The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Predicting Gene Expression from Sequence
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Genomic Expression Programs in the Response of Yeast Cells to Environmental Changes
Gasch, A. P., Spellman, P. T., Kao, C. M., Carmel-Harel, O., Eisen, M. B., Storz, G., Botstein, D., & Brown, P. O. (2000) · Molecular Biology of the Cell · doi:10.1091/mbc.11.12.4241 · Cited by 4,815
Transcriptional Regulatory Networks in Saccharomyces cerevisiae
Lee, T. I., Rinaldi, N. J., Robert, F., Odom, D. T., Bar-Joseph, Z., Gerber, G. K., Hannett, N. M., Harbison, C. T., Thompson, C. M., Simon, I., et al. (2002) · Science · doi:10.1126/science.1075090 · Cited by 2,956
Transcription regulation and animal diversity
Levine, M., & Tjian, R. (2003) · Nature · doi:10.1038/nature01763 · Cited by 1,309
Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling
Gardner, T. S., di Bernardo, D., Lorenz, D., & Collins, J. J. (2003) · Science · doi:10.1126/science.1081900 · Cited by 1,117
Genome-wide binding map of the histone deacetylase Rpd3 in yeast
Kurdistani, S. K., Robyr, D., Tavazoie, S., & Grunstein, M. (2002) · Nature Genetics · doi:10.1038/ng907 · Cited by 279
Self-citation
Cited by
Network motifs: theory and experimental approaches
Alon, U. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2102 · Cited by 3,279
Faith, J. J., Hayete, B., Thaden, J. T., Mogno, I., Wierzbowski, J., Cottarel, G., Kasif, S., Collins, J. J., & Gardner, T. S. (2007) · PLoS Biology · doi:10.1371/journal.pbio.0050008 · Cited by 1,563
Kelley, D. R., Snoek, J., & Rinn, J. L. (2016) · Genome Research · doi:10.1101/gr.200535.115 · Cited by 1,048
Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D., & Stuart, J. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq182 · Cited by 786
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
Bonn, S., Zinzen, R. P., Girardot, C., Gustafson, E. H., Perez-Gonzalez, A., Delhomme, N., Ghavi-Helm, Y., Wilczyński, B., Riddell, A., & Furlong, E. E. M. (2012) · Nature Genetics · doi:10.1038/ng.1064 · Cited by 502
The FANTOM Consortium, & Riken Omics Science Center (2009) · Nature Genetics · doi:10.1038/ng.375 · Cited by 427
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
DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of enhancers
de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2021) · doi:10.1101/2021.10.05.463203 · Cited by 284
de Almeida, B. P., Reiter, F., Pagani, M., & Stark, A. (2022) · Nature Genetics · doi:10.1038/s41588-022-01048-5 · Cited by 283
Systematic discovery of structural elements governing stability of mammalian messenger RNAs
Goodarzi, H., Najafabadi, H. S., Oikonomou, P., Greco, T. M., Fish, L., Salavati, R., Cristea, I. M., & Tavazoie, S. (2012) · Nature · doi:10.1038/nature11013 · Cited by 187
Self-citationMachine learning: a powerful tool for gene function prediction in plants
Mahood, E. H., Kruse, L. H., & Moghe, G. D. (2020) · Applications in Plant Sciences · doi:10.1002/aps3.11376 · Cited by 153
Widespread use of poly(A) tail length control to accentuate expression of the yeast transcriptome
Beilharz, T. H., & Preiss, T. (2007) · RNA · doi:10.1261/rna.569407 · Cited by 144
Hold out the genome: a roadmap to solving the cis-regulatory code
de Boer, C. G., & Taipale, J. (2024) · Nature · doi:10.1038/s41586-023-06661-w · Cited by 107
Cis -regulatory elements explain most of the mRNA stability variation across genes in yeast
Cheng, J., Maier, K. C., Avsec, Ž., Rus, P., & Gagneur, J. (2017) · RNA · doi:10.1261/rna.062224.117 · Cited by 79
Li, L., Zhu, Q., He, X., Sinha, S., & Halfon, M. S. (2007) · Genome Biology · doi:10.1186/gb-2007-8-6-r101 · Cited by 69
A relationship between mRNA expression levels and protein solubility in E. coli
Tartaglia, G. G., Pechmann, S., Dobson, C. M., & Vendruscolo, M. (2009) · Journal of Molecular Biology · doi:10.1016/j.jmb.2009.03.002 · Cited by 63
Transcriptional regulation of protein complexes within and across species
Tan, K., Shlomi, T., Feizi, H., Ideker, T., & Sharan, R. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606914104 · Cited by 58
Size matters: network inference tackles the genome scale
Hayete, B., Gardner, T. S., & Collins, J. J. (2007) · Molecular Systems Biology · doi:10.1038/msb4100118 · Cited by 33
Gong, W., Koyano-Nakagawa, N., Li, T., & Garry, D. J. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0460-0 · Cited by 26
A signature of Neanderthal introgression on molecular mechanisms of environmental responses
Findley, A. S., Zhang, X., Boye, C., Lin, Y. L., Kalita, C. A., Barreiro, L., Lohmueller, K. E., Pique-Regi, R., & Luca, F. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009493 · Cited by 14
Interpreting noncoding genetic variation in complex traits and human disease
Ward, L. D., & Kellis, M. (2012) · Nature Biotechnology · doi:10.1038/nbt.2422 · Cited by 6
Cis-regulatory elements explain most of the mRNA stability variation across genes in yeast
Cheng, J., Maier, K. C., Avsec, Ž., Rus, P., & Gagneur, J. (2016) · doi:10.1101/085522 · Cited by 1
Bhogale, S., Seward, C., Stubbs, L., & Sinha, S. (2023) · doi:10.1101/2023.11.09.565900 · Cited by 1
Metadata
- Authors
- Beer, M. A., & Tavazoie, S.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2004-04
- Volume
- 117
- Issue
- 2
- Pages
- 185-198
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W2073727170
- Cited by
- 599
- References
- 7
- FWCI
- 20.99
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
