatly from a trained classification model that predicts image annotations with a high degree of accuracy. We have therefore implemented two deep-learning prototypes: a coarse classification module for plant object identification (i.e. flower, fruit) and a fine-grained classification module that focuses on plant traits (e.g. reticulate vs. parallel venation, tip shape). Both classification models return results mapped to ontology terms as a form of annotation enrichment. This current version of the Planteome Deep Segmenter module combines image classification with optional guided segmentation and ontology annotation. We have most recently run the module on local Planteome BisQue client services, and are currently working with CyVerse to install a hosted version on their BisQue client service.

}, keywords = {annotation, convolutional neural networks, deep learning, image analysis, machine learning, Ontology, segmentation}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_11.pdf}, author = {Dimitrios Trigkakis and Justin Preece and Austin Meier and Justin Elser and Kris Kvilekval and Dmitry Fedorov and B.S. Manjunath and Pankaj Jaiswal and Sinisa Todorovic} } @conference {ICBO_2018_25, title = {ICBO_2018_25: OOPS: The Ontology of Plant Stress, A semi-automated standardization methodology}, 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 = {

Plant stress traits are an important breeding target for all crop species. Massive amounts of research dollars are spent combating plant diseases and nutrient deficiencies. Often this data is used to achieve a single goal, and then left in a repository to never be used again. As a scientific community, we should be striving to make all publicly funded data reusable, and interoperable. This goal is achievable only through careful annotation using universal data and metadata standards. One such standard is through the use of a standardized vocabulary, or ontology. This paper focuses on producing a semi-automated method to define, and label plant stresses using a combination of web scraping, and ontology design patterns. Standardizing the definitions, and linking plant stress with established hierarchies leverages previous work of developed knowledge bases such as taxonomic classifications and other ontologies.

}, keywords = {automation, data standards, nutrient deficiency, Ontology, plant pathology, Planteome, web scraping}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_25.pdf }, author = {Austin Meier and Marie-Ang{\'e}lique Laporte and Justin Elser and Laurel Cooper and Justin Preece and Pankaj Jaiswal and Jorrit Poelen} } @conference {ICBO_2018_66, title = {ICBO_2018_66: Visualization of N-ary Relationships}, 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 = {Biologists are interested in conducting gene expression analysis to detect co-expression clusters that are regulated by the same set of genes. Due to the involvement of a high number of genes, it becomes difficult to visualize the gene networks to make meaningful insights. By utilizing N-ary data visualization technique, we find the cliques (a complete graph of N genes) among the gene sets and visualize them as a two-dimensional complex (Nodes, edges and polygonal faces) and devise an optimal layout. Consequently, the biologists can easily recognize the cliques in each cluster, the cardinality of the cliques, predominant interactions and the degree of heterogeneity of the data from the visualization.}, keywords = {cliques, data visualization, graph layout, N-ary relationship}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Botong Qu and Prashant Kumar and Eugene Zhang and Pankaj Jaiswal and Laurel Cooper and Justin Elser and Yue Zhang} }