Regulatory element detection using correlation with expression

Cluster analysis and display of genome-wide expression patterns

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The transcriptional network for mesenchymal transformation of brain tumours

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Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

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iRegulon: From a Gene List to a Gene Regulatory Network Using Large Motif and Track Collections

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Transcriptome-wide Analysis of Regulatory Interactions of the RNA-Binding Protein HuR

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Discovering motifs in ranked lists of DNA sequences

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ERRα and Gabpa/b specify PGC-1α-dependent oxidative phosphorylation gene expression that is altered in diabetic muscle.

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Cell-type-specific signatures of microRNAs on target mRNA expression

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Network component analysis: Reconstruction of regulatory signals in biological systems

Liao, J. C., Boscolo, R., Yang, Y.-L., Tran, L. M., Sabatti, C., & Roychowdhury, V. P. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2136632100 · Cited by 601

Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk

Zhou, J., Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., & Troyanskaya, O. G. (2018) · Nature Genetics · doi:10.1038/s41588-018-0160-6 · Cited by 559

Molecular taxonomy of major neuronal classes in the adult mouse forebrain

Sugino, K., Hempel, C. M., Miller, M. N., Hattox, A. M., Shapiro, P., Wu, C., Huang, Z. J., & Nelson, S. B. (2006) · Nature Neuroscience · doi:10.1038/nn1618 · Cited by 521

Evolution of Transcription Factor Binding Sites in Mammalian Gene Regulatory Regions: Conservation and Turnover

Dermitzakis, E. T., & Clark, A. G. (2002) · Molecular Biology and Evolution · doi:10.1093/oxfordjournals.molbev.a004169 · Cited by 463

Use of Chromatin Immunoprecipitation To Clone Novel E2F Target Promoters

Weinmann, A. S., Bartley, S. M., Zhang, T., Zhang, M. Q., & Farnham, P. J. (2001) · Molecular and Cellular Biology · doi:10.1128/mcb.21.20.6820-6832.2001 · Cited by 384

Integrating regulatory motif discovery and genome-wide expression analysis

Conlon, E. M., Liu, X. S., Lieb, J. D., & Liu, J. S. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0630591100 · Cited by 343

ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells

Ouyang, Z., Zhou, Q., & Wong, W. H. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0904863106 · Cited by 339

Analysis of intronic and exonic reads in RNA-seq data characterizes transcriptional and post-transcriptional regulation

Gaidatzis, D., Burger, L., Florescu, M., & Stadler, M. B. (2015) · Nature Biotechnology · doi:10.1038/nbt.3269 · Cited by 313

Analysis of combinatorial cis-regulation in synthetic and genomic promoters

Gertz, J., Siggia, E. D., & Cohen, B. A. (2009) · Nature · doi:10.1038/nature07521 · Cited by 304

Enhanced biclustering on expression data

Yang, J., Wang, H., Wang, W., & Yu, P. (2003) · Third IEEE Symposium on Bioinformatics and Bioengineering, 2003. Proceedings. · doi:10.1109/bibe.2003.1188969 · Cited by 299

Deciphering eukaryotic gene-regulatory logic with 100 million random promoters

de Boer, C. G., Vaishnav, E. D., Sadeh, R., Abeyta, E. L., Friedman, N., & Regev, A. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0315-8 · Cited by 283

Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks

Emmert-Streib, F., Dehmer, M., & Haibe-Kains, B. (2014) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2014.00038 · Cited by 282

Defining transcriptional networks through integrative modeling of mRNA expression and transcription factor binding data

Gao, F., Foat, B. C., & Bussemaker, H. J. (2004) · BMC Bioinformatics · doi:10.1186/1471-2105-5-31 · Cited by 247

Expression-based monitoring of transcription factor activity: the TELiS database

Cole, S. W., Yan, W., Galic, Z., Arevalo, J., & Zack, J. A. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti038 · Cited by 185

Transcriptional Response of Saccharomyces cerevisiae to the Plasma Membrane-Perturbing Compound Chitosan

Zakrzewska, A., Boorsma, A., Brul, S., Hellingwerf, K. J., & Klis, F. M. (2005) · Eukaryotic Cell · doi:10.1128/ec.4.4.703-715.2005 · Cited by 161

Characterization of the transcriptional response to cell wall stress in Saccharomyces cerevisiae

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The Arabidopsis genome: A foundation for plant research

Bevan, M., & Walsh, S. (2005) · Genome Research · doi:10.1101/gr.3723405 · Cited by 153

Quantification of Differential Transcription Factor Activity and Multiomics-Based Classification into Activators and Repressors: diffTF

Berest, I., Arnold, C., Reyes-Palomares, A., Palla, G., Rasmussen, K. D., Giles, H., Bruch, P.-M., Huber, W., Dietrich, S., Helin, K., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.106 · Cited by 119

Quantitative analysis of binding motifs mediating diverse spatial readouts of the Dorsal gradient in the Drosophila embryo

Papatsenko, D., & Levine, M. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0409414102 · Cited by 82

Computational methods for the detection of cis-regulatory modules

Van Loo, P., & Marynen, P. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp025 · Cited by 62

Integrative analysis reveals regulatory programs in endometriosis

Yang, H., Kang, K., Cheng, C., Mamillapalli, R., & Taylor, H. S. (2015) · Reproductive Sciences · doi:10.1177/1933719115592709 · Cited by 39

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction

Chandrashekar, P. B., Alatkar, S., Wang, J., Hoffman, G. E., He, C., Jin, T., Khullar, S., Bendl, J., Fullard, J. F., Roussos, P., et al. (2023) · Genome Medicine · doi:10.1186/s13073-023-01248-6 · Cited by 37