SeWeBMeDA 2017
Semantic Web Solutions for Large-scale Biomedical Data Analytics

Table of Contents

• Preface
  This workshop invites papers for life sciences and biomedical data processing, as well as the amalgamation with Linked Data and Semantic Web technologies for better data analytics, knowledge discovery and user-targeted applications. This research contribution should provide useful information for the Knowledge Acquisition research community as well as the working Data Scientist. This workshop at the Extended Semantic Web Conference (ESWC) seeks original contributions describing theoretical and practical methods and techniques that present the anatomy of large scale linked data infrastructure, which covers: the distributed infrastructure to consume, store and query large volumes of heterogeneous linked data; using indexes and graph aggregation to better understand large linked data graphs, query federation to mix internal and external data-sources, and linked data visualisation tools for health care and life sciences. It will further cover topics around data integration, data profiling, data curation, querying, knowledge discovery, ontology mapping / matching / reconciliation and data / ontology visualisation, applications / tools / technologies / techniques for life sciences and biomedical domain. SeWeBMeDA aims to provide researchers in biomedical and life science, an insight and awareness about large scale data technologies for linked data, which are becoming increasingly important for knowledge discovery in the life sciences domain.
  

Session I - Biomedical data discovery and querying

• An Approach for Discovering and Exploring Semantic Relationships between Genes 1-15
  Nicoletta Dessi, Matteo Pani, Barbara Pes, Diego Reforgiato
• Federated Query Formulation and Processing through BioFed 16-19
  Ali Hasnain, Syeda Sana E Zainab, Dure Zehra, Qaiser Mehmood, Muhammad Saleem, Dietrich Rebholz-Schuhmann

Session 2: Biomedical data and document clustering

• Exploiting Cognitive Computing and Frame Semantic Features for Biomedical Document Clustering 20-34
  Danilo Dessi, Diego Reforgiato Recupero, Gianni Fenu, Sergio Consoli
• A Deep Learning Approach to Genomics Data for Population Scale Clustering and Ethnicity Prediction 35-49
  Md. Rezaul Karim, Achille Zappa, Ratnesh Sahay, Dietrich Rebholz-Schuhmann

Session 3: Biomedical Semantic Resources and Provenance

• SHARP: Harmonizing Cross-workflow Provenance 50-64
  Alban Gaignard, Khalid Belhajjame, Hala Skaf-Molli
• Quantifying the Content of Biomedical Semantic Resources as a Core for Drug Discovery Platforms 65-79
  Ali Hasnain, Dietrich Rebholz-Schuhmann
• SHARP: Harmonizing Galaxy and Taverna Workflow Provenance 80-83
  Alban Gaignard, Khalid Belhajjame, Hala Skaf-Molli