@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_43, title = {ICBO_2018_43: Domain Informational Vocabulary Extraction Experiences with Publication Pipeline Integration and Ontology Curation}, 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 = {

We will present updates on an ongoing project DIVE (Domain Informational Vocabulary Extraction), a system designed for extracting domain information from scientific publications. DIVE implements an ensemble of text mining methods for biological entity extraction from article text. DIVE also attempts use the co-occurrence patterns of these entities to establish probable relationships between them. DIVE also features an improved web interface for expert user curation of extracted information, thereby providing a means for a constantly growing and expert curated body of domain information for an article corpus. We also discuss our experiences from successful integration of DIVE with the publishing pipeline for two prominent Plant Biology Journals (The Plant Cell and Plant Physiology) from ASPB (American Society of Plant Biologists). The extracted results are embedded at the end of the final proof of the published article to enhance its accessibility and discoverability. Furthermore, DIVE tracks expert user curation actions on its web interface for future training and improvement of the entity detection algorithm.

}, keywords = {big data, Cyberinfrastructure, DIVE, machine learning, Ontology}, url = {http://ceur-ws.org/Vol-2285/ICBO_2018_paper_43.pdf }, author = {Amit Gupta and Weijia Xu and Pankaj Jaiswal and Crispin Taylor and Jennifer Regala} } @conference {ICBO_2018_68, title = {ICBO_2018_68: The Planteome Project- Reference Ontologies for Data Integration}, 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 Planteome project is a centralized online plant informatics portal which provides semantic integration of widely diverse datasets with the goal of plant improvement. Traditional plant breeding methods for crop improvement may be combined with next-generation analysis methods and automated scoring of traits and phenotypes to develop improved varieties. The Planteome project (www.planteome.org) develops and hosts a suite of reference ontologies for plants associated with a growing corpus of genomics data. Data annotations linking phenotypes and germplasm to genomics resources are achieved by data transformation and mapping species-specific controlled vocabularies to the reference ontologies. Analysis and annotation tools are being developed to facilitate studies of plant traits, phenotypes, diseases, gene function and expression and genetic diversity data across a wide range of plant species. The project database and the online resources provide researchers tools to search and browse and access remotely via APIs for semantic integration in annotation tools and data repositories providing resources for plant biology, breeding, genomics and genetics.}, keywords = {anatomy, data annotation, development, Ontology, plants, traits}, url = {http://icbo2018.cgrb.oregonstate.edu/}, author = {Laurel Cooper and Pankaj Jaiswal} }