Original scientific paper
Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction
Željko Debeljak
Full text: croatian pdf 266 Kb
page 150-162
downloads: 600
cite
APA 6th Edition
Debeljak, Ž. (2006). Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction. Biochemia Medica, 16 (2), 150-162. Retrieved from https://hrcak.srce.hr/9648
MLA 8th Edition
Debeljak, Željko. "Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction." Biochemia Medica, vol. 16, no. 2, 2006, pp. 150-162. https://hrcak.srce.hr/9648. Accessed 24 Nov. 2024.
Chicago 17th Edition
Debeljak, Željko. "Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction." Biochemia Medica 16, no. 2 (2006): 150-162. https://hrcak.srce.hr/9648
Harvard
Debeljak, Ž. (2006). 'Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction', Biochemia Medica, 16(2), pp. 150-162. Available at: https://hrcak.srce.hr/9648 (Accessed 24 November 2024)
Vancouver
Debeljak Ž. Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction. Biochemia Medica [Internet]. 2006 [cited 2024 November 24];16(2):150-162. Available from: https://hrcak.srce.hr/9648
IEEE
Ž. Debeljak, "Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction", Biochemia Medica, vol.16, no. 2, pp. 150-162, 2006. [Online]. Available: https://hrcak.srce.hr/9648. [Accessed: 24 November 2024]
Full text: english pdf 266 Kb
page 150-162
downloads: 353
cite
APA 6th Edition
Debeljak, Ž. (2006). Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction. Biochemia Medica, 16 (2), 150-162. Retrieved from https://hrcak.srce.hr/9648
MLA 8th Edition
Debeljak, Željko. "Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction." Biochemia Medica, vol. 16, no. 2, 2006, pp. 150-162. https://hrcak.srce.hr/9648. Accessed 24 Nov. 2024.
Chicago 17th Edition
Debeljak, Željko. "Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction." Biochemia Medica 16, no. 2 (2006): 150-162. https://hrcak.srce.hr/9648
Harvard
Debeljak, Ž. (2006). 'Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction', Biochemia Medica, 16(2), pp. 150-162. Available at: https://hrcak.srce.hr/9648 (Accessed 24 November 2024)
Vancouver
Debeljak Ž. Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction. Biochemia Medica [Internet]. 2006 [cited 2024 November 24];16(2):150-162. Available from: https://hrcak.srce.hr/9648
IEEE
Ž. Debeljak, "Bridging the gap between microarray technology and routine clinical diagnostics: a Random Forest approach to the gene expression profile dimensionality reduction", Biochemia Medica, vol.16, no. 2, pp. 150-162, 2006. [Online]. Available: https://hrcak.srce.hr/9648. [Accessed: 24 November 2024]
Abstract
ntroduction: Although recognized as a valuable tool by scientific community, microarray based gene expression profiling has not accessed routine diagnostic application during the last decade. Since this approach is expensive and prone to substantial experimental variation, it is not suited for routine clinical diagnostic purposes at the current state of technology. In order to bridge that gap, different computational dimensionality reduction tools have been developed. The principle of their application is selection of a limited set of biomarker candidates from huge gene expression profiles appropriate for routine diagnostic assessment.
Aim: Random forest (RF) has been established as a reliable predictor. However, its relevant gene selection capabilities gained less attention. The aim of this study was to evaluate suitability of RF for biomarker selection from gene expression profile datasets. Three datasets taken from literature, obtained during small-scale clinical experiments, were chosen for that purpose.
Results: The results obtained show that RF could easily identify good uni-variate classifiers, i.e. single biomarkers when the problem at hand is of low complexity. For more complex problem a reliable two-dimensional classifier candidate could be also found by this approach. However, when the relationship between diagnosis/prognosis and gene expression profiling results are highly complex or the dataset is too small, RF-based dimensionality reduction fails to select a reliable set of biomarker candidates.
Conclusions: Within dataset complexity limitations, RF represents an appropriate tool for biomarker candidate selection.
Keywords
gene expression; microarray; biomarker screening; random forests; feature selection
Hrčak ID:
9648
URI
https://hrcak.srce.hr/9648
Publication date:
20.12.2006.
Article data in other languages:
croatian
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