Artificial Intelligence in Medicine
Volume 28, Issue 1 , Pages 59-74 , May 2003

Analyzing tumor gene expression profiles

  • Carsten Peterson

      Affiliations

    • Corresponding Author InformationCorresponding author. Tel.: +46-46-2229002; fax: +46-46-2229686.
    • Complex Systems Division, Department of Theoretical Physics, Lund University, Sölvegatan 14A, SE-223 62 Lund, Sweden
    web addressweb address
    • ,
  • Markus Ringnér

      Affiliations

    • Cancer Genetics Branch, National Human Genome Research Institute, National Institutes of Health, 50 South Drive MSC 8000, Bethesda, MD 20892, USA
    • Present address: Complex Systems Division, Lund University, Sweden.

Received 28 December 2001 ,Revised 23 October 2002 ,Accepted 28 October 2002.

References 

  1. Ben-Dor A, Friedman N, Yakhini Z. Class discovery in gene expression data. In: Lengauer T, Sankoff D, Istrail S, Pevzner P, Waterman M, editors. Proceedings of the 5th Annual International Conference on Computational Molecular Biology (RECOMB). New York: ACM Press; 2001.
  2. Bilke S. Shuffling yeast gene expression data, LU TP 00-18 2000, http://www.thep.lu.se/complex/publications.html.
  3. Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, et al.  Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature. 2000;406:536–540
  4. Brown MP, Grundy WN, Lin D, Cristianini N, Walsh Sugnet C, Furey TS, et al.  Knowledge-based analysis of microarray gene expression data by using support vector machines. Proc. Natl. Acad. Sci. USA. 2000;97:262–267
  5. Chen Y, Kamat V, Dougherty ER, Bittner ML, Meltzer PS, Trent JM. Ratio statistics of gene expression levels and applications to microarray data analysis. Bioinformatics. 2002;18:1207–1215
  6. Devaux F, Marc P, Jacq C. Transcriptome, transcription activators and microarrays. FEBS Lett. 2001;498:140–144
  7. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA. 1998;95:14863–14868
  8. Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics. 2000;16:906–914
  9. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537
  10. Gruvberger S, Ringnér M, Chen Y, Panavally S, Saal LH, Borg Å, et al.  Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. Cancer Res. 2001;61:5979–5984
  11. Hanley JA, McNeil BJ. The meaning and use of the area under the receiver operating characteristic (ROC) curve. Radiology. 1982;143:29–36
  12. Hedenfalk I, Ringnér M, Ben-Dor A, Yakhini Z, Chen Y, Chebil G, et al. Molecular classification of familial non-BRCA1/2 breast cancer. Proc Natl Acad Sci USA 2003;100:2532–7.
  13. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature 2001;409:860–921.
  14. Kerr MK, Churchill GA. Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc. Natl. Acad. Sci. USA. 2001;98:8961–8965
  15. Khan J, Simon R, Bittner M, Chen Y, Leighton SB, Pohida T, et al.  Gene expression profiling of alveolar rhabdomyosarcoma with cDNA microarrays. Cancer Res. 1998;58:5009–5013
  16. Khan J, Wei JS, Ringnér M, Saal LH, Ladanyi M, Westermann F, et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 2001;7:673–679
  17. Kirkpatrick S, Gelatt C, Vecchi M. Optimization by simulated annealing. Science. 1983;220:671–680
  18. Lisboa PJG, Mehridehnavi AR. Sensitivity methods for variable selection using the MLP. In: Proceedings of the International Workshop for Neural Networks for Identification, Control, Robotics and Signal Processing, Venice. IEEE-Computer Society Press, 1996.
  19. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, et al.  Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat. Biotechnol. 1996;14:1675–1680
  20. Perou CM, Sørlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al.  Molecular portraits of human breast tumours. Nature. 2000;406:747–752
  21. Pinkel D, Segraves R, Sudar D, Clark S, Poole I, Kowbel D, et al.  High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat. Genet. 1998;20:207–211
  22. Quackenbush J. Computational analysis of microarray data. Nat. Rev. Genet. 2001;2:418–427
  23. Ramaswamy S, Tamayo P, Rifkin R, Mukherjee S, Yeang C-H, Angelo M, et al.  Multiclass cancer diagnosis using tumor gene expression signatures. Proc. Natl. Acad. Sci. USA. 2001;98:15149–15154
  24. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270:467–470
  25. Su AI, Welsh JB, Sapinoso LM, Kern SG, Dimitrov P, Lapp H, et al.  Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res. 2001;61:7388–7393
  26. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, et al.  Interpreting patterns of gene expression with self-organizing maps: methods and applications to hematopoietic differentiation. Proc. Natl. Acad. Sci. USA. 1999;96:2907–2912
  27. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM. Systematic determination of genetic network architecture. Nat. Genet. 1999;22:281–285
  28. Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, Tibshirani R, et al.  Missing value estimation methods for DNA microarrays. Bioinformatics. 2001;17:520–525

PII: S0933-3657(03)00035-6

doi: 10.1016/S0933-3657(03)00035-6

Artificial Intelligence in Medicine
Volume 28, Issue 1 , Pages 59-74 , May 2003

Article Tools

* Image Usage & Resolution