Integrated System Supporting Research on Environment Related Cancers

Bensz, Wojciech; Borys, Damian; Fujarewicz, Krzysztof; Herok, Kinga; Jaksik, Roman; Krasucki, Marcin; Kurczyk, Agata; Matusik, Kamil; Mrozek, Dariusz; Ochab, Magdalena; Pacholczyk, Marcin; Pieter, Justyna; Puszyński, Krzysztof; Psiuk-Maksymowicz, Krzysztof; Student, Sebastian; Świerniak, Andrzej; Śmieja, Jarosław

doi:10.1007/978-3-319-31277-4_35

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…SysCancer is a research project aimed at developing an integrated system that combines all stages of cancer studies (Bensz, et al, 2016). The central data warehouse, developed as part of the project, is used to support multidimensional analysis starting from local databases after the data, intended for public access, has been gathered and integrated.…”

Section: Related Workmentioning

confidence: 99%

A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases

Capuano

Foggia

Greco

et al. 2022

International Journal on Semantic Web and Information Systems

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Understanding the role played by genetic variations in diseases, exploring genomic variants and discovering disease-associated loci are among the most pressing challenges of genomic medicine. A huge and ever-increasing amount of information is available to researchers to address these challenges. Unfortunately, it is stored in fragmented ontologies and databases, which use heterogeneous formats and poorly integrated schemas. To overcome these limitations, we propose a linked data approach, based on the formalism of multilayer networks, able to integrate and harmonize biomedical information from multiple sources into a single dense network covering different aspects on Neuroendocrine Neoplasms (NENs). The proposed integration schema consists of three interconnected layers representing, respectively, information on the disease, on the affected genes, on the related biological processes and molecular functions. An easy-to-use client-server application was also developed to browse and search for information on the model supporting multilayer network analysis.

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Section: Related Workmentioning

confidence: 99%

A Semantic Framework Supporting Multilayer Networks Analysis for Rare Diseases

Capuano

Foggia

Greco

et al. 2022

International Journal on Semantic Web and Information Systems

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“…In the domain of cancer research, SysCancer has the aim to provide an integrated system that combines different stages of cancer studies [19]. The data warehouse can allow a multidimensional analysis of collected and integrated data meant for public access.…”

Section: Background and Related Workmentioning

confidence: 99%

A Linked Data Application for Harmonizing Heterogeneous Biomedical Information

et al. 2022

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In the biomedical field, there is an ever-increasing number of large, fragmented, and isolated data sources stored in databases and ontologies that use heterogeneous formats and poorly integrated schemes. Researchers and healthcare professionals find it extremely difficult to master this huge amount of data and extract relevant information. In this work, we propose a linked data approach, based on multilayer networks and semantic Web standards, capable of integrating and harmonizing several biomedical datasets with different schemas and semi-structured data through a multi-model database providing polyglot persistence. The domain chosen concerns the analysis and aggregation of available data on neuroendocrine neoplasms (NENs), a relatively rare type of neoplasm. Integrated information includes twelve public datasets available in heterogeneous schemas and formats including RDF, CSV, TSV, SQL, OWL, and OBO. The proposed integrated model consists of six interconnected layers representing, respectively, information on the disease, the related phenotypic alterations, the affected genes, the related biological processes, molecular functions, the involved human tissues, and drugs and compounds that show documented interactions with them. The defined scheme extends an existing three-layer model covering a subset of the mentioned aspects. A client–server application was also developed to browse and search for information on the integrated model. The main challenges of this work concern the complexity of the biomedical domain, the syntactic and semantic heterogeneity of the datasets, and the organization of the integrated model. Unlike related works, multilayer networks have been adopted to organize the model in a manageable and stratified structure, without the need to change the original datasets but by transforming their data “on the fly” to respond to user requests.

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