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Session A: (July 22 and July 23)	Session B: (July 24 and July 25)
Presentation Schedule for July 22, 6:00 pm – 8:00 pm A) 3DSIG B) BioVis C) Function D) HitSeq E) RegSys F) SysMod G) TransMed H) Special Session (SST01): Text Mining for Biology and Healthcare Presentation Schedule for July 23, 6:00 pm – 8:00 pm I) Bio-Ontologies J) CompMS K) EvolCompGen L) Microbiome M) NetBio	Presentation Schedule for July 24, 6:00 pm – 8:00 pm P) BOSC Q) CAMDA R) Education S) MLCSB T) RNA U) VarI V) General Computational Biology
Session A Poster Set-up and Dismantle Session A Posters set up: Monday, July 22 between 7:30 am - 10:00 am Session A Posters should be removed at 8:00 pm, Tuesday, July 23.	Session B Poster Set-up and Dismantle Session B Posters set up: Wednesday, July 24 between 7:30 am - 10:00 am Session B Posters should be removed at 2:00 pm, Thursday, July 25.

Short Abstract: There is over 500 biomedical ontologies and millions of triples storing biological interconnected data in knowledge bases as semantic known facts consisting of entities and their relations. Such knowledge bases are being mostly used for information retrieval, data integration and provision. Developing machine learning methods which can exploit such re-sources for predictive analysis and novel discovery becomes necessary and of significant importance. In this work, we utilize the plethora of biological linked data and bio-ontologies and we form a knowledge graphs centered around bio-logical entities and classes. Knowledge graphs embedding methods have recently emerged as an effective and promising paradigm for analyzing and learning from knowledge graphs within and across subjects’ domains. In this work, we present the potential of utilizing knowledge graphs embeddings methods as predictive tools in the bio-medicine domain. We compare between four state-of-the-art methods in the link prediction task concerning important biological relations. Each of these methods is a representative of various categories of knowledge graphs methods. We investigates various settings and evaluation metrics and their effects on the performance.

Short Abstract: Rhea (https://www.rhea-db.org) is an expert-curated database of biochemical reactions which aims to capture the complete metabolic diversity of any living organism as described in the scientific literature. Rhea was first made publicly available 10 years ago and is continuously growing since this first milestone. A key feature of the Rhea database is the fact that reactions participants are precisely chemically described using the ChEBI database/ontology (Chemical Entities of Biological Interest). Since December 2018, Rhea is used as an annotation vocabulary in UniProtKB, the universal protein knowledgebase (https://www.uniprot.org). This significantly improves the amount of details that can be annotated at the chemical level for enzymes catalytic activities (See poster "Enhanced enzyme annotation in UniProtKB using Rhea"). Recently, the Rhea project contributed a building block to the emerging field of semantic web in the life sciences community by offering an RDF representation of the data and a public SPARQL endpoint to query this data. Semantic integration of biochemical reactions data in RDF allows to ask complex cross-databases questions, using federated SPARQL queries. Broad queries spanning combinations of large RDF datasets can be challenging in term of execution time, but targeted biological questions can be answered surprisingly quickly. Examples queries will be shown.

Short Abstract: A huge amount of biomedical data are collected around the world related to many pathologies. In particular, in neurodegenerative diseases area, last years have witnessed the increasing of specialized databases such as Alzheimer’s Disease Neuroimaging Initiative (ADNI), which covers psychometric tests, biospecimen, imaging, and laboratory results. Analyzing these data is a challenging task and machine learning (ML) may offer methods and tools for knowledge discovery from them. However, ADNI suffers from a scarce conceptualization behind the collected data, which prevents a fully intuitive access to the data themselves and a direct analysis through ML methods. Therefore, in order to take advantage of this big data repository, we are working on two directions: (i) develop a detailed ontology to give a more conceptual organization to the data, to ease data access and interpretation, and to facilitate data integration approaches with other data sources; (ii) apply logic data mining methodologies to extract knowledge and generate probabilistic diagnostic models from the ontology, in order to classify patients into disease categories.

Short Abstract: The amount of available data, which can facilitate answering research questions, is growing. However, various data formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question. This paper presents a semi-automated framework that provides semantic enhancement of biomedical data, specifically datasets containing gene information. This framework proposes the combination of Machine Learning classification and annotation using BioPortal to automatically identify the semantic type of a concept. Compared to baseline methods, the proposed framework achieves the highest results.

Short Abstract: Biological databases contain ever-increasing amounts of data, often presented as complex and heterogeneous graphs, which are difficult to interpret even for experts. We propose an approach to simplify representation of such graphs using biomedical ontologies, in order to produce more general and understandable views of these complex graphs. Our approach uses an ontology-based similarity to merge nodes of a graph according to their corresponding ontology class(es). We exploit the mathematical framework of formal concept analysis, and its extension, pattern structures. We define an operator that computes the similarity of graph nodes annotated with several ontology classes. Here, the similarity of such nodes is a generalized description of their common semantics, according to the annotating ontology. This description is expressed using classes from the ontology, and annotates the group of merged nodes, represented in the graph as a metanode. We propose a implementation of this approach as a Cytoscape 3 App, configurable for use with any ontology or hierarchy of concepts. We describe a use case of this tool to simplify a graph of interactions between drugs and proteins. Here drugs are annotated with their biological and chemical roles and applications, expressed as CHEBI classes.

Short Abstract: The Database of Protein Disorder (DisProt, URL: www.disprot.org) is the major repository for manually curated intrinsic disorder (ID) annotations which are provided by almost 40 curators from different groups and countries. Currently, DisProt annotation uses a controlled vocabulary of functional terms associated with ID grouped in three main categories (molecular function, structural transition and interaction partner). Additionally, it allows to select the experimental techniques used for determining ID. In this work we report the results of our efforts and improvements made in the latest version of this resource, which will be released in June 2019. This version features almost 800 new proteins and more than 3500 ID region annotations. The DisProt system was also re-designed to support Minimum Information About Disorder Experiments (MIADE) as an early implementation of common standard for ELIXIR data interoperability in IDP field. On the new DisProt annotation interface curators are encouraged to report author statements from articles documenting ID proteins. This will provide a new corpus of statements for the development of a new generation of text mining tools that will improve article curation and processing, integrating DisProt annotations into SciLite and EuropePMC.

Short Abstract: Biomedical information is scattered across different biological data resources, which are biologically related but only loosely linked to each other in terms of data connections. This hinders the applications of integrative systems biology applications on data. We aim to develop a comprehensive resource, CROssBAR, to address these shortcomings by establishing relationships between relevant biological data sources to present a well-connected database, focusing on the fields of drug discovery and precision medicine. CROssBAR will contain 3 modules: (1) novel computational methods using graph theory and deep learning algorithms, to reveal unknown drug-target interactions and gene/protein-disease associations; (2) multi-partite biological networks where nodes will represent compounds/drugs, genes/proteins, pathways/systems and diseases, the edges will represent known and predicted pairwise relations in-between; and (3) an open access database and web-service to provide access to the resultant networks with its components. We have developed data pipelines for the heavy lifting of data from different data sources like UniProt, ChEMBL, PubChem, Drugbank and EFO persisting only specific data attributes for biomedical entity networks. The database is hosted in self-sufficient collections in MongoDB. The CROssBAR resource should help researchers in the interpretation of biomedical data by observing biological entities together with their relations.

Short Abstract: Sickle cell disease (SCD) is the most common monogenic disease in humans with multiple phenotypic expressions that can manifest as both acute and chronic complications. Although described more than a century ago, challenges in comprehensive disease management and collaborative research on this disease are compounded by the complex molecular and clinical phenotypes of SCD, environmental and psychosocial factors, limited number of therapeutic options and ambiguous terminology. This ambigous terminology has hampered the integration and interoperability of existing SCD knowledge, and the SCD research translation. Sickle Cell Disease Ontology (SCDO), which is a community driven integrative and universal knowledge representation system for SCD, overcomes this issue by providing a controlled vocabulary developed by a group of experts in both SCD and ontology design. SCDO is the first comprehensive standardized human- and machine-readable resource that unambiguously represents terminology and concepts about SCD for researchers, patients and clinicians. It is built around the central concept 'Hemoglobinopathy', allowing inclusion of non-SCD haemoglobinopathies, such as thalassaemias, which may interfere with SCD phenotypic manifestations. This collaboratively developed ontology constitutes a comprehensive knowledge management system, standardize terminology of various SCD related factors, that will promote interoperability of different research datasets, facilitate seamless data sharing and collaborations.

Short Abstract: Several approaches have been developed to apply meta-analytical tasks on the ontological graphs, aiming to detect differences and commonalities between groups of semantic terms. The majority of these approaches rely solely on the concept of most informative common ancestor (MICA), ignoring the high topological complexity of these graphs. The purpose of this study is to winnow the crucial factors that impinge on the semantic association of two terms and to evaluate existing similarity measures, in terms of their consistency with them. To address this, an instance of Gene Ontology was constructed and pairs of terms were ranked, according to the proposed rules. Different measures were applied, to measure their ability to reproduce the same ranking. The Aggregate Information Content measure had the best performance among various measures, suggesting that the inclusion of common informative ancestors in the estimation of IC-based similarity measures enhances the performance. Therefore, multiple parent inheritance is important in biomedical ontologies and should be taken into consideration. However, none of the existing measures presented ultimate accuracy, indicating the need of a more sophisticated approach.

Short Abstract: The recently developed BioAssay Express technology streamlines the conversion of human-readable assay descriptions to computer-readable information. BioAssay Express uses public semantic standards (ontologies) to markup bioprotocols, which unleashes the full power of informatics technology on data that could previously only be organized by crude text searching (https://peerj.com/articles/cs-61/). One of several annotation-support strategies within BioAssay Express is the use of machine learning models to provide statistically backed "suggestions" to the curator. We will describe our efforts to complement these models by applying ontology derived text mining, association rules mining based on existing annotations, and axioms that are embedded within the underlying ontologies. BioAssay Express includes the BioAssay Ontology (BAO), Gene Ontology (GO), Drug Target Ontology (DTO) and Cell Line Ontology (CLO). It can also be extended to handle private ontologies. We will explore how this resource will be used to encourage further semantic annotation of publicly available bioassay protocol data. These efforts are timely and important, as such datasets (released by both public and private organizations) are only increasing, with the volume already exceeding the ability of individual scientists to manage productively.

Short Abstract: Ontologies are becoming more relevant for data science as the need for standardized vocabulary and metadata increases. However, ontologies must evolve in order to stay relevant. A frequent complaint of ontology users is not knowing where to make requests for changes to ontologies. We are developing a set of infrastructure services with a public API that will allow users of ontologies to easily request new terms and update existing ones. Domain experts who are using Collaborative Drug Discovery's new tool BioAssay Express (BAE) to annotate their bioassays in a semi-automated and standardized fashion (using well-known ontologies like BioAssay Ontology (BAO), Gene Ontology (GO), Disease Ontology (DOID), and Drug Target Ontology (DTO)); and metadata curators who are using CEDAR Workbench, both have access to a number of ontologies. However, currently these users can not provide feedback to the ontology authors or request terms. Our initial goal in the OntoloBridge project is to help the users of BAE request changes to the existing BAE-exposed vocabularies in a semi-automated way. Collaborators at Stanford University are investigating the adoption of these APIs in tools like BioPortal and CEDAR. OntoloBridge ontology request services intend to increase the Findability, Accessibility, Interoperability, and Reproducibility (FAIR) of the above-mentioned ontologies, user requests to change them, and the BioAssay Protocols and metadata resources that rely on them.

Short Abstract: PDF version is uploaded as per requested at https://sites.google.com/site/bioontologies/