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

By Sumanta Basu, Karl Kumbier, James B. Brown, Bin Yu. 2017

324 citations recorded by the source corpus.

Significance We developed a predictive, stable, and interpretable tool: the iterative random forest algorithm (iRF). iRF discovers high-order interactions among biomolecules with the same order of computational cost as random forests. We demonstrate the efficacy of iRF by finding known and promising interactions among biomolecules, of up to fifth and sixth order, in two data examples in transcriptional regulation and alternative splicing. Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on random forests (RFs) and random intersection trees (RITs) and through extensive, biologically inspired simulations, we developed the iterative random forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with the same order of computational cost as the RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human-derived cell lines. In Drosophila, among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, third-order interactions, e.g., between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF rediscovered a central role of H3K36me3 in chromatin-mediated splicing regulation and identified interesting fifth- and sixth-order interactions, indicative of multivalent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens additional avenues of inquiry into the molecular mechanisms underlying genome biology.

Subfields: Artificial Intelligence, Molecular Biology, Statistics and Probability, Statistical Learning Theory

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Citations in the corpus

Local Linear Forests (2018, methodology): “More sophisticated tree-based ensembles motivated by random forests have been proposed by Basu et al. (2018), who iteratively grown feature-weighted tree ensembles that perform especially well for discovering interactions, Zhou and Hooker (2018), who considered a hybrid between random forests and…”
Identifying Causal Structure in Large-Scale Kinetic Systems (2018, methodology): “ques or various types of sparsity enforcing regularization; they usually focus on predictive performance, and sometimes consider stability with respect to resampling [Meinshausen and Buhlmann, 2010, Basu et al., 2018]. In this work, we develop novel methodology for structure search in ordinary diere”
Definitions, methods, and applications in interpretable machine learning (2019, methodology): “For instance, in one example discussed above (20), the authors evaluated a new interpretation method (iterative random forests) by demonstrating that it could be used to identify meaningful biological Boolean interactions for use in experiments.”
Three principles of data science: predictability, computability, and stability (PCS) (2018, methodology): “In our recent works on DeepTune (32), iterative random forests (iRF) (33), and signed iterative random forests (siRF) (43), we use PCS inference to make recommendations as inputs to downstream human decisions.”
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, unknown): “Iterative random forests [102,128] High-order interaction discovery.”