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Abstract Many biomedical problems relate to mutant functional properties across a sequence space of interest, e. Detailed knowledge of mutant properties and function improves medical treatment and prevention. A functional census of p53 cancer rescue mutants would aid the search for cancer treatments from p53 mutant rescue. We devised a general methodology for conducting a functional census of a mutation sequence space by choosing informative mutants early.

The methodology was tested in a double-blind predictive test on the functional rescue property of 71 novel putative p53 cancer rescue mutants iteratively predicted in sets of three 24 iterations. Wu, M. This weight vector is usually obtained by solving a convex optimization problem. Based on this fact we present a direct method to build Sparse Kernel Learning Algorithms SKLA by adding one more constraint to the original convex optimization problem, such that the sparseness of the resulting KM is explicitly controlled while at the same time the performance of the resulting KM can be kept as high as possible.

A gradient based approach is provided to solve this modified optimization problem. Further analysis of the SLMC algorithm indicates that it essentially finds a discriminating subspace that can be spanned by a small number of vectors, and in this subspace, the different classes of data are linearly well separated.

Experimental results over several classification benchmarks demonstrate the effectiveness of our approach. Abstract We present a framework for efficient extrapolation of reduced rank approximations, graph kernels, and locally linear embeddings LLE to unseen data. We also present a principled method to combine many of these kernels and then extrapolate them. Central to our method is a theorem for matrix approximation, and an extension of the representer theorem to handle multiple joint regularization constraints.

Experiments in protein classification demonstrate the feasibility of our approach. Vishwanathan, SVN. Blanchard, G. Abstract We study the properties of the eigenvalues of Gram matrices in a non-asymptotic setting. Using local Rademacher averages, we provide data-dependent and tight bounds for their convergence towards eigenvalues of the corresponding kernel operator.

We perform these computations in a functional analytic framework which allows to deal implicitly with reproducing kernel Hilbert spaces of infinite dimension. In these bounds, the dependence on the decay of the spectrum and on the closeness of successive eigenvalues is made explicit. Kato, T. Abstract Prediction of human cell response to anti-cancer drugs compounds from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs.

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Hence there is a strong need for more practical and robust methods than standard methods for real-value prediction. We devised an extended version of the off-subspace noise-reduction de-noising method to incorporate heterogeneous network data such as sequence similarity or protein-protein interactions into a single framework. Using that method, we first de-noise the gene expression data for training and test data and also the drug-response data for training data.

Then we predict the unknown responses of each drug from the de-noised input data. For ascertaining whether de-noising improves prediction or not, we carry out fold cross-validation for assessment of the prediction performance. Furthermore, we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data.

We found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer drug responses from microarray data. Kuss, M. Cho, S. Abstract We present three data mining problems that are often encountered in building a response model. They are robust modeling, variable selection and data selection.

Respective algorithmic solutions are given. They are bagging based ensemble, genetic algorithm based wrapper approach and nearest neighbor-based data selection in that order. Proposed methods were found to solve the problems in a practical way. Pfingsten, T. Abstract Fluctuations are inherent to any fabrication process.

In recent years it has become possible to model the performance of such complex systems on the basis of design specifications, and model-based Sensitivity Analysis has made its way into industrial engineering. We show how an efficient Bayesian approach, using a Gaussian process prior, can replace the commonly used brute-force Monte Carlo scheme, making it possible to apply the analysis to computationally costly models.

We introduce a number of global, statistically justified sensitivity measures for design analysis and optimization.

Empirical Inference Science Afterword of 2006

Two models of integrated systems serve us as case studies to introduce the analysis and to assess its convergence properties. We show that the Bayesian Monte Carlo scheme can save costly simulation runs and can ensure a reliable accuracy of the analysis. Jegelka, S. Abstract How orientation and ocular-dominance OD maps develop before visual experience begins is controversial. Possible influences include molecular signals and spontaneous activity, but their contributions remain unclear.

Individual maps develop robustly with various previsual patterns, and are aided by background noise. Therefore, future biological experiments should account for multiple activity sources, and should measure map interactions rather than maps of single features. Abstract The determination of macromolecular structures requires weighting of experimental evidence relative to prior physical information.

Although it can critically affect the quality of the calculated structures, experimental data are routinely weighted on an empirical basis. At present, cross-validation is the most rigorous method to determine the best weight. We describe a general method to adaptively weight experimental data in the course of structure calculation. It is further shown that the necessity to define weights for the data can be completely alleviated. We demonstrate the method on a structure calculation from NMR data and find that the resulting structures are optimal in terms of accuracy and structural quality.

Our method is devoid of the bias imposed by an empirical choice of the weight and has some advantages over estimating the weight by cross-validation. Habeck, M. Cheng, J. Large-scale prediction of disulphide bridges using kernel methods, two-dimensional recursive neural networks, and weighted graph matching Proteins , 62 3 , February article Web DOI [BibTex]. Grosse-Wentrup, M. Abstract Given a linear and instantaneous mixture model, we prove that for blind source separation BSS algorithms based on mutual information, only sources with non-Gaussian distribution are consistently reconstructed independent of initial conditions.

This allows the identification of non-Gaussian sources and consequently the identification of signal and noise subspaces through BSS. The results are illustrated with a simple example, and the implications for a variety of signal processing applications, such as denoising and model identification, are discussed. Graf, A. Zhang, K. Radl, A.

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Zhang, W. Abstract We propose a novel approach to similarity assessment for graphic symbols. Symbols are represented as 2D kernel densities and their similarity is measured by the Kullback-Leibler divergence. Symbol orientation is found by gradient-based angle searching or independent component analysis. Experimental results show the outstanding performance of this approach in various situations. In this article, we first investigate the feasibility of separating the SDICA mixture in an adaptive manner.

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  5. This method is based on the minimization of the mutual information between outputs. Some practical issues are discussed. For better applicability, a scheme to avoid the high-dimensional score function difference is given. Third, we investigate one form of the overcomplete ICA problems with sources having specific frequency characteristics, which BS-ICA can also be used to solve. Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems. Toggle navigation.

    Year 45 64 63 46 31 38 14 12 6 3 2. Another detailed study on volatility forecasting and the specific problems of time series data was performed by Gavrishchaka and Ganguli, The authors found that SVMs could successfully handle both long memory and multiscale effects of inhomogeneous markets without imposing the restric- tive model assumptions of other methods.

    They emphasised the capability of SVMs to process real-time multiscale and high-frequency market data and their ability to tolerate data incompleteness. Using kernel methods Ince and Trafalis, selected stocks for short- term portfolio management. In their study they examined SVMs and minimax probability machines MPM , both of which provided similarly good results, depending on a sensible choice of free parameters. Trafalis et al. Mitschele Baesens et al. Further applications in financial forecasting were presented by Trafalis and Ince, ; Ince and Trafalis, , van Gestel et al.

    Another interesting hybrid approach that again combined self organizing maps with SVMs for exchange rate prediction was presented by Ni and Yin, In table 1 we present a structured overview of these applications showing their individ- ual publication dates. It can be concluded from the table that support vector regression plays a very dominant role within market risk management while in credit risk management mostly kernel based classification methods are em- ployed.

    There are also a smaller number of kernel PCA applications and some hybrid approaches throughout most of the considered areas. Interestingly, we found within the sample of publications of this review that the publication frequency reached another peak in after an early peak in These numbers underpin the timeliness of the proposed methods and the domain specific advantages that kernel methods may deliver within finance.

    Possible Future Application Fields Besides the presented overview of machine learning concepts and the review of ongoing research efforts in the subject matter it is an aim of this contribution to identify promising trends for future application of kernel methods. Due to their strengths in statistical data analysis, SVMs in particular can possibly improve performance in numerous further financial application fields. Even though it usually involves very heterogenous data sets with possibly non-linear relations, banks commonly still trust in linear methods, like linear regression, to derive their parameter estimates in practice.

    Referring to the presented advanced dimensionality reduction methods in section 3 there are also promising new application fields. Thomason, 6 LGD is a highly relevant parameter from the Basel II context Basel Committee on Banking Supervision and represents the percentage of an engagement that a financial institution looses if a specific obligor defaults. Mitschele reviewed PCA as a traditional dimensionality reduction method in the con- text of financial forecasting models. He stated that standard PCA using the normal distribution assumption may be not particularly suited for financial data.

    While he proposed a self-developed advanced PCA approach, a num- ber of authors have already employed other recently developed dimensionality reduction algorithms such as KPCA. Cao et al. They found that KPCA had the best characteristics. Within short term portfolio management Ince and Trafalis, used KPCA and factor analysis, respectively, to identify the most influential inputs for a SVM based stock price forecasting model. Furthermore, within market risk management there are different new areas where dimensionality reduction can also be applied, for instance term struc- ture modeling.

    In these models a high number of possible input factors has to be reduced to eventually make the modeling possible Alexander, However, to the knowledge of the authors, no kernel method applications have been reported in this area yet. Apart from such modeling issues, market risk management often involves the approximation of high quantiles for a certain distribution.

    This is espe- cially relevant in the context of portfolio risk management where value at risk VaR measures the risk of a loss within a specific time interval given a certain confidence level quantile. In a novel application Christmann, and subsequently Takeuchi et al. It has been integrated as package e into the R project which is a popular open source statistics software9.

    One of the main strengths of this implementation is the fact that through scalable memory requirements it can handle problems with many thousands of support vectors and several hundred thousands of training vectors very efficiently. This well-documented software package offers standard classification and regression using LS-SVM algorithms. Addi- tionally KPCA, ultra large scale problems and a number of other advanced methods are supported.

    WEKA13 Witten and Frank, is a sophisticated environment with graphical user interface for machine learning and data mining which is imple- mented in Java. It includes a large library of classification algorithms including SVMs and neural networks as well as evaluation tools such as ROC curves Fawcett, It includes a large variety of tools for preprocessing, training and evaluation. A WEKA interface has also been integrated. It offers the option to employ combined kernels which can be constructed by weighted linear combinations of sub-kernels. Mitschele 9 Conclusion The overview of kernel methods showed that the field has quickly advanced in recent years and provides an umbrella for some of the most successful algorithms for classification, regression, and dimensionality reduction.

    Non- linear methods such as KPCA, Isomap, and non-linear SVMs for regression and classification can be obtained through kernelisation of linear techniques. The development on the machine learning side is rapid and new concepts and improvements, which have not been yet applied in finance, are continually emerging. Recent advances in the machine learning community to establish task specific kernel design offer new opportunities and challenges for financial applications. Among the financial applications addressed in the review section notably the best results have been obtained in the area of credit risk whenever the un- derlying data exhibited non-linear characteristics, as for instance in Baesens et al.

    The availability of kernel methods to accurately handle nonlinear dependencies has potential to further enhance current re- sults. With respect to the high amounts that are dealt with on the financial markets even very small performance or accuracy improvements can result in considerable savings.

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    Estimation of Dependences Based on Empirical Data

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