Cited by 320Genomics and Chromatin DynamicsRNA Research and SplicingBioinformatics and Genomic Networks

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

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Basu, Sumanta, Kumbier, Karl, Brown, James B., Yu, Bin·2018·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1711236115PMC PMC5828575

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Abstract

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.

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Fig. 1. iRF workflow. Iteratively reweighted RFs (blue boxes) are trained on full data D and pass Gini importance as weights to the next iteration. In iteration K (red box), feature-weighted RFs are grown using w^(K) on B bootstrap samples of the full data D(1),…,D(B). Decision paths and predicted leaf node labels are passed to the RIT (green box), which computes prevalent interactions in the RF ensemble. Recovered interactions are scored for stability across (outer-layer) bootstrap samples.CC BY-NC-ND · PubMed Central

Fig. 2. (A) Accuracy of iRF (AUC-PR) in predicting active elements from TF binding and histone modification data. (B) The 20 most stable interactions recovered by iRF after five iterations. Interactions that are a strict subset of another interaction with stability score ≥0.5 have been removed for cleaner visualization. iRF recovers known interactions among Gt, Kr, and Hb and interacting roles of master regulator Zld. (C) Surface maps demonstrating the proportion of active enhancers by quantiles of Zld, Gt, and Kr binding (held-out test data). On the subset of data where Kr binding is lower than the median Kr level, the proportion of active enhancers does not change with Gt and Zld. On the subset of data with Kr binding above the median level, the structure of the response surface reflects an order-3 AND interaction: Increased levels of Zld, Gt, and Kr binding are indicative of enhancer status for a subset of observations. (D) Quantiles of Zld, Gt, and Kr binding grouped by enhancer status (balanced sample of held-out test data). The block of active elements highlighted in red represents the subset of observations for which the AND interaction is active.CC BY-NC-ND · PubMed Central

Fig. 3. (A) Accuracy of iRF (AUC-PR) in classifying included exons from excluded exons in held-out test data. iRF shows 7% increase in AUC-PR over RF. (B) An order-6 interaction recovered by iRF (stability score 0.5) displayed on a superheat map which juxtaposes two separately clustered heat maps of exons with high and low splicing rates. Coenrichment of all six plotted features reflects an AND-type rule indicative of high splicing rates for the exons highlighted in red (held-out test data). The subset of Pol II, S2 phospho-Pol II, H3K36me3, H3K79me2, and H4K20me1 was recovered as an order-5 interaction in all bootstrap samples (stability score 1.0). (C) The 20 most stable interactions recovered in the second iteration of iRF. Interactions that are a strict subset of another interaction with stability score ≥0.5 have been removed for cleaner visualization.CC BY-NC-ND · PubMed Central

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Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. (1997) · European Conference on Computational Learning Theory · doi:10.1007/3-540-59119-2_166 · Cited by 23,988
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
Bagging predictors
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Mutations affecting segment number and polarity in Drosophila
Nüsslein‐Volhard, C., & Wieschaus, E. F. (1980) · Nature · doi:10.1038/287795a0 · Cited by 4,370
A non-radioactive in situ hybridization method for the localization of specific RNAs in Drosophila embryos reveals translational control of the segmentation gene hunchback
Tautz, D., & Pfeifle, C. (1989) · Chromosoma · doi:10.1007/bf00291041 · Cited by 2,518
ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia
Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B. E., Bickel, P., Brown, J. B., Cayting, P., et al. (2012) · Genome Research · doi:10.1101/gr.136184.111 · Cited by 2,033
Self-citation
Epigenome editing by a CRISPR-Cas9-based acetyltransferase activates genes from promoters and enhancers
Hilton, I. B., D’Ippolito, A. M., Vockley, C. M., Thakore, P. I., Crawford, G. E., Reddy, T. E., & Gersbach, C. A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3199 · Cited by 1,931
Multifactor-Dimensionality Reduction Reveals High-Order Interactions among Estrogen-Metabolism Genes in Sporadic Breast Cancer
Ritchie, M. D., Hahn, L. W., Roodi, N., Bailey, L., Dupont, W. D., Parl, F. F., & Moore, J. H. (2001) · The American Journal of Human Genetics · doi:10.1086/321276 · Cited by 1,899
Predictive learning via rule ensembles
Friedman, J. H., & Popescu, B. E. (2008) · The Annals of Applied Statistics · doi:10.1214/07-aoas148 · Cited by 1,315
Extensive Promoter-Centered Chromatin Interactions Provide a Topological Basis for Transcription Regulation
Li, G., Ruan, X., Auerbach, R. K., Sandhu, K. S., Zheng, M., Wang, P., Poh, H. M., Goh, Y., Lim, J., Zhang, J., et al. (2012) · Cell · doi:10.1016/j.cell.2011.12.014 · Cited by 1,232
Measuring reproducibility of high-throughput experiments
Li, Q., Brown, J. B., Huang, H., & Bickel, P. J. (2011) · The Annals of Applied Statistics · doi:10.1214/11-aoas466 · Cited by 1,032
Self-citation
Measuring reproducibility of high-throughput experiments
Li, Q., Brown, J. B., Huang, H., & Bickel, P. J. (2010) · UC Berkeley · Cited by 915
Self-citation
Indian Hedgehog: A Mechanotransduction Mediator in Condylar Cartilage
Tang, G. H., Rabie, A. B. M., & Hägg, U. (2004) · Journal of Dental Research · doi:10.1177/154405910408300516 · Cited by 819
Differential chromatin marking of introns and expressed exons by H3K36me3
Kolasinska-Zwierz, P., Down, T. A., Latorre, I. J., Liu, T., Liu, X. S., & Ahringer, J. (2009) · Nature Genetics · doi:10.1038/ng.322 · Cited by 699
A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries
Fisher, S., Barry, A. J., Abreu, J., Minie, B., Nolan, J., Delorey, T. M., Young, G., Fennell, T. J., Allen, A., Ambrogio, L., et al. (2011) · Genome biology · doi:10.1186/gb-2011-12-1-r1 · Cited by 676
Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome
Berman, B. P., Nibu, Y., Pfeiffer, B. D., Tomančák, P., Celniker, S., Levine, M., Rubin, G. M., & Eisen, M. B. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.231608898 · Cited by 605
Lehninger principles of biochemistry
Nelson, D. L., Cox, M. M., & Lehninger, A. L. (2008) · Cited by 600
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Hoffman, M. M., Ernst, J., Wilder, S. P., Kundaje, A. B., Harris, R. S., Libbrecht, M., Giardine, B. M., Ellenbogen, P. M., Bilmes, J. A., Birney, E., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1284 · Cited by 529
Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAsPreprint
Tilgner, H., Knowles, D. G., Johnson, R., Davis, C., Chakrabortty, S. K., Djebali, S., Curado, J., Snyder, M. P., Gingeras, T., & Guigó, R. (2012) · Genome Research · doi:10.1101/gr.134445.111 · Cited by 486
Transcription Factors Bind Thousands of Active and Inactive Regions in the Drosophila Blastoderm
Li, X., MacArthur, S., Bourgon, R., Nix, D. A., Pollard, D. A., Iyer, V. N., Hechmer, A., Simirenko, L., Stapleton, M., Hendriks, C. L. L., et al. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060027 · Cited by 485
Bayesian inference of epistatic interactions in case-control studies
Zhang, Y., & Liu, J. S. (2007) · Nature Genetics · doi:10.1038/ng2110 · Cited by 478
The zinc-finger protein Zelda is a key activator of the early zygotic genome in Drosophila
Liang, H.-L., Nien, C.-Y., Liu, H.-Y., Metzstein, M. M., Kirov, N., & Rushlow, C. (2008) · Nature · doi:10.1038/nature07388 · Cited by 478
Transcriptional Enhancers in Animal Development and Evolution
Levine, M. (2010) · Current Biology · doi:10.1016/j.cub.2010.06.070 · Cited by 468
Genome-scale functional characterization of Drosophila developmental enhancers in vivo
Kvon, E. Z., Kazmar, T., Stampfel, G., Yáñez-Cuna, J. O., Pagani, M., Schernhuber, K., Dickson, B. J., & Stark, A. (2014) · Nature · doi:10.1038/nature13395 · Cited by 461
Genetic adaptation to high altitude in the Ethiopian highlands
Scheinfeldt, L., Soi, S., Thompson, S. R., Ranciaro, A., Meskel, D. W., Beggs, W. R., Lambert, C. A., Jarvis, J. P., Abate, D., Belay, G., et al. (2012) · Genome biology · doi:10.1186/gb-2012-13-1-r1 · Cited by 459
Zelda Binding in the Early Drosophila melanogaster Embryo Marks Regions Subsequently Activated at the Maternal-to-Zygotic Transition
Harrison, M. M., Li, X.-Y., Kaplan, T., Botchan, M. R., & Eisen, M. B. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002266 · Cited by 338
Developmental roles of 21 Drosophila transcription factors are determined by quantitative differences in binding to an overlapping set of thousands of genomic regions
MacArthur, S., Li, X., Li, J. J., Brown, J. B., Chu, H. C., Zeng, L., Grondona, B. P., Hechmer, A., Simirenko, L., Keränen, S. V. E., et al. (2009) · Genome biology · doi:10.1186/gb-2009-10-7-r80 · Cited by 333
Self-citation
The gap gene network
Jaeger, J. (2010) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-010-0536-y · Cited by 290
Whole-genome ChIP–chip analysis of Dorsal, Twist, and Snail suggests integration of diverse patterning processes in the Drosophila embryo
Zeitlinger, J., Zinzen, R. P., Stark, A., Kellis, M., Zhang, H., Young, R. A., & Levine, M. (2007) · Genes & Development · doi:10.1101/gad.1509607 · Cited by 286
Control of Drosophila body pattern by the hunchback morphogen gradient
Struhl, G., Johnston, P., & Lawrence, P. A. (1992) · Cell · doi:10.1016/0092-8674(92)90405-2 · Cited by 285
From gradients to stripes in Drosophila embryogenesis: filling in the gaps
Rivera-Pomar, R. V., & Jäckle, H. (1996) · Trends in Genetics · doi:10.1016/0168-9525(96)10044-5 · Cited by 264
Modeling gene expression using chromatin features in various cellular contexts
Dong, X., Greven, M. C., Kundaje, A. B., Djebali, S., Brown, J., Cheng, C., Gingeras, T., Gerstein, M., Guigó, R., Birney, E., et al. (2012) · Genome biology · doi:10.1186/gb-2012-13-9-r53 · Cited by 263
Self-citation
ATP-dependent chromatin remodeling during mammalian development
Hota, S. K., & Bruneau, B. G. (2016) · Development · doi:10.1242/dev.128892 · Cited by 253
Temporal Coordination of Gene Networks by Zelda in the Early Drosophila Embryo
Nien, C.-Y., Liang, H.-L., Butcher, S., Sun, Y., Fu, S., Gocha, T., Kirov, N., Manak, J. R., & Rushlow, C. A. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002339 · Cited by 242
Enriched random forests
Amaratunga, D., Cabrera, J., & Lee, Y.-S. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn356 · Cited by 238
Feature selection via regularized trees
Deng, H., & Runger, G. C. (2012) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252640 · Cited by 206
Dynamic reprogramming of chromatin accessibility during Drosophilaembryo development
Thomas, S., Li, X., Sabo, P. J., Sandstrom, R. S., Thurman, R. E., Canfield, T. K., Giste, E., Fisher, W., Hammonds, A. S., Celniker, S., et al. (2011) · Genome biology · doi:10.1186/gb-2011-12-5-r43 · Cited by 176
Cross-regulatory interactions among the gap genes of Drosophila
Jäckle, H., Tautz, D., Schuh, R., Seifert, E., & Lehmann, R. (1986) · Nature · doi:10.1038/324668a0 · Cited by 163
Mutually repressive interactions between the gap genes giant and Krüppel define middle body regions of the Drosophila embryo
Kraut, R. S., & Levine, M. (1991) · Development · doi:10.1242/dev.111.2.611 · Cited by 152
Spatial regulation of the gap gene giant during Drosophila development
Kraut, R. S., & Levine, M. (1991) · Development · doi:10.1242/dev.111.2.601 · Cited by 149
Interactions of the Drosophila gap gene giant with maternal and zygotic pattern-forming genes
Eldon, E. D., & Pirrotta, V. (1991) · Development · doi:10.1242/dev.111.2.367 · Cited by 142
DNA regions bound at low occupancy by transcription factors do not drive patterned reporter gene expression in Drosophila
Fisher, W. W., Li, J. J., Hammonds, A. S., Brown, J. B., Pfeiffer, B. D., Weiszmann, R., MacArthur, S., Thomas, S., Stamatoyannopoulos, J. A., Eisen, M. B., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1209589110 · Cited by 140
Self-citation
Sequence Analysis Using Logic Regression
Kooperberg, C., Ruczinski, I., LeBlanc, M. L., & Hsu, L. (2001) · Genetic Epidemiology · doi:10.1002/gepi.2001.21.s1.s626 · Cited by 138
Zelda Potentiates Morphogen Activity by Increasing Chromatin Accessibility
Foo, S. M., Sun, Y., Lim, B., Ziukaite, R., O’Brien, K. M., Nien, C.-Y., Kirov, N., Shvartsman, S. Y., & Rushlow, C. A. (2014) · Current Biology · doi:10.1016/j.cub.2014.04.032 · Cited by 134
eFORGE: A Tool for Identifying Cell Type-Specific Signal in Epigenomic Data
Breeze, C. E., Paul, D. S., van Dongen, J., Butcher, L. M., Ambrose, J. C., Barrett, J. J. E., Lowe, R., Rakyan, V. K., Iotchkova, V., Frontini, M., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.10.059 · Cited by 125
cis‐acting control elements for Krüppel expression in the Drosophila embryo.
Hoch, M., Schröder, C., Seifert, E., & Jäckle, H. (1990) · The EMBO Journal · doi:10.1002/j.1460-2075.1990.tb07440.x · Cited by 117
Stability
Yu, B. (2013) · Bernoulli · doi:10.3150/13-bejsp14 · Cited by 113
Self-citation
Node harvest
Meinshausen, N. (2010) · The Annals of Applied Statistics · doi:10.1214/10-aoas367 · Cited by 107
Estimation Stability With Cross-Validation (ESCV)
Lim, C., & Yu, B. (2015) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2015.1020159 · Cited by 106
Self-citation
Gene expression mediated by cis‐acting sequences of the Krüppel gene in response to the Drosophila morphogens bicoid and hunchback.
Hoch, M., Seifert, E., & Jäckle, H. (1991) · The EMBO Journal · doi:10.1002/j.1460-2075.1991.tb07763.x · Cited by 105
The giant gene of Drosophila encodes a b-ZIP DNA-binding protein that regulates the expression of other segmentation gap genes
Capovilla, M., Eldon, E. D., & Pirrotta, V. (1992) · Development · doi:10.1242/dev.114.1.99 · Cited by 105
Impacts of the ubiquitous factor Zelda on Bicoid-dependent DNA binding and transcription in DrosophilaPreprint
Xu, Z., Chen, H., Jia, L., Yu, D., Struffi, P., & Small, S. (2014) · Genes & Development · doi:10.1101/gad.234534.113 · Cited by 98
Co-ChIP enables genome-wide mapping of histone mark co-occurrence at single-molecule resolution
Weiner, A., Lara‐Astiaso, D., Krupalnik, V., Gafni, O., David, E., Winter, D. R., Hanna, J. H., & Amit, I. (2016) · Nature Biotechnology · doi:10.1038/nbt.3652 · Cited by 95
Systematic image‐driven analysis of the spatial Drosophila embryonic expression landscape
Frise, E., Hammonds, A. S., & Celniker, S. (2010) · Molecular Systems Biology · doi:10.1038/msb.2009.102 · Cited by 93
Intron-centric estimation of alternative splicing from RNA-seq data
Pervouchine, D. D., Knowles, D. G., & Guigó, R. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts678 · Cited by 89
Determination of gene expression patterns using high-throughput RNA in situ hybridization to whole-mount Drosophila embryos
Weiszmann, R., Hammonds, A. S., & Celniker, S. (2009) · Nature Protocols · doi:10.1038/nprot.2009.55 · Cited by 88
Autonomous concentration-dependent activation and repression of Krüppel by hunchback in the Drosophila embryo
Schulz, C., & Tautz, D. (1994) · Development · doi:10.1242/dev.120.10.3043 · Cited by 86
A U1 snRNP–specific assembly pathway reveals the SMN complex as a versatile hub for RNP exchange
So, B. R., Wan, L., Zhang, Z., Li, P., Babiash, E. S., Duan, J., Younis, I., & Dreyfuss, G. (2016) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3167 · Cited by 82
Drosophila mef2Expression during Mesoderm Development Is Controlled by a Complex Array ofcis-Acting Regulatory Modules
Nguyen, H. T., & Xu, X. (1998) · Developmental Biology · doi:10.1006/dbio.1998.9081 · Cited by 75
A balanced iterative random forest for gene selection from microarray data
Anaissi, A., Kennedy, P. J., Goyal, M. L., & Catchpoole, D. R. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-261 · Cited by 75
A Broad Set of Chromatin Factors Influences Splicing
Allemand, E., Myers, M. P., Garcia‐Bernardo, J., Harel‐Bellan, A., Krainer, A. R., & Muchardt, C. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006318 · Cited by 49
Processing the H3K36me3 signature
Sims, R. J., & Reinberg, D. (2009) · Nature Genetics · doi:10.1038/ng0309-270 · Cited by 37
Random Intersection TreesPreprint
Shah, R. D., & Meinshausen, N. (2013) · arXiv (Cornell University) · doi:10.5555/2627435.2627455 · Cited by 37
ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data
Lundberg, S., Tu, W. B., Raught, B., Penn, L. Z., Hoffman, M. M., & Lee, S. (2016) · Genome biology · doi:10.1186/s13059-016-0925-0 · Cited by 36
Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data
Yoshida, K., Yoshimoto, J., & Doya, K. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1543-x · Cited by 35
Extensive cross-regulation of post-transcriptional regulatory networks in DrosophilaPreprint
Stoiber, M. H., Olson, S., May, G. E., Duff, M. O., Manent, J., Obar, R. A., Guruharsha, K. G., Bickel, P. J., Artavanis‐Tsakonas, S., Brown, J. B., et al. (2015) · Genome Research · doi:10.1101/gr.182675.114 · Cited by 28
Self-citation
Global Quantitative Modeling of Chromatin Factor Interactions
Zhou, J., & Troyanskaya, O. G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003525 · Cited by 27
CTCF: from insulators to alternative splicing regulation
Kornblihtt, A. R. (2012) · Cell Research · doi:10.1038/cr.2012.22 · Cited by 24
Forest Garrote
Meinshausen, N. (2009) · Electronic Journal of Statistics · doi:10.1214/09-ejs434 · Cited by 12
Computing away the magic?
Levine, M. (2013) · eLife · doi:10.7554/elife.01135 · Cited by 8
Superheat: Supervised heatmaps for visualizing complex dataPreprint
Barter, R., & Yu, B. (2015) · arXiv (Cornell University) · Cited by 6
Self-citation
Letters to the Editors
Drachenberg, C. B., Bourquin, P. M., Cochran, L. M., Burke, K. C., Kumar, D., White, C. S., Papadimitriou, J. C., Laforga, J. B. M., López, J. I., Ng, K., et al. (1998) · Acta Cytologica · doi:10.1159/000331955 · Cited by 5

Research works printed but not linked: 1

RS Hallmann, LG Schneeweiss, E Correa, J Zamora, Fine needle aspiration biopsy of thymic carcinoid tumor: A case with immunocytochemical correlation. Acta Cytol 42, 1042–1043 (1998).

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Authors: Basu, S., Kumbier, K., Brown, J. B., & Yu, B.
Affiliations: Cornell University (US)
University of California, Berkeley (US)
Lawrence Berkeley National Laboratory (US)
Corresponding author: Brown, James B.
Yu, Bin
ORCID: Basu, Sumanta
Kumbier, Karl
Brown, James B.
Yu, Bin
Venue: Proceedings of the National Academy of Sciences
Publisher: National Academy of Sciences
Published: 2018-01-19
Volume: 115
Issue: 8
Pages: 1943-1948
ISSN: 0027-8424 (ISSN-L)
1091-6490
DOI: 10.1073/pnas.1711236115
PMC: PMC5828575
OpenAlex: W2731142262
Cited by: 317
References: 80
Citations by year: 2026: 24 · 2025: 32 · 2024: 39 · 2023: 51 · 2022: 48 · 2021: 42 · 2020: 46 · 2019: 20 · 2018: 12
FWCI: 10.76
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
arXiv (Cornell University) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
arXiv (Cornell University)
Funders: HHS | NIH | National Human Genome Research Institute (U01HG007031, R00 HG006698)
DOD | United States Army | RDECOM | Army Research Office (W911NF1710005)
DOD | United States Navy | Office of Naval Research (N00014-16-1- 2664)
U.S. Department of Energy (DOE DE-AC02-05CH11231, DE-SC0017069)
National Science Foundation (CCF-0939370)
HHS | NIH |U.S. National Library of Medicine (T32LM012417)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: higher-order interactions, epistasis, H3K36me3, epigenetic interactions
MeSH terms: Algorithms, Animals, Drosophila, Models, Genetic, Alternative Splicing, Gene Expression Regulation, Developmental, Computational Biology, Gene Regulatory Networks, Genome-Wide Association Study

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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

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