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Area of Science:

  • Bioinformatics
  • Computational Biology

Background:

  • Biological research relies on semantic similarity measures (SSM) to compare genes and gene products.
  • Current SSMs struggle with the scale and complexity of big data, hindering biological big data analysis.
  • There is a need for scalable and efficient SSMs to manage growing biological datasets.

Purpose of the Study:

  • To develop and evaluate a parallel and distributed processing approach for biological SSMs to address big data challenges.
  • To enhance existing SSMs (Resnik, SSDD, SORA) with threaded parallel processing for improved performance.
  • To assess the effectiveness of splitting Gene Ontology (GO) data and employing data clustering for optimizing SSM calculations.

Main Methods:

  • Implemented a three-step solution: splitting Gene Ontology (GO) data, data clustering, and semantic similarity calculation.
  • Enhanced three prominent SSMs (Resnik, Shortest Semantic Differentiation Distance (SSDD), and SORA) using threaded parallel processing.
  • Utilized parallel and distributed processing by partitioning data and applying SSMs to each partition.

Main Results:

  • Threaded parallel processing significantly reduced the time required for calculating semantic similarity between gene pairs.
  • Average time reductions were 24.51% for Resnik, 22.93% for SSDD, and 33.68% for SORA.
  • Total time reductions ranged from 8.88% to 39.27% for the enhanced SSMs, with greater reductions observed using split GO and data clustering compared to equal data division.

Conclusions:

  • The proposed parallel and distributed approach effectively manages big data scalability and computational issues in biological SSMs.
  • Threaded parallel processing enhances the performance of Resnik, SSDD, and SORA, leading to substantial time reductions.
  • The combination of split GO, data clustering, and threaded SSMs offers a powerful solution for efficient big data analysis in biology.