Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data
<p>Abstract</p> <p>Background</p> <p>Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2010-02-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/11/109 |
| _version_ | 1818773265872584704 |
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| author | Li Tong-Hua Tang Kai-Lin Xiong Wen-Wei Chen Kai |
| author_facet | Li Tong-Hua Tang Kai-Lin Xiong Wen-Wei Chen Kai |
| author_sort | Li Tong-Hua |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.</p> <p>Results</p> <p>We propose a method based on statistical moments to reduce feature dimensions. After refining and <it>t</it>-testing, SELDI-TOF data are divided into several intervals. Four statistical moments (mean, variance, skewness and kurtosis) are calculated for each interval and are used as representative variables. The high dimensionality of the data can thus be rapidly reduced. To improve efficiency and classification performance, the data are further used in kernel PLS models. The method achieved average sensitivity of 0.9950, specificity of 0.9916, accuracy of 0.9935 and a correlation coefficient of 0.9869 for 100 five-fold cross validations. Furthermore, only one control was misclassified in leave-one-out cross validation.</p> <p>Conclusion</p> <p>The proposed method is suitable for analyzing high-throughput proteomics data.</p> |
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| format | Article |
| id | doaj.art-d128abdd5c2b4b9bb141ea6fcaeecf72 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-18T10:22:31Z |
| publishDate | 2010-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-d128abdd5c2b4b9bb141ea6fcaeecf722022-12-21T21:11:05ZengBMCBMC Bioinformatics1471-21052010-02-0111110910.1186/1471-2105-11-109Ovarian cancer classification based on dimensionality reduction for SELDI-TOF dataLi Tong-HuaTang Kai-LinXiong Wen-WeiChen Kai<p>Abstract</p> <p>Background</p> <p>Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.</p> <p>Results</p> <p>We propose a method based on statistical moments to reduce feature dimensions. After refining and <it>t</it>-testing, SELDI-TOF data are divided into several intervals. Four statistical moments (mean, variance, skewness and kurtosis) are calculated for each interval and are used as representative variables. The high dimensionality of the data can thus be rapidly reduced. To improve efficiency and classification performance, the data are further used in kernel PLS models. The method achieved average sensitivity of 0.9950, specificity of 0.9916, accuracy of 0.9935 and a correlation coefficient of 0.9869 for 100 five-fold cross validations. Furthermore, only one control was misclassified in leave-one-out cross validation.</p> <p>Conclusion</p> <p>The proposed method is suitable for analyzing high-throughput proteomics data.</p>http://www.biomedcentral.com/1471-2105/11/109 |
| spellingShingle | Li Tong-Hua Tang Kai-Lin Xiong Wen-Wei Chen Kai Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data BMC Bioinformatics |
| title | Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data |
| title_full | Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data |
| title_fullStr | Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data |
| title_full_unstemmed | Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data |
| title_short | Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data |
| title_sort | ovarian cancer classification based on dimensionality reduction for seldi tof data |
| url | http://www.biomedcentral.com/1471-2105/11/109 |
| work_keys_str_mv | AT litonghua ovariancancerclassificationbasedondimensionalityreductionforselditofdata AT tangkailin ovariancancerclassificationbasedondimensionalityreductionforselditofdata AT xiongwenwei ovariancancerclassificationbasedondimensionalityreductionforselditofdata AT chenkai ovariancancerclassificationbasedondimensionalityreductionforselditofdata |