Sensitivity analysis of granularity levels in complex biological networks

Sean West, Hesham Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The influx of biomedical measurement technologies continues to define a rapidly changing and growing landscape, multi-modal and uncertain in nature. The focus of the biomedical research community shifted from pure data generation to the development of methodologies for data analytics. Although many researchers continue to focus on approaches developed for analyzing single types of biological data, recent attempts have been made to utilize the availability of multiple heterogeneous data sets that contain various types of data and try to establish tools for data fusion and analysis in many bioinformatics applications. At the heart of this initiative is the attempt to consolidate the domain knowledge and experimental data sources in order to enhance our understanding of highly-specific conditions dependent on sensory data containing inherent error. This challenge refers to granularity: the specificity or mereology of alternate information sources may impact the final data fusion. In an earlier work, we employed data integration methods to analyze biological data obtained from protein interaction networks and gene expression data. We conducted a study to show that potential problems can arise from integrating or fusing data obtained at different granularity levels and highlight the importance of developing advanced data fusing techniques to integrate various types of biological data for analytical purposes. In this work, we explore the impact of granularity from a more formulized approach and show that granularity levels significantly impact the quality of knowledge extracted from the heterogeneous data sets. Further, we extend our previous results to study the relationship between granularity and knowledge extraction across multiple diseases, examining generalizability and estimating the utility of a similar methodology to reflect the impact of granularity levels.

Original languageEnglish (US)
Title of host publicationBiomedical Engineering Systems and Technologies - 9th International Joint Conference, BIOSTEC 2016, Revised Selected Papers
EditorsHugo Gamboa, Ana Fred
PublisherSpringer Verlag
Pages167-188
Number of pages22
ISBN (Print)9783319547169
DOIs
StatePublished - 2017
EventDoctoral Consortium - 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016 - Rome, Italy
Duration: Feb 21 2016Feb 23 2016

Publication series

NameCommunications in Computer and Information Science
Volume690
ISSN (Print)1865-0929

Other

OtherDoctoral Consortium - 9th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2016
CountryItaly
CityRome
Period2/21/162/23/16

Keywords

  • Clusters
  • Co-regulation
  • Correlation networks
  • Data integration
  • Gene expression data
  • Knowledge extraction
  • Protein-protein interaction

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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  • Cite this

    West, S., & Ali, H. (2017). Sensitivity analysis of granularity levels in complex biological networks. In H. Gamboa, & A. Fred (Eds.), Biomedical Engineering Systems and Technologies - 9th International Joint Conference, BIOSTEC 2016, Revised Selected Papers (pp. 167-188). (Communications in Computer and Information Science; Vol. 690). Springer Verlag. https://doi.org/10.1007/978-3-319-54717-6_10