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Posters - Schedules

Poster presentations at ISMB/ECCB 2021 will be presented virtually. Authors will pre-record their poster talk (5-7 minutes) and will upload it to the virtual conference platform site along with a PDF of their poster beginning July 19 and no later than July 23. All registered conference participants will have access to the poster and presentation through the conference and content until October 31, 2021. There are Q&A opportunities through a chat function and poster presenters can schedule small group discussions with up to 15 delegates during the conference.

Information on preparing your poster and poster talk are available at: https://www.iscb.org/ismbeccb2021-general/presenterinfo#posters

Ideally authors should be available for interactive chat during the times noted below:

View Posters By Category

Session A: Sunday, July 25 between 15:20 - 16:20 UTC
Session B: Monday, July 26 between 15:20 - 16:20 UTC
Session C: Tuesday, July 27 between 15:20 - 16:20 UTC
Session D: Wednesday, July 28 between 15:20 - 16:20 UTC
Session E: Thursday, July 29 between 15:20 - 16:20 UTC
Chemical Entity Normalization for Successful Translational Development of Alzheimer’s Disease and Dementia Therapeutics
COSI: Bio-Ontologies
  • Sarah Mullin, Yale University, United States
  • Robert McDougal, Yale University, United States
  • Kei-Hoi Cheung, Yale University, United States
  • Halil Kilicoglu, University of Illinois Urbana-Champaign, United States
  • Amanda Beck, Albert Einstein College of Medicine, United States
  • Caroline Zeiss, Yale University, United States

Short Abstract: Despite advances in identifying the biological basis of Alzheimer’s disease (AD) and dementia, there remain few chemical therapeutic interventions. One major challenge is the poor translation of effective therapies from animals to humans. Text mining translation-related characteristics, such as chemical interventions, can help to address this challenge. However, normalization to a standardized ontology that contains hierarchical relations and molecule structure information, is challenging. We provide a reproducible hierarchical primarily dictionary-based method to normalize chemical mentions from PubTator to Chemical Entities of Biological Interest (ChEBI), a fully curated database and OBO Foundry ontology for molecular entities. To generate this mapping we make use of external synonym databases, ChEBI parent-child relationships, and nearby context words. We found 277,844 PubMed abstracts related to Alzheimer’s and dementia in PubTator. Of the total 55,574 chemical mentions found in the article title, we normalized 49,966 mentions to 3,507 unique ChEBI entities. In addition, we were able to identify potential new candidate entities related to AD and dementia from the remaining 9.4%. Patterns that emerge from aggregation of standardized chemical interventions can help ascertain translational potential. In addition, effective and correct normalization in text mining is important for future downstream applications, such as improved efficacy and drug design.

Designing potential extensions from G-SRS to ChEBI to identify natural product-drug interactions
COSI: Bio-Ontologies
  • Sanya Taneja, University of Pittsburgh, United States
  • Tiffany Callahan, University of Colorado Anschutz Medical Campus, United States
  • Mathias Brochhausen, University of Arkansas for Medical Sciences, United States
  • Mary Paine, Washington State University, United States
  • Sandra Kane-Gill, University of Pittsburgh, United States
  • Richard Boyce, University of Pittsburgh, United States

Short Abstract: Botanical and other natural products (NPs) are not as widely represented in biomedical ontologies compared to conventional drugs. The growing use of NPs that have been implicated in clinically significant pharmacokinetic NP–drug interactions (NPDIs) renders addressing this knowledge gap imperative. In this study, we designed potential logical extensions to the Chemical Entities of Biological Interest (ChEBI) ontology that map information about NPs and NP constituents from the Global Substance Registration System (G-SRS). We extracted information from the G-SRS database using SQL; created semantically consistent logical representations for the case NPs - kratom, goldenseal, and green tea; and integrated them within the ChEBI ontology. The merged ontology contains NP information in computable form and is compatible with the principles of the Open Biomedical Ontologies Foundry. The potential logical extensions are the first step in advancing re-search related to NPDIs using biomedical ontologies and knowledge graphs.

Elucidating human ageing-related phenotypic abnormalities with hierarchical feature selection method
COSI: Bio-Ontologies
  • Cen Wan, Birkbeck, University of London, United Kingdom

Short Abstract: The recent success of hierarchical feature selection methods enables us to discover knowledge from ontology data. In this work, we focus on discovering relationships between ageing and human phenotypic abnormalities by adopting a well-known hierarchical feature selection method. The selected human phenotype ontology terms further reveal strong links between ageing and developmental phenotypic abnormalities.

Goal-models to support communication, planning and guiding of FAIRification
COSI: Bio-Ontologies
  • César Bernabé, Leiden University Medical Center, Netherlands
  • Annika Jacobsen, Leiden University Medical Center, Netherlands
  • Núria Queralt Rosinach, Leiden University Medical Center, Netherlands
  • Vitor E. Silva Souza, Federal University of Espírito Santo, Brazil, Brazil
  • Luiz Santos, University of Twente, Netherlands
  • Marco Roos, Leiden University Medical Centre, Netherlands
  • Barend Mons, Leiden University Medical Centre, Netherlands

Short Abstract: Implementing the FAIR principles makes data ready for efficient analysis with other data. Workflows for the process of making data FAIR (‘FAIRification’) describe how the principles can be realised. As a multidisciplinary activity, FAIRification relies on good communication with different expertise involved. However, FAIRification workflows currently do not specify methods to meet this need.
We are designing a method that uses ‘goal-oriented models’ to support the FAIRification ‘objective identification’ and ‘conceptual modelling’ steps. In the former, the motivation(s) for the need for FAIR data are identified. In the latter, goal models are used to define the scope, identify important concepts and validate the resulting conceptual model. The method will also describe best practices and activities for conceptual modelling.
It is expected that the approach will contribute by improving the efficiency of FAIRification procedures, based on clear and easier communication of constraints and intentions among everyone involved in the project; and enhance the interoperability of FAIRified data, based on the expected improvement of the data models that are built following the method. We are currently finalizing the design of the method and running a set of proofs-of-concept to validate and adjust it.

How much can model organism phenotypes teach us about human disease? A study using ontologies and semantic machine learning
COSI: Bio-Ontologies
  • Sarah Alghamdi, King Abdullah University of Science and Technology, Saudi Arabia
  • Paul Schofield, University of Cambridge, United Kingdom
  • Robert Hoehndorf, King Abdullah University of Science and Technology, Saudi Arabia

Short Abstract: The use of model organisms such as the mouse, fruitfly and
zebrafish has been key in driving our understanding of human disease
and its underlying biology for arguably a century, mainly due to the
availability of genetic approaches. Many thousands of phenotypic
annotations are now available for the major experimental model
organism. Different organisms offer differing strengths and
weaknesses. When combining the phenotypic annotations across multiple
model organisms, the strengths and weaknesses of each model may be
compensated and coverage of the human genome can be optimised. Work
over the past decade has demonstrated the power of cross-species
phenotypic comparisons, and cross-species phenotype ontologies such as
uPheno and the PhenomeNET ontology have been developed for this
purpose. We report further development of the pan-species phenotype
ontology PhenomeNet-Extended (Pheno-e), in particular including
phenotypes from Schizosaccharomyces and Drosophila.
We apply ontology embeddings and unsupervised machine learning to
measure the semantic similarity between phenotypes resulting from
loss-of-function mutations in model organisms and their associated
phenotypes. We demonstrate the different contributions of each
species' phenotypic data to the identification of human gene-disease
associations, and investigate the physiological and anatomical
properties through which each species contributes.

Predicting Gene-Disease Associations with Knowledge Graph Embeddings over Multiple Ontologies
COSI: Bio-Ontologies
  • Susana Nunes, LASIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal
  • Rita T. Sousa, LASIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal
  • Catia Pesquita, LASIGE, Faculdade de Ciências, Universidade de Lisboa, Portugal

Short Abstract: Ontology-based approaches for predicting gene-disease associations include the more classical semantic similarity methods and more recently knowledge graph embeddings. While semantic similarity is typically restricted to hierarchical relations within the ontology, knowledge graph embeddings consider their full breadth. However, embeddings are produced over a single graph and complex tasks such as gene-disease association may require additional ontologies.
We investigate the impact of employing richer semantic representa-tions that are based on more than one ontology, able to represent both genes and diseases and consider multiple kinds of relations within the ontologies. Our experiments demonstrate the value of employing knowledge graph embeddings based on random-walks and highlight the need for a closer integration of different ontologies.

Publishing Medical Context of Neurological Drug Indications as a Knowledge Graph
COSI: Bio-Ontologies
  • Jinzhou Yang, Maastricht University, Institute of Data Science, Netherlands
  • Remzi Celebi, Maastricht University, Institute of Data Science, Netherlands
  • Leoni Bücken, Maastricht University, Institute of Data Science, Netherlands
  • Sarah Chenine, Maastricht University, Institute of Data Science, Netherlands
  • Vincent Emonet, Maastricht University, Institute of Data Science, Netherlands
  • Michel Dumontier, Maastricht University, Institute of Data Science, Netherlands

Short Abstract: Motivation: Understanding the medical context of therapeutic intervention is crucial to its successful use in people. However, this contextual information is not recorded in a machine-readable manner, thereby limiting its use in query answering, clinical decision support, and computational drug discovery. Here, we describe a semi-automated approach to capture drug indications and their medical context. Our approach involves i) a pre-screening of relevant terms using natural language processing tools, and ii) the development and use of Nanobench semantic templates to facilitate data curation with support for term auto-completion from vocabulary standards. We apply our method to create the NeuroDKG, a knowledge graph for Neuropharmaceutical Drugs, which is available as a set of nanopublications.
Availability: The NeuroDKG is available at github.com/MaastrichtU-IDS/neuro_dkg

The COVID-19 epidemiology and monitoring ontology
COSI: Bio-Ontologies
  • Núria Queralt Rosinach, Leiden University Medical Center, Netherlands
  • Paul Schofield, University of Cambridge, United Kingdom
  • Robert Hoehndorf, King Abdullah University of Science and Technology, Saudi Arabia
  • Claus Weiland, Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Frankfurt am Main, Germany, Germany
  • Erik Schultes, GO FAIR International Support and Coordination Offcie: Leiden, NL, Netherlands
  • César Henrique Bernabé, Leiden University Medical Center, Netherlands
  • Marco Roos, Leiden University Medical Centre, Netherlands

Short Abstract: One year ago, the novel COVID-19 infectious disease emerged and spread, causing high mortality and morbidity rates worldwide. In the OBO Foundry, there are more than one hundred ontologies to share and analyse large-scale datasets for biological and biomedical sciences. However, this pandemic revealed that we lack tools for an efficient and timely exchange of this epidemiological data which is necessary to assess the impact of disease outbreaks, the efficacy of mitigating interventions and to provide a rapid response. Recently, several new COVID-19 ontologies have developed such as the IDO extension or CIDO. Hence, our research question was to determine if there was a good representation of epidemiological quantitative concepts in OBO ontologies. Our objectives were to identify missing COVID-19 epidemiological terms and implement axiom patterns for extensions to existing ontologies or to build a new, logically well-formed, and accurate ontology in OBO. In this study we present our findings and contributions for the bio-ontologies community.

Wikidata for 5-star Linked Open Databases: a case study of PanglaoDB
COSI: Bio-Ontologies
  • Tiago Lubiana, University of São Paulo, Brazil
  • João Vitor Ferreira Cavalcante, Federal University of Rio Grande do Norte, Brazil

Short Abstract: PanglaoDB is a database of cell type markers widely used for single-cell RNA sequencing data analysis. PanglaoDB is in a 3-star category for Linked Open Data. Conforming data to W3C standards with cross-database links makes data 5-star and is a valuable step in making biological sources Findable, Accessible, Interoperable, and Reusable. Thus, we leveraged Wikidata, a freely editable knowledge graph database to connect PanglaoDB to the semantic web. After creating classes and relations, we matched PanglaoDB's categories to Wikidata URIs and added the information via Wikidata's API. Then, we explored the 5-star data with SPARQL queries to ask questions like “which cell types express markers related to neurogenesis?” and “which diseases are related to human pancreatic beta cells?”. As Wikidata is connected to several biomedical resources, is under stable funding, and is continuously updated by contributors, it increases the magnitude and the stability of the contribution of PanglaoDB to the scientific community. The approach can be applied to any knowledge set of public interest (given proper permissions), providing a low-cost and low-barrier platform for sharing curated biological knowledge.



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