SWAT4HCLS 10th conference - Rome 2017


December 4th Tutorial day
December 5th-6th Conference days
December 7th Hackathon day


Registration will start at 9.00.
A welcome coffee service will be served at 10.00 and the tutorial will start at 10.30.
A lunch break will be offered from 12.30 to 13.30.
An afternoon coffee break is foreseen at 15.30.
Tracks are held in parallel. Tutorials within tracks are held in series.

Track A

Track B

Track C


Detailed Scientific Programme

The detailed scientific programme, still subject to small changes, is available in a separate page.

Confirmed Keynotes

Carole Goble, University of Manchester, United Kingdom
Carole Goble has spent her career wrangling semantics, software and scientists. She is a Full Professor in the School of Computer Science at the University of Manchester where she leads a team of researchers and software developers working in e-Science, building e-infrastructure for researchers working at the lab, national, and pan-national level. She applies technical advances in knowledge technologies, distributed computing, workflows and social computing to solve information management problems for Life Scientists, especially Systems Biology, and other scientific disciplines, including Biodiversity, Chemistry, Health informatics and Astronomy.
Her current research interests are in reproducible research, asset curation and preservation, semantic interoperability, knowledge exchange between scientists and new models of scholarly communication.
She is currently: the Head of ELIXIR-UK, co-leads the ELIXIR Interoperability Platform, coordinates the FAIRDOM initiative, is co-founder of the UK’s Software Sustainability Institute, and has a position on the Council of the BBSRC (the UK national funder of Bioscience).
She is one of the many authors of the influential FAIR Data Principles Nature paper and has been involved with the Semantic Web since way back: in 2001 she was a co-founder of the Journal of Web Semantics and its co-EIC for 7 years, and in 2014 she was the General Chair of the Intl Semantic Web Conference.
In 2008 she was awarded the Microsoft Jim Gray award for outstanding contributions to e-Science and in 2010 was elected a Fellow of the Royal Academy of Engineering. In 2014 she was made Commander of the Order of the British Empire by HM the Queen for her Services to Science: she got a bling medal.

Title of the talk: FAIRy stories: tales, musings and morals.
Findable Accessable Interoperable Reusable < data | models | SOPs | samples | articles| * >.
FAIR is a mantra; a meme; a myth; a mystery; a moan. For the past 15 years I have been working on FAIR in a bunch of projects and initiatives in Life Science projects. Some are top-down like Life Science European Research Infrastructures ELIXIR and ISBE, and some are bottom-up, supporting research projects in Systems and Synthetic Biology (FAIRDOM), Biodiversity (BioVel) and Pharmacology (open PHACTS), for example. Some have become movements, like Bioschemas, the Common Workflow Language and Research Objects. Others focus on cross-cutting approaches in reproducibility, computational workflows, metadata representation and scholarly sharing & publication.
In this talk I will relate a series of FAIRy tales. Some of them are Grimm. Some have happy endings. Who are the villains and who are the heroes? What are the morals we can draw from these stories?

Volker Tresp, Ludwig Maximilan University of Munich and Siemens, Germany
Volker Tresp received a Diploma degree from the University of Göttingen, Germany, in 1984 and the M.Sc. and Ph.D. degrees from Yale University, New Haven, CT, in 1986 and 1989 respectively. Since 1989 he is the head of various research teams in machine learning at Siemens, Research and Technology. He filed more than 70 patent applications and was inventor of the year of Siemens in 1996. He has published more than 150 scientific articles and administered over 20 Ph.D. theses. The company Panoratio is a spin-off out of his team. His research focus in recent years has been “Machine Learning in Information Networks” for modelling Knowledge Graphs, medical decision processes and sensor networks. He is the coordinator of one of the first nationally funded Big Data projects for the realization of “Precision Medicine”. Since 2011 he is also a Professor at the Ludwig Maximilian University of Munich where he teaches an annual course on Machine Learning.

Title of the talk: Learning with Knowledge Graphs: From Medical Decision Support to Human Perception and Memory
In recent years a number of large-scale triple-oriented knowledge graphs have been generated. They are being used in research and in applications to support search, text understanding and question answering. Knowledge graphs pose new challenges for machine learning, and several research groups have developed novel statistical models that can be used to compress knowledge graphs, to derive implicit facts, to detect errors, and to support the above mentioned applications. Some of the most successful statistical models are based on tensor decompositions that use latent representations of the involved generalized entities. In my talk I will introduce knowledge graphs and approaches to learning with knowledge graphs. In a medical application, we combined static knowledge graphs, which present patients background information, with dynamic knowledge graphs, which describe procedures, lab results and diagnosis, and with features extracted from unstructured data such as texts, images and genetic information, to realize an integrated clinical decision support system. We compared our decision support system with the decisions of physicians in a tumor board setting. Finally, I will discuss how knowledge graphs might be related to cognitive semantic memory, episodic memory and perception.

Peter Robinson, The Jackson Laboratory for Precision Medicine, Farmington, USA
Peter Robinson studied Mathematics and Computer Science at Columbia University and Medicine at the University of Pennsylvania. He completed training as a Pediatrician at the Charité University Hospital in Berlin, Germany.
His group developed the Human Phenotype Ontology (HPO), which is now an international standard for computation over human disease that is used by the Sanger Institute, several NIH-funded groups including the Undiagnosed Diseases Program, Genome Canada, the rare diseases section of the UK’s 100,000 Genomes Project, and many others.
The group develops algorithms and software for the analysis of exome and genome sequences and has used whole-exome sequencing and other methods to identify a number of novel disease genes

Title of the talk: The Human Phenotype Ontology: A Semantic Framework for Phenotype Driven Translational Research and Genomic Diagnostics
Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations, and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO has over 12,300 terms in a subtype hierarchy, which also encodes the specificity of each term for differential diagnosis as a function of the frequency of the term across all diseases in the HPO database. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute towards nascent efforts at global data exchange for identifying disease etiologies.
In this talk, I will explain how HPO-based algorithms for the analysis of clinical data work and show how they can be applied to genomic diagnostics for rare genetic diseases, which is an area where ontologies have made a substantial contribution to translational research and diagnostics. Although sequencing of exomes and whole genomes has enabled the discovery of hundreds of novel disease-associated genes, the diagnostic yield (percentage of patients who receive a molecular diagnosis) in many large-scale Exome/Genome Sequencing studies has been only 11% to 25% Variant-driven analysis struggles to identify a disease-causing variant among the roughly 100,000 variants in a typical exome or ~4.5 million variants in a typical genome. With the Human Phenotype Ontology (HPO), we have developed an ontology-based deep phenotyping approach wherein computable phenotypic profiles of human diseases and individual patients allow linking “fuzzy” matching of sets of terms that are close to each other in the hierarchy and weighted according to the specificity of individual phenotypic abnormalities. The phenotypic analysis can be integrated with the genomic analysis in order to increase the diagnostic yield.


  • Approach to an ontology-based mobile intervention for patients with hypertension
    Tyler Wheeler and Samina Abidi
  • Enriching the Human Phenotype Ontology with inferred axioms from textual descriptions
    Shahad Kudama, Rafael Berlanga and Ernesto Jiménez-Ruiz
  • BioKB – Text mining and semantic technologies for the biomedical content discovery
    Maria Biryukov, Valentin Gruès and Venkata Satagopam
  • Towards a FAIR Sharing of Scientific Experiments: Improving Discoverability and Reusability of Dielectric Measurements of Biological Tissues
    Md. Rezaul Karim, Matthias Heinrichs, Lars C. Gleim, Michael Cochez, Emily Porter, Alessandra La Gioia, Saqib Salahuddin, Stefan Decker, Martin O’Halloran and Oya Deniz Beyan
  • Drug Repurposing Using a Semantic Knowledge Graph
    Tareq B. Malas, Roman Kudrin, Sergei Starikov, Dorien Peters, Marco Roos, Peter A.C. ʼt Hoen and Kristina Hettne
  • Embracing Semantic Technology for Better Metadata Authoring in Biomedicine
    Attila L. Egyedi, Martin O’Connor, Marcos Martínez-Romero, Debra Willrett, Josef Hardi, John Graybeal and Mark Musen
  • Experiments to create ontology-based disease models for diabetic retinopathy from different biomedical resources
    Mercedes Arguello Casteleiro, Catalina Martínez Costa, Julio Des-Diz, Maria Jesus Fernandez-Prieto, Chris Wroe, Diego Maseda-Fernandez, George Demetriou, Goran Nenadic, John Keane, Stefan Schulz and Robert Stevens
  • Can Semantics-driven Query Expansion led by Plausible Patterns help Medical Knowledge Discovery and Decision Support?
    Hossein Mohammadhassanzadeh Syed Sibte Raza Abidi and Samina Raza Abidi
  • A Semantic Web Framework for Behavioral User Modeling and Action Planning for Personalized Behavior Modification
    William Van Woensel, Wasif Baig, Syed Sibte Raza Abidi and Samina Abidi
  • Achieving Pro-Active Guidance of Patients through ADL using Knowledge-Driven Activity Recognition and Complex Semantic Workflows
    William Van Woensel, Patrice Roy and Syed Sibte Raza Abidi
  • Blending FHIR RDF and OWL
    Harold Solbrig, Eric Prud’Hommeaux and Guoqian Jiang
  • Machine Learning based Drug Indication Prediction using Linked Open Data
    Remzi Çelebİ, Özgün Erten and Michel Dumontier
  • Linking African Traditional Medicine Knowledge
    A. Gossa Lo, Victor de Boer and Stefan Schlobach
  • Leveraging logical rules for efficacious representation of large orthology datasets
    Tarcisio M. Farias, Hirokazu Chiba and Jesualdo T. Fernández-Breis
  • Developing A Semantic Web-based Framework for Executing the Clinical Quality Language Using FHIR
    Guoqian Jiang, Eric Prud’Hommeaux and Harold Solbrig
  • Clustering Cancer Drugs According to their Mechanisms of Action
    Syed Abdullah Ali and Riza Theresa Batista-Navarro

Poster and demo session

Accepted software demos

  • Overview of a suite of tools and training material for implementing FAIR data principles (demo only)
    Mark Thompson, Luiz Olavo Bonino Da Silva Santos, Kees Burger, Rajaram Kaliyaperumal, Erik Schultes, Annika Jacobsen, Claudio Carta, Richard Finkers, David van Enckevort, Mascha Jansen, Barend Mons and Marco Roos
  • Message Exchange between Independent Implementations of Servers in the Nexus-PORTAL-DOORS System (demo only)
    Adam Craig, Seung-Ho Bae and Carl Taswell
  • LOD Surfer API: Web API for LOD Surfing using Class-Class Relationships in Life Sciences (also poster)
    Atsuko Yamaguchi, Kouji Kozaki, Yasunori Yamamoto, Hiroshi Masuya and Norio Kobayashi
  • DISQOVER: Through the large data landscape of Life Sciences (also poster)
    Berenice Wulbrecht, Filip Pattyn, Kenny Knecht and Hans Constandt
  • SPARQList: Markdown-based highly configurable REST API hosting server for SPARQL (also poster)
    Toshiaki Katayama and Shuichi Kawashima
  • SPARQL-Proxy: a generic proxy server for SPARQL endpoint (also poster)
    Shuichi Kawashima and Toshiaki Katayama

Accepted posters

  • Patient and Provider Perspectives on Usefulness of a Computerized Behaviour Modification Intervention in a Shared Decision Making Environment
    Samina Abidi, Michael Vallis, Syed Sibte Raza Abidi, Helena Piccinini-Vallis and Imran Syed Ali
  • Revealing disease similarities by text mining
    Alberto Calderone, Luana Licata, Elisa Micarelli, Livia Perfetto and Gianni Cesareni
  • Optimizing Semantic Data Transformation using High Performance Computing Techniques
    José Antonio Bernabé Díaz, María Del Carmen Legaz-García, José Manuel García Carrasco and Jesualdo Tomás Fernández-Breis
  • On Improving the Phenotype Acquisition Process using Semantic Web Technology
    Ernesto Jimenez-Ruiz, Dag Hovland, Laura Slaughter, Tony Handstad and Arild Waaler
  • BioGraph: Linking Biological Bases Across Organisms
    Luana Loubet Borges and André Santanchè
  • Assessment of FAIRness of open data sources in life sciences
    Filip Pattyn, Bérénice Wulbrecht, Kenny Knecht and Hans Constandt
  • Fuzzy Knowledge Base for Medical Training
    Ray Dueñas Jiménez, André Santanchè, Roger Vieira Horvat, Marcelo Schweller, Tiago de Araujo Guerra Grangeia and Marco Antonio Carvalho-Filho
  • Towards a community recommended choice of ontologies to increase interoperability of genetic variant data
    Annika Jacobsen, Friederike Ehrhart, Rajaram Kaliyaperumal, Mark Thompson, Erik A. Schultes, Jeroen F.J. Laros, Chris T. Evelo, Andra Waagmeester and Marco Roos
  • The FAIRification of data and the potential of FAIR resources demnostrated, in practice, at the Rome Bring Your Own Data workshop
    Claudio Carta, Marco Roos, Annika Jacobsen, Rajaram Kaliyaperumal, Mark Thompson, Mark Wilkinson, Ronald Cornet, Andra Waagmeester, David van Enckevort, Mascha Jansen, Luana Licata, Allegra Via and Domenica Taruscio
  • LOD Surfer API: Web API for LOD Surfing using Class-Class Relationships in Life Sciences (also demo)
    Atsuko Yamaguchi, Kouji Kozaki, Yasunori Yamamoto, Hiroshi Masuya and Norio Kobayashi
  • Metadata-driven interdisciplinary research projects using RIKEN MetaDatabase
    Norio Kobayashi, Satoshi Kume and Hiroshi Masuya
  • Bioschemas: schema.org for the Life Sciences
    Leyla Jael García Castro, Olga X. Giraldo, Alexander Garcia, Michel Dumontier and Bioschemas Community
  • The UniProt SPARQL Endpoint: 32 Billion Triples in Production
    Jerven Bolleman, Sebastien Gehant, Thierry Lombardot, Alan Bridge, Ioannis Xenarios and Nicole Redaschi
  • Document-centric workflow: a framework for ontology development
    Aisha Blfgeh and Phillip Lord
  • Visualizing metabolomics data in directed biological networks
    Ryan Miller, Denise Slenter, Martina Kutmon, Jonathan Melius, Georg Summer, Chris Evelo and Egon Willighagen
  • Shō-coin: A knowledge-based economy for Life Sciences
    Alexander Garcia, Erick Antezana, Jael García, Olga X. Giraldo, Pjotr Prins and Rutger Vos
  • Towards an Ontology for Sharing Information on Pharmacovigilance Signals
    Pantelis Natsiavas, Magnus Wallberg and Vassilis Koutkias
  • Construction of Knowledge-base for Clinical Interpretation of Genomic Variants
    Mayumi Kamada, Toshiaki Katayama, Shuichi Kawashima, Ryosuke Kojima, Masahiko Nakatsui and Yasushi Okuno
  • DISQOVER: Through the large data landscape of Life Sciences (also demo)
    Berenice Wulbrecht, Filip Pattyn, Kenny Knecht and Hans Constandt
  • SPARQL-Proxy: a generic proxy server for SPARQL endpoint (also demo)
    Shuichi Kawashima and Toshiaki Katayama
  • SPARQList: Markdown-based highly configurable REST API hosting server for SPARQL (also demo)
    Toshiaki Katayama and Shuichi Kawashima


The panel discussion theme and discussant list will appear here soon.
More information coming soon!


At the hackathon participants can work on any topic. At the start of the hackathon, there will be time for introductions and proposals.


The hackathon starts at 09:00. It is at the same location as the tutorials. Which is around the corner from the conference venue. Entering that building requires reporting at the reception of the main entrance. To enter the hackathon participants need to deposit an ID document at the main reception.

We are currently collecting ideas.
Please suggest a topic through this form:

Suggest Hackathon topic

The following topics have been suggested:


Proposed by: Daniel Mietchen

  • Annotate Wikidata items about publications with statements about their topics
  • Build workflow to curate Wikidata items for newly described/ redescribed taxa
  • Annotate Wikidata items about sound-making organisms with corresponding sound recordings

Deep Neural Networks for Analysing Cancer Genomics Data

Proposed by: Md. Rezaul Karim, Oya Beyan, Michael Cochez, Lars Gleim, Nils Lukas and Stefan Decker
Problem Definition:

  • Cancer type detection
  • Predicting cancer subtypes
  • Predicting survival rate

Further details: Hackaton_SWAT4LS_DeepLearing_for_Cancer_Genomics

BYOD ShExathon

Proposed by: Eric Prud’Hommeaux, Andra Waagmeester

Problem definition

  • Model your data
  • Detect inconsistencies
  • Develop data production pipelines


Problem definition
Proposed by: Leyla Garcia

  • Annotate with bioschema
  • Propose or adapt bioschema headers