Use of SVM-based ensemble feature selection method for gene expression data analysis.

Gene selection is one of the key steps for gene expression data analysis. An SVM-based ensemble feature selection method is proposed in this paper. Firstly, the method builds many subsets by using Monte Carlo sampling. Secondly, ranking all the features on each of the subsets and integrating them to obtain a final ranking list. Finally, the optimum feature set is determined by a backward feature elimination strategy. This method is applied to the analysis of 4 public datasets: the Leukemia, Prostate, Colorectal, and SMK_CAN, resulting 7, 10, 13, and 32 features. The AUC obtained from independent test sets are 0.9867, 0.9796, 0.9571, and 0.9575, respectively. These results indicate that the features selected by the proposed method can improve sample classification accuracy, and thus be effective for gene selection from gene expression data.

A Label-Free Fluorescent DNA Calculator Based on Gold Nanoparticles for Sensitive Detection of ATP.

Herein we described a deoxyribonucleic acid (DNA) calculator for sensitive detection of the determination of adenosine triphosphate (ATP) using gold nanoparticles (GNP) and PicoGreen fluorescence dye as signal transducer, and ATP and single-stranded DNA (DNA-M') as activators. The calculator-related performances including linearity, reaction time, logic gate, and selectivity were investigated, respectively. The results revealed that this oligonucleotide sensor was highly sensitive and selective. The detection range was 50⁻500 nmol/L (R² = 0.99391) and the detection limit was 46.5 nmol/L. The AND DNA calculator was successfully used for the ATP detection in human urine. Compared with other methods, this DNA calculator has the characteristics of being label-free, non-enzymic, simple, and highly sensitive.