miR-Q: a novel quantitative RT-PCR approach for the expression profiling of small RNA molecules such as miRNAs in a complex sample

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are small endogenous non-coding interfering RNA molecules regarded as major regulators in eukaryotic gene expression. Different methods are employed for miRNA expression profiling. For a better understanding of their role in essential biological processes, convenient methods for differential miRNA expression analysis are required.</p> <p>Results</p> <p>Here, we present the miR-Q assay as a highly sensitive quantitative reverse transcription PCR (qRT-PCR) for expression analysis of small RNAs such as miRNA molecules. It shows a high dynamic range of 6 to 8 orders of magnitude comprising a sensitivity of up to 0.2 fM miRNA, which corresponds to single copies per cell. There is nearly no cross reaction among closely-related miRNA family members, which points to the high specificity of the assays. Using this approach, we quantified the expression of let-7b in different human cell lines as well as miR-145 and miR-21 expression in porcine intestinal samples.</p> <p>Conclusion</p> <p>miR-Q is a cost-effective and highly specific approach, which neither requires the use of fluorochromic probes, nor Locked Nucleic Acid (LNA)-modified oligonucleotides. Moreover, it provides a remarkable increase in specificity and simplified detection of small RNAs.</p

kNN and SVM classification for EEG: a review

This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model to predict the corresponding class of new input in an unseen dataset. EEG signals contaminated with various noises and artefacts, non-stationary and poor in signal-to-noise ratio (SNR). Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. However, different results were observed between different EEG applications. Hence, this paper reviews the used of kNN and SVM classifier on various EEG applications, identifying their advantages and disadvantages, and also their overall performances