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Related Concept Videos

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Related Experiment Video

Updated: Jun 21, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Fast multisegment alignments for temporal expression profiles.

Adam A Smith1, Mark Craven

  • 1Department of Computer Sciences, University of Wisconsin, Madison, Wisconsin 53706, USA. aasmith@cs.wisc.edu

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference
|August 1, 2009
PubMed
Summary
This summary is machine-generated.

We developed two heuristics to accelerate a toxicogenomic time-series alignment algorithm. These methods improve speed without sacrificing the accuracy of the multisegment alignment, making complex data analysis more efficient.

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Last Updated: Jun 21, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Toxicogenomic data analysis relies on accurate time-series alignment.
  • Existing dynamic time warping (DTW) algorithms offer O(n(2)) complexity but may lack accuracy for complex datasets.
  • Previous work introduced a more accurate multisegment alignment algorithm with O(n(5)) time complexity.

Purpose of the Study:

  • To enhance the efficiency of a previously developed multisegment time-series alignment algorithm.
  • To introduce heuristics that reduce computational time without compromising alignment accuracy.
  • To make advanced toxicogenomic data analysis more accessible and faster.

Main Methods:

  • Developed a cone-shaped restriction heuristic to limit alignment search space, achieving a constant factor speedup.
  • Implemented a second heuristic that restricts alignments to those near a DTW-like method's output, reducing complexity to O(n(3)).
  • Validated that both heuristics maintain the accuracy of the original multisegment alignment algorithm.

Main Results:

  • The cone-shaped heuristic provides a constant factor speedup.
  • The DTW-like heuristic significantly reduces time complexity to O(n(3)).
  • Both introduced heuristics preserve the high accuracy of the original O(n(5)) algorithm.

Conclusions:

  • The presented heuristics offer a practical solution for accelerating accurate time-series alignment in toxicogenomics.
  • These methods balance computational efficiency with the need for precise data analysis.
  • The improved algorithm facilitates more rapid similarity queries on toxicogenomic time-series data.