The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Discovering molecular pathways from protein interaction and gene expression data
Abstract
In this paper, we describe an approach for identifying‘ pathways’ from gene expression and protein interaction data. Our approach is based on the assumption that many pathways exhibit two properties: their genes exhibit a similar gene expression profile, and the protein products of the genes often interact. Our approach is based on a unified probabilistic model, which is learned from the data using the EM algorithm. We present results on two Saccharomyces cerevisiae gene expression data sets, combined with a binary protein interaction data set. Our results show that our approach is much more successful than other approaches at discovering both coherent functional groups and entire protein complexes. Contact: eran@cs.stanford.edu Keywords: probabilistic models, protein interaction, gene expression. *To whom correspondence should be addressed.
Overview
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
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
Cited by
Integrative approaches for finding modular structure in biological networks
Mitra, K., Carvunis, A.-R., Ramesh, S. K., & Ideker, T. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3552 · Cited by 535
Classification in Networked Data: A Toolkit and a Univariate Case Study
(2007) · Journal of Machine Learning Research · doi:10.5555/1248659.1248693 · Cited by 452
Lin, G. N., Corominas, R., Lemmens, I., Yang, X., Tavernier, J., Hill, D. E., Vidal, M., Sebat, J., & Iakoucheva, L. M. (2015) · Neuron · doi:10.1016/j.neuron.2015.01.010 · Cited by 155
VisANT 3.0: new modules for pathway visualization, editing, prediction and construction
Hu, Z., Ng, D. M., Yamada, T., Chen, C., Kawashima, S., Mellor, J., Linghu, B., Kanehisa, M., Stuart, J. M., & DeLisi, C. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm295 · Cited by 76
HyBrow: a prototype system for computer-aided hypothesis evaluation
Racunas, S. A., Shah, N. H., Albert, I., & Fedoroff, N. V. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth905 · Cited by 70
Metadata
- Authors
- Segal, E., Wang, H., & Koller, D.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-07-03
- Volume
- 19
- Issue
- suppl_1
- Pages
- i264-i272
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2136549265
- Cited by
- 302
- References
- 2
- FWCI
- 7.29
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Discovering molecular pathways from protein interaction and gene expression data. https://app.cochat.ai/papers/10.1093/bioinformatics/btg1037
- The paper
- Segal, E., Wang, H., & Koller, D. (2003). Discovering molecular pathways from protein interaction and gene expression data. Bioinformatics. https://doi.org/10.1093/bioinformatics/btg1037
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.
