The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power
Open accessAbstract
Complexity is the grand challenge for science and engineering in the 21st century. We suggest that biology is a discipline that is uniquely situated to tackle complexity, through a diverse array of technologies for characterizing molecular structure, interactions and function. A major difficulty in the analysis of complex biological systems is dealing with the low signal-to-noise inherent to nearly all large-scale biological data sets. We discuss powerful bioinformatic concepts for boosting signal-to-noise through external knowledge incorporated in processing units we call Filters and Integrators. These concepts are illustrated in four landmark studies that have provided model implementations of Filters, Integrators, or both.
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
Sequencing technologies – the next generation
Metzker, M. L. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2626 · Cited by 7,066
From molecular to modular cell biology.
Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999) · Nature · doi:10.1038/35011540 · Cited by 3,632
International network of cancer genome projects
The International Cancer Genome Consortium (2010) · Nature · doi:10.1038/nature08987 · Cited by 2,299
Discovering regulatory and signalling circuits in molecular interaction networks
Ideker, T., Ozier, O., Schwikowski, B., & Siegel, A. F. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s233 · Cited by 1,197
Self-citationAnalysis of Genetic Inheritance in a Family Quartet by Whole-Genome Sequencing
Roach, J. C., Glusman, G., Smit, A. F. A., Huff, C. D., Hubley, R., Shannon, P. T., Rowen, L., Pant, K. P., Goodman, N., Bamshad, M., et al. (2010) · Science · doi:10.1126/science.1186802 · Cited by 1,084
Self-citationSegrè, A. V., DIAGRAM Consortium, MAGIC investigators, Groop, L., Mootha, V. K., Daly, M. J., & Altshuler, D. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001058 · Cited by 463
The evolution of hierarchical gene regulatory networks
Erwin, D. H., & Davidson, E. H. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2499 · Cited by 454
A systems approach to prion disease
Hwang, D., Lee, I. Y., Yoo, H., Gehlenborg, N., Cho, J., Petritis, B., Baxter, D., Pitstick, R., Young, R., Spicer, D., et al. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.10 · Cited by 287
Self-citation
Cited by
The BioGRID interaction database: 2013 update
Chatr-aryamontri, A., Breitkreutz, B.-J., Heinicke, S., Boucher, L., Winter, A., Stark, C., Nixon, J., Ramage, L., Kolas, N., O’Donnell, L., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1158 · Cited by 1,017
Liu, Z.-P., Wu, C., Miao, H., & Wu, H. (2015) · Database · doi:10.1093/database/bav095 · Cited by 555
Promise of personalized omics to precision medicine
Chen, R., & Snyder, M. (2013) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1198 · Cited by 323
Making sense of big data in health research: towards an EU action plan
Auffray, C., Balling, R., Barroso, I., Bencze, L., Benson, M., Bergeron, J., Bernal-Delgado, E., Blomberg, N., Bock, C., Conesa, A., et al. (2016) · Genome Medicine · doi:10.1186/s13073-016-0323-y · Cited by 286
Tian, Q., Price, N. D., & Hood, L. (2012) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2011.02498.x · Cited by 224
Self-citation
Metadata
- Authors
- Ideker, T., Dutkowski, J., & Hood, L.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2011-03
- Volume
- 144
- Issue
- 6
- Pages
- 860-863
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W1975387345
- Cited by
- 205
- References
- 8
- FWCI
- 9.52
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power. https://app.cochat.ai/papers/10.1016/j.cell.2011.03.007
- The paper
- Ideker, T., Dutkowski, J., & Hood, L. (2011). Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power. Cell. https://doi.org/10.1016/j.cell.2011.03.007
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.
