An integrated approach for identifying wrongly labeled samples when performing classification in microarray data

published_or_final_versio

Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle

Using high throughput DNA binding data for transcription factors and DNA microarray time course data, we constructed four transcription regulatory networks and analysed them using a novel extension to the network component analysis (NCA) approach. We incorporated probe level uncertainties in gene expression measurements into the NCA analysis by the application of probabilistic principal component analysis (PPCA), and applied the method to data from yeast metabolic cycle. Analysis shows statistically significant enhancement to periodicity in a large fraction of the transcription factor activities inferred from the model. For several of these we found literature evidence of post-transcriptional regulation. Accounting for probe level uncertainty of microarray measurements leads to improved network component analysis. Transcription factor profiles showing greater periodicity at their activity levels, rather than at the corresponding mRNA levels, for over half the regulators in the networks points to extensive post-transcriptional regulations. ©2009 IEEE.published_or_final_versio

A prediction approach for multichannel EEG signals modeling using local wavelet SVM

Accurate modeling of the multichannel electroencephalogram (EEG) signal is an important issue in clinical practice. In this paper, we propose a new local spatiotemporal prediction method based on support vector machines (SVMs). Combining with the local prediction method, the sequential minimal optimization (SMO) training algorithm, and the wavelet kernel function, a local SMO-wavelet SVM (WSVM) prediction model is developed to enhance the efficiency, effectiveness, and universal approximation capability of the prediction model. Both the spatiotemporal modeling from the measured time series and the details of the nonlinear modeling procedures are discussed. Simulations and experimental results with real EEG signals show that the proposed method is suitable for real signal processing and is effective in modeling the local spatiotemporal dynamics. This method greatly increases the computational speed and more effectively captures the local information of the signal. © 2006 IEEE.published_or_final_versio

Comparison of blind source separation methods in fast somatosensory-evoked potential detection

Blind source separation (BSS) is a promising method for extracting somatosensory-evoked potential (SEP). Although various BSS algorithms are available for SEP extraction, few studies have addressed the performance differences between them. In this study, we compared the performance of a number of typical BSS algorithms on SEP extraction from both computer simulations and clinical experiment. The algorithms we compared included second-order blind identification, estimation of signal parameters via rotation invariance technique, algorithm for multiple unknown signals extraction, joint approximate diagonalization of eigenmatrices, extended infomax, and fast independent component analysis. The performances of these BSS algorithms were determined by the correlation coefficients between the true and the extracted SEP signals. There were significant differences in the performances of the various BSS algorithms in a simulation study. In summary, second-order blind identification using six covariance matrix denoting SOBI6 was recommended as the most appropriate BSS method for fast SEP extraction from noisy backgrounds. Copyright © 2011 by the American Clinical Neurophysiology Society.postprin

Spectrum of generalized Petersen Graphs

The Australasian Journal of Combinatorics 49 (2011), 39-45In this paper, we completely describe the spectrum of the generalized Petersen graph P(n/k), thus adding to the classes of graphs whose spectrum in completely known

Gene selection in microarray data analysis for brain cancer classification

Cancer classification has been one of the most challenging tasks in clinical diagnosis. At present cancer classification is done mainly by looking through the cells' morphological differences, which do not always give a clear distinction of cancer subtypes. Unfortunately, this may have a significant impact on the final outcome of whether a patient could be cured effectively. Microarray technology can play an important role on diagnosing which type of disease one is carrying. The gene selection process is critical for developing gene markers for faster and more accurate diagnosis. In this paper, we develop a method using pairwise data comparisons instead of the one-over-the-rest approach used nowadays. Results are evaluated using available clustering techniques including hierarchical clustering and k-means clustering. Using pairwise comparison, the best accuracy achieved is 95% while it is only 83% when using one-over-the-rest approach. ©2006 IEEE.published_or_final_versio

An improved method for discriminating ECG signals using typical nonlinear dynamic parameters and recurrence quantification analysis in cardiac disease therapy

The discrimination of ECG signals using nonlinear dynamic parameters is of crucial importance in the cardiac disease therapy and chaos control for arrhythmia defibrillation in the cardiac system. However, the discrimination results of previous studies using features such as maximal Lyapunov exponent (λ max) and correlation dimension (D 2) alone are somewhat limited in recognition rate. In this paper, improved methods for computing λ max and D 2 are purposed. Another parameter from recurrence quantification analysis is incorporated to the new multi-feature Bayesian classifier with λ max and D 2 so as to improve the discrimination power. Experimental results have verified the prediction using Fisher discriminant that the maximal vertical line length (V max) from recurrence quantification analysis is the best to distinguish different ECG classes. Experimental results using the MIT-BIH Arrhythmia Database show improved and excellent overall accuracy (96.3%), average sensitivity (96.3%) and average specificity (98.15%) for discriminating sinus, premature ventricular contraction and ventricular flutter signals. © 2005 IEEE.published_or_final_version27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS 2005), Shanghai, 17-18 January 2006. In Conference Proceedings of IEEE Engineering in Medicine and Biology Society, 2005, p. 2459-246

Rare B Decays with a HyperCP Particle of Spin One

In light of recent experimental information from the CLEO, BaBar, KTeV, and Belle collaborations, we investigate some consequences of the possibility that a light spin-one particle is responsible for the three Sigma^+ -> p mu^+ mu^- events observed by the HyperCP experiment. In particular, allowing the new particle to have both vector and axial-vector couplings to ordinary fermions, we systematically study its contributions to various processes involving b-flavored mesons, including B-Bbar mixing as well as leptonic, inclusive, and exclusive B decays. Using the latest experimental data, we extract bounds on its couplings and subsequently estimate upper limits for the branching ratios of a number of B decays with the new particle. This can serve to guide experimental searches for the particle in order to help confirm or refute its existence.Comment: 17 pages, 3 figures; discussion on spin-0 case modified, few errors corrected, main conclusions unchange