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Biclustering fMRI time series: a comparative study.

Eduardo N Castanho1, Helena Aidos1, Sara C Madeira2

  • 1LASIGE, Faculdade de Ciências, Universidade de Lisboa, Lisbon, Portugal.

BMC Bioinformatics
|May 23, 2022
PubMed
Summary
This summary is machine-generated.

Biclustering, a method for simultaneous row and column clustering, shows promise for analyzing functional Magnetic Resonance Imaging (fMRI) time series data. While exhaustive approaches yield homogeneous biclusters, scalability remains a challenge for real-world neuroscience applications.

Keywords:
BiclusteringNeurosciencesTime series analysisfMRI

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Area of Science:

  • Neuroscience
  • Data Science
  • Bioinformatics

Background:

  • Biclustering, a simultaneous row and column clustering technique, has proven effective in gene expression data analysis.
  • Despite recognized potential, research on biclustering applications beyond gene expression is limited.
  • A gap exists in exploring biclustering's utility in other scientific domains.

Purpose of the Study:

  • To evaluate biclustering for functional Magnetic Resonance Imaging (fMRI) time series data.
  • To compare biclustering algorithms with traditional clustering methods on fMRI data.
  • To propose a novel methodology for evaluating biclustering outside of gene expression analysis.

Main Methods:

  • Comparison of seven state-of-the-art biclustering algorithms and three traditional clustering algorithms.
  • Application of algorithms to both artificial and real fMRI time series data.
  • Development of a biclustering evaluation methodology tailored for spatio-temporal data.

Main Results:

  • Exhaustive biclustering approaches demonstrated superiority in identifying homogeneous biclusters in fMRI data.
  • Both artificial and real fMRI time series data were analyzed to assess algorithm performance.
  • The proposed evaluation methodology confirmed biclustering's effectiveness in detecting local patterns.

Conclusions:

  • Biclustering offers a promising avenue for spatio-temporal data analysis, particularly in neuroscience.
  • The study highlights the effectiveness of biclustering in uncovering local patterns within fMRI time series.
  • Further research on scalability is crucial for the practical implementation of biclustering in fMRI analysis.