Literature DB >> 14962943

Application of the GA/KNN method to SELDI proteomics data.

Leping Li1, David M Umbach, Paul Terry, Jack A Taylor.   

Abstract

SUMMARY: Proteomics technology has shown promise in identifying biomarkers for disease, toxicant exposure and stress. We show by example that the genetic algorithm/k-nearest neighbors method, developed for mining high-dimensional microarray gene expression data, is also capable of mining surface enhanced laser desorption/ionization-time-of-flight proteomics data. AVAILABILITY: The source code of the program and documentation on how to use it are freely available to non-commercial users at http://dir.niehs.nih.gov/dirbb/lifiles/softlic.htm

Mesh:

Substances:

Year:  2004        PMID: 14962943     DOI: 10.1093/bioinformatics/bth098

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  12 in total

1.  Toward predicting metastatic progression of melanoma based on gene expression data.

Authors:  Yuanyuan Li; Juno M Krahn; Gordon P Flake; David M Umbach; Leping Li
Journal:  Pigment Cell Melanoma Res       Date:  2015-04-24       Impact factor: 4.693

2.  Comparison of feature selection and classification for MALDI-MS data.

Authors:  Qingzhong Liu; Andrew H Sung; Mengyu Qiao; Zhongxue Chen; Jack Y Yang; Mary Qu Yang; Xudong Huang; Youping Deng
Journal:  BMC Genomics       Date:  2009-07-07       Impact factor: 3.969

3.  Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data.

Authors:  Kai-Lin Tang; Tong-Hua Li; Wen-Wei Xiong; Kai Chen
Journal:  BMC Bioinformatics       Date:  2010-02-27       Impact factor: 3.169

4.  Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm.

Authors:  Zhanchao Li; Xuan Zhou; Zong Dai; Xiaoyong Zou
Journal:  BMC Bioinformatics       Date:  2010-06-16       Impact factor: 3.169

5.  A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels.

Authors:  Ivica Kopriva; Marko Filipović
Journal:  BMC Bioinformatics       Date:  2011-12-30       Impact factor: 3.169

6.  A simple method to combine multiple molecular biomarkers for dichotomous diagnostic classification.

Authors:  Manju R Mamtani; Tushar P Thakre; Mrunal Y Kalkonde; Manik A Amin; Yogeshwar V Kalkonde; Amit P Amin; Hemant Kulkarni
Journal:  BMC Bioinformatics       Date:  2006-10-10       Impact factor: 3.169

7.  A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer.

Authors:  Jiang Wu; Yanju Ji; Ling Zhao; Mengying Ji; Zhuang Ye; Suyi Li
Journal:  Comput Math Methods Med       Date:  2016-08-23       Impact factor: 2.238

Review 8.  Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery.

Authors:  Anouk Suppers; Alain J van Gool; Hans J C T Wessels
Journal:  Proteomes       Date:  2018-04-26

Review 9.  Intelligence Algorithms for Protein Classification by Mass Spectrometry.

Authors:  Zichuan Fan; Fanchen Kong; Yang Zhou; Yiqing Chen; Yalan Dai
Journal:  Biomed Res Int       Date:  2018-11-11       Impact factor: 3.411

10.  PatternLab for proteomics: a tool for differential shotgun proteomics.

Authors:  Paulo C Carvalho; Juliana S G Fischer; Emily I Chen; John R Yates; Valmir C Barbosa
Journal:  BMC Bioinformatics       Date:  2008-07-21       Impact factor: 3.169

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