Muntazir Mehdi

Linked Biomedical Dataspace: Lessons Learned Integrating Data for Drug Discovery

The increase in the volume and heterogeneity of biomedical data sources has motivated researchers to embrace Linked Data (LD) technologies to solve the ensuing integration challenges and enhance information discovery. As an integral part of the EU GRANATUM project, a Linked Biomedical Dataspace (LBDS) was developed to semantically interlink data from multiple sources and augment the design of in silico experiments for cancer chemoprevention drug discovery. The different components of the LBDS facilitate both the bioinformaticians and the biomedical researchers to publish, link, query and visually explore the heterogeneous datasets. We have extensively evaluated the usability of the entire platform. In this paper, we showcase three different workflows depicting real-world scenarios on the use of LBDS by the domain users to intuitively retrieve meaningful information from the integrated sources. We report the important lessons that we learned through the challenges encountered and our accumulated experience during the collaborative processes which would make it easier for LD practitioners to create such dataspaces in other domains. We also provide a concise set of generic recommendations to develop LD platforms useful for drug discovery.

A Roadmap for Navigating the Life Sciences Linked Open Data Cloud

Multiple datasets that add high value to biomedical research have been exposed on the web as a part of the Life Sciences Linked Open Data (LSLOD) Cloud. The ability to easily navigate through these datasets is crucial for personalized medicine and the improvement of drug discovery process. However, navigating these multiple datasets is not trivial as most of these are only available as isolated SPARQL endpoints with very little vocabulary reuse. The content that is indexed through these endpoints is scarce, making the indexed dataset opaque for users. In this paper, we propose an approach for the creation of an active Linked Life Sciences Data Roadmap, a set of congurable rules which can be used to discover links (roads) between biological entities (cities) in the LSLOD cloud. We have catalogued and linked concepts and properties from 137 public SPARQL endpoints. Our Roadmap is primarily used to dynamically assemble queries retrieving data from multiple SPARQL endpoints simultaneously. We also demonstrate its use in conjunction with other tools for selective SPARQL querying, semantic annotation of experimental datasets and the visualization of the LSLOD cloud. We have evaluated the performance of our approach in terms of the time taken and entity capture. Our approach, if generalized to encompass other domains, can be used for road-mapping the entire LOD cloud.

On-the-fly generation of multidimensional data cubes for web of things

The dynamicity of sensor data sources and publishing real-time sensor data over a generalised infrastructure like the Web pose a new set of integration challenges. Semantic Sensor Networks demand excessive expressivity for efficient formal analysis of sensor data. This article specifically addresses the problem of adapting data model specific or context-specific properties in automatic generation of multidimensional data cubes. The idea is to generate data cubes on-the-fly from syntactic sensor data to sustain decision making, event processing and to publish this data as Linked Open Data.

SAFE: SPARQL federation over RDF data cubes with access control

BackgroundSeveral query federation engines have been proposed for accessing public Linked Open Data sources. However, in many domains, resources are sensitive and access to these resources is tightly controlled by stakeholders; consequently, privacy is a major concern when federating queries over such datasets. In the Healthcare and Life Sciences (HCLS) domain real-world datasets contain sensitive statistical information: strict ownership is granted to individuals working in hospitals, research labs, clinical trial organisers, etc. Therefore, the legal and ethical concerns on (i) preserving the anonymity of patients (or clinical subjects); and (ii) respecting data ownership through access control; are key challenges faced by the data analytics community working within the HCLS domain. Likewise statistical data play a key role in the domain, where the RDF Data Cube Vocabulary has been proposed as a standard format to enable the exchange of such data. However, to the best of our knowledge, no existing approach has looked to optimise federated queries over such statistical data.ResultsWe present SAFE: a query federation engine that enables policy-aware access to sensitive statistical datasets represented as RDF data cubes. SAFE is designed specifically to query statistical RDF data cubes in a distributed setting, where access control is coupled with source selection, user profiles and their access rights. SAFE proposes a join-aware source selection method that avoids wasteful requests to irrelevant and unauthorised data sources. In order to preserve anonymity and enforce stricter access control, SAFE’s indexing system does not hold any data instances—it stores only predicates and endpoints. The resulting data summary has a significantly lower index generation time and size compared to existing engines, which allows for faster updates when sources change.ConclusionsWe validate the performance of the system with experiments over real-world datasets provided by three clinical organisations as well as legacy linked datasets. We show that SAFE enables granular graph-level access control over distributed clinical RDF data cubes and efficiently reduces the source selection and overall query execution time when compared with general-purpose SPARQL query federation engines in the targeted setting.

A linked data platform for finite element biosimulations

Biosimulation models have been recently introduced to understand the exact causative factors that give rise to impairment in human organs. Finite Element Method (FEM) provides a mathematical framework to simulate dynamic biological systems, with applications ranging from human ear, cardiovascular, to neurovascular research. Due to lack of a well-integrated data infrastructure, the steps involved in the execution and comparative evaluation of large Finite Element (FE) simulations are time consuming and are performed in isolated environments. In this paper, we present a Linked Data platform to improve the automation in integration, analysis and visualisation of biosimulation models for the inner-ear (cochlea) mechanics. The proposed platform aims to help domain scientists and clinicians for exploring and analysing Finite Element (FE) numerical data and simulation results obtained from multiple domains such as biological, geometrical, mathematical, physical models. We validate the platform by conducting a qualitative survey and perform quantitative experiments to record overall performance.

Towards precision medicine: discovering novel gynecological cancer biomarkers and pathways using linked data

BackgroundNext Generation Sequencing (NGS) is playing a key role in therapeutic decision making for the cancer prognosis and treatment. The NGS technologies are producing a massive amount of sequencing datasets. Often, these datasets are published from the isolated and different sequencing facilities. Consequently, the process of sharing and aggregating multisite sequencing datasets are thwarted by issues such as the need to discover relevant data from different sources, built scalable repositories, the automation of data linkage, the volume of the data, efficient querying mechanism, and information rich intuitive visualisation.ResultsWe present an approach to link and query different sequencing datasets (TCGA, COSMIC, REACTOME, KEGG and GO) to indicate risks for four cancer types – Ovarian Serous Cystadenocarcinoma (OV), Uterine Corpus Endometrial Carcinoma (UCEC), Uterine Carcinosarcoma (UCS), Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) – covering the 16 healthy tissue-specific genes from Illumina Human Body Map 2.0. The differentially expressed genes from Illumina Human Body Map 2.0 are analysed together with the gene expressions reported in COSMIC and TCGA repositories leading to the discover of potential biomarkers for a tissue-specific cancer.ConclusionWe analyse the tissue expression of genes, copy number variation (CNV), somatic mutation, and promoter methylation to identify associated pathways and find novel biomarkers. We discovered twenty (20) mutated genes and three (3) potential pathways causing promoter changes in different gynaecological cancer types. We propose a data-interlinked platform called BIOOPENER that glues together heterogeneous cancer and biomedical repositories. The key approach is to find correspondences (or data links) among genetic, cellular and molecular features across isolated cancer datasets giving insight into cancer progression from normal to diseased tissues. The proposed BIOOPENER platform enriches mutations by filling in missing links from TCGA, COSMIC, REACTOME, KEGG and GO datasets and provides an interlinking mechanism to understand cancer progression from normal to diseased tissues with pathway components, which in turn helped to map mutations, associated phenotypes, pathways, and mechanism.

Drug Dosage Balancing Using Large Scale Multi-omics Datasets

Cancer is a disease of biological and cell cycle processes, driven by dosage of the limited set of drugs, resistance, mutations, and side effects. The identification of such limited set of drugs and their targets, pathways, and effects based on large scale multi-omics, multi-dimensional datasets is one of key challenging tasks in data-driven cancer genomics. This paper demonstrates the use of public databases associated with Drug-TargetGene/Protein-Disease to dissect the in-depth analysis of approved cancer drugs, their genetic associations, their pathways to establish a dosage balancing mechanism. This paper will also help to understand cancer as a disease associated pathways and effect of drug treatment on the cancer cells. We employ the Semantic Web approach to provide an integrated knowledge discovery process and the network of integrated datasets. The approach is employed to sustain the biological questions involving 1 Associated drugs and their omics signature, 2i¾?Identification of gene association with integrated Drug-Target databases 3 Mutations, variants, and alterations from these targets 4 Their PPI Interactions and associated oncogenic pathways 5 Associated biological process aligned with these mutations and pathways to identify IC-50 level of each drug along-with adverse events and alternate indications. In principal this large semantically integrated database of around 30 databases will serve as Semantic Linked Association Prediction in drug discovery to explore and expand the dosage balancing and drug re-purposing.

Demonstrating a Linked Data Visualiser for Finite Element Biosimulations

Healthcare experts have recently turned towards the use of Biosimulation models to understand the multiple or different causative factors that cause impairment in human organs. The applications of biosimulations have been applied in different biological systems ranging from human ear, cardiovascular, to neurovascular research using Finite Element Method (FEM). FEM provide a mathematical framework to simulate these dynamic biological systems. Visualizing and analyzing huge amounts of Finite Element (FE) Biosimulation numerical data is a strenuous task. In this paper, we demonstrate a Linked Data Visualiser -- called SIFEM Visualiser -- to help domain-experts to Visualise, analyze and compare biosimulation results from heterogeneous, complex, and high volume numerical data. The SIFEM Visualiser aims to help healthcare experts in exploring and analyzing Finite Element (FE) numerical data and simulation results obtained from different aspects of inner-ear (Cochlear) model - such as biological, geometrical, mathematical, and physical models.

Extending inner-ear anatomical concepts in the Foundational Model of Anatomy (FMA) ontology

The inner ear is physically inaccessible in living humans, which leads to unique difficulties in studying its normal function and pathology as in other human organs. Recently, biosimulation model has gained a significant attention to understand the exact causative factors that give rise to impairment in human organs. However, to build a biosimulation model for human organ concepts and their topological relationships from multiple and semantically overlapping domains such as biology, anatomy, geometrical, mathematical, physical models are required. In this paper, we focus on modelling the inner-ear macro anatomical concepts and their topological relationships. We extended the Foundational Model of Anatomy (FMA) ontology to cover micro-level version of human inner-ear anatomy where connection between simulating tissues, liquids, soft tissues and connecting adjacent (e.g. hair cells, perilymph) parts studied in detail, included and implemented.

Synthesizing a Criterion for SOA Reference Architecture to Sustain eParticipation

With inception of Service-Orientation in research and industry, the need to select a Reference Architecture (RA) that supports Service Orientation in some specific domain has developed into a challenge. Institutionalizing a criterion that helps software designers and developers to properly extend or design an RA for a domain-specific, goal-aware and context-aware implementation of a Service-Oriented Architecture (SOA) system has evolved into a necessity. In this article, a criterion derived from understanding existing standard SOA reference architectures is presented. In following presented work, we focus specifically on the eParticipation domain to validate the proposed criterion. The criterion will not only help improve the process of refining and specialising standard SOA-RA, but also provides a set of key ingredients to sustain SOA-RA definition in the eGovernment domain, specifically to sustain information integration in eParticipation.