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BioOpener
REVEALING BIOMEDICAL KNOWLEDGE

What is BIOOPENER

The genomics era has led to great improvements in the understanding of the importance of personalised/precision medicine and novel drug discovery methods. The BIOOPENER project targets key challenges in understanding the disease of genome (i.e., cancer) are:

  • Discovering specific gene mutations for various cancer types.
  • Discovering pathways causing promoter changes in various cancer types.
  • Discovering associations between selected genetic features to drug-like bioactive compounds.
  • Discovering Protein Protein Interaction Networks (PPIs) and their implications.

The objective is to understand the tumorigenesis of various cancer types. The genetic and epigenetic changes are accumulated overtime transforming the normal tissue into invasive carcinoma.

The key approach is to link (connect) datasets, graphs and terminologies from a specific -omic level towards the broader, genetic, cellular and molecular context giving insight into cancer progression from a normal to healthy tissues with pathway components, mutations, associated phenotypes, and mechanism.

The BIOOPENER project aims to :

  • Automate Data Linkage.
  • Resolve Terminological Heterogeneity.
  • Handle Large Volume Data.
  • Federate Query Over Distributed Data Sources.
  • Support Intuitive Information Rich Visualisation & Analytics.
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Query Linked Genomics (QLG)

Query Linked Genomics (QLG) 1.0

Revision History


Version Year
1.0 Covers Ovarian Cancer Type 2016

Example Graphs

Explore BioOpener

A User Guide to Walk-Through Query Linked Genomics (QLG) 1.0

Core Features

In order to find, access, aggregate, and use multisite fragmented biomedical repositories for direct use into analytical pipe lines, the BIOOPENER platform has significant scientific & technical features.

Terminologies & Ontologies

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Terminologies & Ontologies

Provide access across multidimensional biomedical datasets as Linked Open Data (LOD)4 and provide intuitive data access, search, navigation and visualization.

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Linking Repositories

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Linking Repositories

Provide access across multidimensional biomedical datasets as Linked Open Data (LOD)4 and provide intuitive data access, search, navigation and visualization.

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Federated Querying

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Federated Querying

Provide users with the most comprehensive summary and data promoting exploratory analysis and Identifying functions of genes that play a relevant role in cancer development.

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Visualisation & Analytics

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Visualisation & Analytics

Provides visualisation and aggregation of high- throughput biomedical datasets and interconnected biomedical visualisation i.e (Peaks & Motifs) .

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