Data

Gene expression analysis for tumor classification using vector quantization

Monash University
Ana María Espinosa (Aggregated by) Christian Lemaitre (Aggregated by) Edna Márquez (Aggregated by) Jaime Berumen (Aggregated by)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/03/5a137205bd04a&rft.title=Gene expression analysis for tumor classification using vector quantization&rft.identifier=https://doi.org/10.4225/03/5a137205bd04a&rft.publisher=Monash University&rft.description=Gene expression analysis is one of the most important tasks for genomic medicine, using these it is possible to classify tumors, which are directly related with the development of cancer. This paper presents a clustering method for tumor classification, vector quantization, using gene expression profiles from microarrays of mRNA with samples of cervical cancer and normal cervix. Vector quantization is used to divide the space into regions, and the centroids of the regions represent patients with tumors or healthy ones. Also the regions found by the vector quantizer are used as the base for classifying other tumors, that could help in the prognostics of the illness or for finding new groups of tumors. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.&rft.creator=Ana María Espinosa&rft.creator=Christian Lemaitre&rft.creator=Edna Márquez&rft.creator=Jaime Berumen&rft.date=2017&rft_rights=In Copyright&rft_subject=Bioinformatics -- Congresses&rft_subject=Computational biology -- Congresses&rft_subject=Computer vision in medicine -- Congresses&rft_subject=Computational biology -- Methods -- Congresses&rft_subject=Pattern recognition, automated -- Methods -- Congresses&rft_subject=Gene expression analysis&rft_subject=Clustering&rft_subject=Vector quantization&rft_subject=Tumor classification&rft_subject=2008&rft_subject=conference paper&rft_subject=1959.1/63695&rft_subject=monash:7855&rft_subject=Bioinformatics&rft_subject=Pattern Recognition and Data Mining&rft_subject=Bioinformatics Software&rft.type=dataset&rft.language=English Access the data
Cite Saved to MyRDA Save to MyRDA

Licence & Rights:

view details

In Copyright

Access:

Other

Full description

Gene expression analysis is one of the most important tasks for genomic medicine, using these it is possible to classify tumors, which are directly related with the development of cancer. This paper presents a clustering method for tumor classification, vector quantization, using gene expression profiles from microarrays of mRNA with samples of cervical cancer and normal cervix. Vector quantization is used to divide the space into regions, and the centroids of the regions represent patients with tumors or healthy ones. Also the regions found by the vector quantizer are used as the base for classifying other tumors, that could help in the prognostics of the illness or for finding new groups of tumors. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

Issued: 2017-11-21

Created: 2017-11-21

This dataset is part of a larger collection

Click to explore relationships graph
Help

Related People

Subjects
1959.1/63695 | 2008 | Bioinformatics | Bioinformatics -- Congresses | Bioinformatics Software | Clustering | Computational biology -- Congresses | Computational biology -- Methods -- Congresses | Computer vision in medicine -- Congresses | Gene expression analysis | Pattern Recognition and Data Mining | Pattern recognition, automated -- Methods -- Congresses | Tumor classification | Vector quantization | conference paper | monash:7855 |

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Identifiers
Similar data you may be interested in: