The discriminant power of RNA features for pre-miRNA recognition

Computational discovery of microRNAs (miRNA) is based on pre-determined sets of features from miRNA precursors (pre-miRNA). These feature sets used by current tools for pre-miRNA recognition differ in construction and dimension. Some feature sets are composed of sequence-structure patterns commonly found in pre-miRNAs, while others are a combination of more sophisticated RNA features. Current tools achieve similar predictive performance even though the feature sets used - and their computational cost - differ widely. In this work, we analyze the discriminant power of seven feature sets, which are used in six pre-miRNA prediction tools. The analysis is based on the classification performance achieved with these feature sets for the training algorithms used in these tools. We also evaluate feature discrimination through the F-score and feature importance in the induction of random forests. More diverse feature sets produce classifiers with significantly higher classification performance compared to feature sets composed only of sequence-structure patterns. However, small or non-significant differences were found among the estimated classification performances of classifiers induced using sets with diversification of features, despite the wide differences in their dimension. Based on these results, we applied a feature selection method to reduce the computational cost of computing the feature set, while maintaining discriminant power. We obtained a lower-dimensional feature set, which achieved a sensitivity of 90% and a specificity of 95%. Our feature set achieves a sensitivity and specificity within 0.1% of the maximal values obtained with any feature set while it is 34x faster to compute. Even compared to another feature set, which is the computationally least expensive feature set of those from the literature which perform within 0.1% of the maximal values, it is 34x faster to compute.Comment: Submitted to BMC Bioinformatics in October 25, 2013. The material to reproduce the main results from this paper can be downloaded from http://bioinformatics.rutgers.edu/Static/Software/discriminant.tar.g

Additional file 2 of Automatic learning of pre-miRNAs from different species

Phylum/division, subphylum/class, species, acronyms, number of redundant negative examples out of 1,000 sequences excised from CDS or pseudo genes and the corresponding website link for download. (PDF 27.1 kb

Additional file 1 of Automatic learning of pre-miRNAs from different species

Phylum/division, subphylum/class, species, acronyms, number of positive examples available at miRBase 20, mean and standard deviation of the length distributions. NR=Non-Redundant. (PDF 17.8 kb

3D conductive monolithic carbons from pyrolyzed bamboo for microfluidic self-heating system

Bamboo, like wood, is a promising natural template for biobased devices that takes advantage of its hierarchical architecture, microarray channels, anisotropic mechanical and electrical properties. Herein we report a low heat thermal treatment (HTT, 700-1000 °C) of natural bamboo specimens to obtain bamboo-based graphitic devices with thermoelectric and electrochemical properties. The preservation of the highly anisotropic architecture of three-dimensional carbon material (3D-CM) allowed adding specific thermoelectric and electrochemical properties depending on the HTT of the pristine specimens. High electric conductivity (σ, 839 S/m) was observed at 1000 °C showing a remarkable potential application as a bamboo-based working electrode. The bamboo annealed to 700 °C showed higher resistivity (ρ, 0.15 Ω m, and σ, 6.6 S/m), thermal conductivity (1.77 W/m K), and thermal heating rate (1.0 °C/s). The pyrolyzed biomass (B-700) was used as a 3D microfluidic heater to heat polar solvents (H2O and ethylene glycol) in flow mode up to their boiling points. A 2D carbon hotplate heater was built-up to warm solvent in batch mode. A complete chemical and physical characterization of the samples allowed us to determine structural and chemical compositions, cellulose crystalline structure phase transition to graphitic/turbostratic carbon, thermal and electrical conductivity of unprecedented bambootronics bio-devices