The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Inferring network mechanisms: The Drosophila melanogaster protein interaction network
Open accessAbstract
Naturally occurring networks exhibit quantitative features revealing underlying growth mechanisms. Numerous network mechanisms have recently been proposed to reproduce specific properties such as degree distributions or clustering coefficients. We present a method for inferring the mechanism most accurately capturing a given network topology, exploiting discriminative tools from machine learning. The Drosophila melanogaster protein network is confidently and robustly (to noise and training data subsampling) classified as a duplication–mutation–complementation network over preferential attachment, small-world, and a duplication–mutation mechanism without complementation. Systematic classification, rather than statistical study of specific properties, provides a discriminative approach to understand the design of complex networks.
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
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Social Network Analysis: Methods and Applications
Wasserman, S., & Faust, K. (1994) · doi:10.1017/cbo9780511815478 · Cited by 17,557
Network motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
A Probabilistic Theory of Pattern Recognition
Devroye, L., Györfi, L., & Lugosi, G. (1996) · Stochastic Modelling and Applied Probability · doi:10.1007/978-1-4612-0711-5 · Cited by 3,274
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
Wang, W., Yu, H., & Long, M. (2004) · Nature Genetics · doi:10.1038/ng1338 · Cited by 114
Cited by
Evaluation of clustering algorithms for protein-protein interaction networks
Brohée, S., & van Helden, J. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-488 · Cited by 892
Broido, A. D., & Clauset, A. (2019) · Nature Communications · doi:10.1038/s41467-019-08746-5 · Cited by 875
A Survey of Statistical Network Models
Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2010) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000005 · Cited by 819
Interactome: gateway into systems biology
Cusick, M. E., Klitgord, N., Vidal, M., & Hill, D. E. (2005) · Human Molecular Genetics · doi:10.1093/hmg/ddi335 · Cited by 414
Generative models of the human connectome
Betzel, R. F., Avena-Koenigsberger, A., Goñi, J., He, Y., de Reus, M. A., Griffa, A., Vértes, P. E., Mišic, B., Thiran, J.-P., Hagmann, P., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.041 · Cited by 303
Metadata
- Authors
- Middendorf, M., Ziv, E., & Wiggins, C. H.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2005-02-22
- Volume
- 102
- Issue
- 9
- Pages
- 3192-3197
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2131984349
- Cited by
- 196
- References
- 7
- FWCI
- 10.36
- Type
- journal article
- Language
- en
- Open access
- green
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: Inferring network mechanisms: The Drosophila melanogaster protein interaction network. https://app.cochat.ai/papers/10.1073/pnas.0409515102
- The paper
- Middendorf, M., Ziv, E., & Wiggins, C. H. (2005). Inferring network mechanisms: The Drosophila melanogaster protein interaction network. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0409515102
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.