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Worry less about the algorithm, more about the sequence of events.

Farrokh Alemi1

  • 1Health Informatics Program, Department of Health Administration and Policy, George Mason University, Fairfax VA, USA.

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|December 31, 2020
PubMed
Summary

Incorporating sequence information into network learning algorithms significantly improves accuracy. This method enhances directed acyclical graph network discovery by considering variable order, leading to more reliable models.

Keywords:
IAMBcausal networksdirected acyclical graphfast IAMBgrow shrinkhill climbingincremental association Markov Blanketinterleaved IAMBmaximum-minimum hill climbingnetwork modelsrestricted maximizationreverse causalitysequence constraints

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Area of Science:

  • Computational biology and bioinformatics
  • Machine learning and artificial intelligence
  • Network science and systems biology

Background:

  • Numerous algorithms exist for learning network structure and parameters from data, such as Grow Shrink and Hill Climbing.
  • These algorithms often optimize data fit but disregard the temporal order of variable occurrences within the network structure.

Purpose of the Study:

  • To investigate whether incorporating sequence information (temporal order of variable occurrence) enhances the accuracy of algorithms learning directed acyclical graph networks.
  • To evaluate the impact of sequence constraints on network discovery accuracy using both simulated and real-world datasets.

Main Methods:

  • Simulated a 13-variable network with 10,000 observations per simulation, incorporating known sequence information for some variables.
  • Applied four conditional dependency and four search and score algorithms, using partial sequence information to constrain directed arcs.
  • Compared sequence-constrained and unconstrained algorithms using Area Under the Receiver Operating Curve (AROC) and analyzed a real dataset of 1.3 million disability assessments using Bayesian Information Criterion (BIC).

Main Results:

  • Sequence-constrained algorithms achieved significantly higher accuracy in simulated data (AROC = 0.94) compared to unconstrained algorithms (AROC = 0.74).
  • The agreement between discovered and observed networks improved substantially, from a range of 0.54–0.97 to 0.88–1 with sequence information.
  • In the real dataset, the Bayesian network constructed using sequence information showed a 6% lower BIC score, indicating a better fit.

Conclusions:

  • Sequence information demonstrably improves the accuracy of various network learning algorithms, including those for directed acyclical graph discovery.
  • The findings suggest that sequence information should be routinely considered when learning network structures from data for enhanced reliability and accuracy.
  • This approach offers a valuable enhancement for causal inference and predictive modeling in complex systems.