The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
An Efficient Semi-Unsupervised Gene Selection Method via Spectral Biclustering
Abstract
Gene selection is an important issue in microarray data processing. In this paper, we propose an efficient method for selecting relevant genes. First, we use spectral biclustering to obtain the best two eigenvectors for class partition. Then gene combinations are selected based on the similarity between the genes and the best eigenvectors. We demonstrate our semi-unsupervised gene selection method using two microarray cancer data sets, i.e., the lymphoma and the liver cancer data sets, where our method is able to identify a single gene or a two-gene combinations which can lead to predictions with very high accuracy.
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
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Cited by
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection
Ang, J. C., Mirzal, A., Haron, H., & Hamed, H. N. A. (2016) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2015.2478454 · Cited by 641
Feature selection methods for big data bioinformatics: A survey from the search perspective
Wang, L., Wang, Y., & Chang, Q. (2016) · Methods · doi:10.1016/j.ymeth.2016.08.014 · Cited by 331
Self-citation
Metadata
- Authors
- Liu, B., Wan, C., & Wang, L.
- Venue
- IEEE Transactions on Nanobioscience
- Published
- 2006-06
- Volume
- 5
- Issue
- 2
- Pages
- 110-114
- ISSN
- 1536-1241 (ISSN-L)
- OpenAlex
- W2097169398
- Cited by
- 188
- References
- 1
- FWCI
- 4.17
- 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: An Efficient Semi-Unsupervised Gene Selection Method via Spectral Biclustering. https://app.cochat.ai/papers/10.1109/tnb.2006.875040
- The paper
- Liu, B., Wan, C., & Wang, L. (2006). An Efficient Semi-Unsupervised Gene Selection Method via Spectral Biclustering. IEEE Transactions on Nanobioscience. https://doi.org/10.1109/tnb.2006.875040
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.
