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Iterative random forests to discover predictive and stable high-order interactions.

Sumanta Basu1,2,3, Karl Kumbier4, James B Brown5,4,6,7

  • 1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853.

Proceedings of the National Academy of Sciences of the United States of America
|January 21, 2018
PubMed
Summary
This summary is machine-generated.

The iterative random forest (iRF) algorithm identifies complex, high-order gene interactions. This new method efficiently detects stable molecular interactions, advancing our understanding of gene expression and regulation.

Keywords:
genomicshigh-order interactioninterpretable machine learningrandom forestsstability

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Area of Science:

  • Genomics and computational biology
  • Molecular mechanisms of gene regulation
  • Statistical modeling for biological data

Background:

  • Genomic assays provide molecular insights but understanding high-order interactions is challenging.
  • Existing methods struggle to efficiently detect complex, multi-component molecular interactions.
  • Gene expression is driven by intricate interactions within larger molecular machines.

Purpose of the Study:

  • To develop a novel algorithm for detecting stable, high-order molecular interactions.
  • To address the statistical challenges in understanding how molecular interactions drive gene expression.
  • To provide a computationally efficient method for discovering complex biological relationships.

Main Methods:

  • Development of the iterative random forest (iRF) algorithm, building on random forests (RFs) and random intersection trees (RITs).
  • Training a feature-weighted ensemble of decision trees to identify stable, high-order interactions.
  • Application of iRF to enhancer activity prediction in *Drosophila* and alternative splicing in human cell lines.

Main Results:

  • iRF identified 80% of previously reported pairwise transcription factor interactions in *Drosophila* enhancer activity.
  • The algorithm detected novel third-order interactions in *Drosophila*, suggesting new experimental hypotheses.
  • In human cells, iRF confirmed the role of H3K36me3 in splicing and revealed fifth- and sixth-order interactions related to nucleosome function.

Conclusions:

  • The iterative random forest (iRF) algorithm efficiently detects high-order molecular interactions.
  • iRF facilitates the discovery of complex regulatory relationships in genomics.
  • This method opens new avenues for investigating molecular mechanisms in genome biology.