Selecting clinically-driven biomarkers for cancer nanotechnology.
The challenge of biomarker identification for bionanotechnology is that we need to find less than ten potential biomarkers from high throughput data so that quantum dot synthesis and imaging can be effective. Among all the extensive biomarker research, the novelty of our research is to reduce the number the biomarkers by studying the efficacy of several classifiers and error estimation methods. Specifically, we are using renal cancer expression data. The dataset consists of 31 microarray samples divided into four classes -- clear cell, oncocytoma/chromophobe, papillary, and angiomyolipoma. Each class is compared to all other classes using error estimation methods for support vector machines (SVM), Fisher's discriminant (FD), and signed distance function (SDF). Prior knowledge of significant biomarker from a previous study is used to score the effectiveness of each classifier in correctly identifying these biomarkers. We have achieved intelligent model selection for biomarker identification so that the total number of nano-imaging targets is small.
Phan
John H
JH
Dept. of Biomed. Eng., Georgia Inst. of Technol., Atlanta, GA 30332, USA.
Young
Andrew N
AN
Wang
May D
MD
D014408:Biomarkers, Tumor / Q000032:analysis* / Q000235:genetics; D001698:Biomedical Engineering; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D005819:Genetic Markers; D006801:Humans; D007680:Kidney Neoplasms / Q000737:chemistry / Q000175:diagnosis / Q000235:genetics; D036103:Nanotechnology / Q000379:methods; D009369:Neoplasms / Q000737:chemistry / Q000175:diagnosis* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D045663:Quantum Dots
D014408:Biomarkers, Tumor; D005819:Genetic Markers