Automatic classification of laser-induced breakdown spectroscopy (LIBS) data of protein biomarker solutions.

We perform multi-class classification of laser-induced breakdown spectroscopy data of four commercial samples of proteins diluted in phosphate-buffered saline solution at different concentrations: bovine serum albumin, osteopontin, leptin, and insulin-like growth factor II. We achieve this by using principal component analysis as a method for dimensionality reduction. In addition, we apply several different classification algorithms (K-nearest neighbor, classification and regression trees, neural networks, support vector machines, adaptive local hyperplane, and linear discriminant classifiers) to perform multi-class classification. We achieve classification accuracies above 98% by using the linear classifier with 21-31 principal components. We obtain the best detection performance for neural networks, support vector machines, and adaptive local hyperplanes for a range of the number of principal components with no significant differences in performance except for that of the linear classifier. With the optimal number of principal components, a simplistic K-nearest classifier still provided acceptable results. Our proposed approach demonstrates that highly accurate automatic classification of complex protein samples from laser-induced breakdown spectroscopy data can be successfully achieved using principal component analysis with a sufficiently large number of extracted features, followed by a wrapper technique to determine the optimal number of principal components.

Agent based modeling of blood coagulation system: implementation using a GPU based high speed framework.

The coagulation and fibrinolytic systems are complex, inter-connected biological systems with major physiological roles. The complex, nonlinear multi-point relationships between the molecular and cellular constituents of two systems render a comprehensive and simultaneous study of the system at the microscopic and macroscopic level a significant challenge. We have created an Agent Based Modeling and Simulation (ABMS) approach for simulating these complex interactions. As the scale of agents increase, the time complexity and cost of the resulting simulations presents a significant challenge. As such, in this paper, we also present a high-speed framework for the coagulation simulation utilizing the computing power of graphics processing units (GPU). For comparison, we also implemented the simulations in NetLogo, Repast, and a direct C version. As our experiments demonstrate, the computational speed of the GPU implementation of the million-level scale of agents is over 10 times faster versus the C version, over 100 times faster versus the Repast version and over 300 times faster versus the NetLogo simulation.

Blind multiuser detector for chaos-based CDMA using support vector machine.

The algorithm and the results of a blind multiuser detector using a machine learning technique called support vector machine (SVM) on a chaos-based code division multiple access system is presented in this paper. Simulation results showed that the performance achieved by using SVM is comparable to existing minimum mean square error (MMSE) detector under both additive white Gaussian noise (AWGN) and Rayleigh fading conditions. However, unlike the MMSE detector, the SVM detector does not require the knowledge of spreading codes of other users in the system or the estimate of the channel noise variance. The optimization of this algorithm is considered in this paper and its complexity is compared with the MMSE detector. This detector is much more suitable to work in the forward link than MMSE. In addition, original theoretical bit-error rate expressions for the SVM detector under both AWGN and Rayleigh fading are derived to verify the simulation results.

Analysis of tidal breathing flow volume loops for automated lung-function diagnosis in infants.

Lung-function analysis in the age group below 5 years has not yet found its way into clinical routine. One possible candidate for routine lung testing in this age group is the analysis of tidal breathing flow-volume (TBFV) loops, a technique that has not yet proven to be capable of detecting obstructive and other lung disorders at an early stage. We present a new set of mathematical features useful to analyze TBFV loops. These new features attempt to describe more complex properties of the loops, thus imitating medical judgment of the curves (e.g., "round," "triangular," etc.) in a "linguistic" manner. Furthermore, we introduce support vector machines (SVMs) as a method for automated classification of diseases. In a retrospective clinical trial on 195 spontaneously breathing infants aged 3 to 24 months, the discriminant power of individual features and the overall diagnostic performance of SVMs is investigated and compared with the results obtained with traditional Bayes' classifiers. We demonstrate that the proposed new features perform better in all examined disease groups and that depending on the disease, the classification error can be reduced by up to 50%. We conclude that TBFV loops may have a much stronger discriminant power than previously thought.

Resolution and uniqueness of estimated parameters of a model of thin filament regulation in solution.

The estimation of chemical kinetic rate constants for any non-trivial model is complex due to the nonlinear effects of second order chemical reactions. We developed an algorithm to accomplish this goal based on the Damped Least Squares (DLS) inversion method and then tested the effectiveness of this method on the McKillop-Geeves (MG) model of thin filament regulation. The kinetics of MG model is defined by a set of nonlinear ordinary differential equations (ODEs) that predict the evolution of troponin-tropomyosin-actin and actin-myosin states. The values of the rate constants are estimated by integrating these ODEs numerically and fitting them to a series of stopped-flow pyrene fluorescence transients of myosin-S1 fragment binding to regulated actin in solution. The accuracy and robustness of the estimated rate constants are evaluated for DLS and two other methods, namely quasi-Newton (QN) and simulated annealing (SA). The comparison of these methods revealed that SA provides the best estimates of the model parameters because of its global optimization scheme. However it converges slowly and does quantify the uniqueness of the estimated parameters. On the other hand the QN method converges rapidly but only if the initial guess of the parameters is close to the optimum values, otherwise it diverges. Overall, the DLS method proves to be the most convenient method. It converges fast and was able to provide excellent estimates of kinetic parameters. Furthermore, DLS provides the model resolution matrix, which quantifies the interdependence of model parameters thereby evaluating the uniqueness of their estimated values. This property is essential for estimating of the dependence of the model parameters on experimental conditions (e.g. Ca(2+) concentration) when it is assessed from noisy experimental data such as pyrene fluorescence from stopped-flow transients. The advantages of the DLS method observed in this study should be further examined in other physicochemical systems to firmly establish the observed effectiveness of DSL vs. the other parameter estimation methods.

Gene extraction for cancer diagnosis by support vector machines--an improvement.

OBJECTIVE: To improve the performance of gene extraction for cancer diagnosis by recursive feature elimination with support vector machines (RFE-SVMs): A cancer diagnosis by using the DNA microarray data faces many challenges the most serious one being the presence of thousands of genes and only several dozens (at the best) of patient's samples. Thus, making any kind of classification in high-dimensional spaces from a limited number of data is both an extremely difficult and a prone to an error procedure. The improved RFE-SVMs is introduced and used here for an elimination of less relevant genes and just for a reduction of the overall number of genes used in a medical diagnostic. METHODS: The paper shows why and how the, usually neglected, penalty parameter C and some standard data preprocessing techniques (normalizing and scaling) influence classification results and the gene selection of RFE-SVMs. The gene selected by RFE-SVMs is compared with eight other gene selection algorithms implemented in the Rankgene software to investigate whether there is any consensus among the algorithms, so the scope of finding the right set of genes can be reduced. RESULTS: The improved RFE-SVMs is applied on the two benchmarking colon and lymphoma cancer data sets with various C parameters and different standard preprocessing techniques. Here, decreasing C leads to the smaller diagnosis error in comparisons to other known methods applied to the benchmarking data sets. With an appropriate parameter C and with a proper preprocessing procedure, the reduction in a diagnosis error is as high as 36%. CONCLUSIONS: The results suggest that with a properly chosen parameter C, the extracted genes and the constructed classifier will ensure less overfitting of the training data leading to an increased accuracy in selecting relevant genes. Finally, comparison in gene ranking obtained by different algorithms shows that there is a significant consensus among the various algorithms as to which set of genes is relevant.