Decoding Sequence Classification Models for Acquiring New Biological Insights

Classifying biological sequences is one of the most important tasks in computational biology. In the last decade, support vector machines (SVMs) in combination with sequence kernels have emerged as a de-facto standard. These methods are theoretically well-founded, reliable, and provide high-accuracy solutions at low computational cost. However, obtaining a highly accurate classifier is rarely the end of the story in many practical situations. Instead, one often aims to acquire biological knowledge about the principles underlying a given classification task. SVMs with traditional sequence kernels do not offer a straightforward way of accessing this knowledge.

In this contribution, we propose a new approach to analyzing biological sequences on the basis of support vector machines with sequence kernels. We first extract explicit pattern weights from a given SVM. When classifying a sequence, we then compute a prediction profile by distributing the weight of each pattern to the sequence positions that match the pattern. The final profile not only allows assessing the importance of a position, but also determining for which class it is indicative. Since it is unfeasible to analyze profiles of all sequences in a given data set, we advocate using affinity propagation (AP) clustering to narrow down the analysis to a small set of typical sequences.

The proposed approach is applicable to a wide range of biological sequences and a wide selection of sequence kernels. To illustrate our framework, we present the prediction of oligomerization tendencies of coiled coil proteins as a case study.
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Orderings of fuzzy sets based on fuzzy orderings. Part II: generalizations

In Part I of this series of papers, a general approach for ordering fuzzy sets with respect to fuzzy orderings was presented. Part I also highlighted three limitations of this approach. The present paper addresses these lim- itations and proposes solutions for overcoming them. We rst consider a fuzzi cation of the ordering relation, then ways to compare fuzzy sets with di erent heights, and nally we introduce how to re ne the ordering relation by lexicographic hybridization with a di erent ordering methodPeer Reviewe

Robust rank correlation coefficients on the basis of fuzzy

The goal of this paper is to demonstrate that established rank correlation measures are not ideally suited for measuring rank correlation for numerical data that are perturbed by noise. We propose to use robust rank correlation measures based on fuzzy orderings. We demonstrate that the new measures overcome the robustness problems of existing rank correlation coe cients. As a rst step, this is accomplished by illustrative examples. The paper closes with an outlook on future research and applicationsPeer Reviewe

Orderings of fuzzy sets based on fuzzy orderings. Part I: the basic approach

The aim of this paper is to present a general framework for comparing fuzzy sets with respect to a general class of fuzzy orderings. This approach includes known techniques based on generalizing the crisp linear ordering of real numbers by means of the extension principle, however, in its general form, it is applicable to any fuzzy subsets of any kind of universe for which a fuzzy ordering is known|no matter whether linear or partialPeer Reviewe

On the Preservation of Monotonicity by Extended Mappings

Abstract Images of fuzzy relations provide powerful access to fuzzifications of properties of and/or relationships between fuzzy sets. As an important example, images of fuzzy orderings canonically lead to a concept of ordering of fuzzy sets. This contribution studies in which way the partial (i.e. componentwise) monotonicity of an n-ary mapping transfers to its extension to fuzzy sets

Assessing luminosity correlations via cluster analysis: Evidence for dual tracks in the radio/X-ray domain of black hole X-ray binaries

[abridged] The radio:X-ray correlation for hard and quiescent state black hole X-ray binaries is critically investigated in this paper. New observations of known sources, along with newly discovered ones, have resulted in an increasingly large number of outliers lying well outside the scatter about the quoted best-fit relation. Here, we employ and compare state of the art data clustering techniques in order to identify and characterize different data groupings within the radio:X-ray luminosity plane for 18 hard and quiescent state black hole X-ray binaries with nearly simultaneous multi-wavelength coverage. Linear regression is then carried out on the clustered data to infer the parameters of a relationship of the form {ell}_{r}=alpha+beta {ell}_x through a Bayesian approach (where {ell} denotes log lum). We conclude that the two cluster model, with independent linear fits, is a significant improvement over fitting all points as a single cluster. While the upper track slope (0.63\pm0.03) is consistent, within the errors, with the fitted slope for the 2003 relation (0.7\pm0.1), the lower track slope (0.98\pm0.08) is not consistent with the upper track, nor it is with the widely adopted value of ~1.4 for the neutron stars. The two luminosity tracks do not reflect systematic differences in black hole spins as estimated either from reflection, or continuum fitting method. These results are insensitive to the selection of sub-samples, accuracy in the distances, and to the treatment of upper limits. Besides introducing a further level of complexity in understanding the interplay between synchrotron and Comptonised emission from black hole X-ray binaries, the existence of two tracks in the radio:X-ray domain underscores that a high level of caution must be exercised when employing black hole luminosity relations for the purpose of estimating a third parameter, such as distance or mass.Comment: MNRAS, in press (10 pages, 7 figures

Approximation of Belief Functions by Minimizing Euclidean Distance

Abstract. This paper addresses the approximation of belief functions by minimizing the Euclidean distance to a given belief function in the set of probability functions. The special case of Dempster-Shafer belief functions is considered in particular detail. It turns out that, in this case, an explicit solution by means of a projective transformation can be given. Furthermore, we also consider more general concepts of belief. We state that the approximation by means of minimizing the Euclidean distance, unlike other methods that are restricted to Dempster-Shafer belief, works as well. However, the projective transformation formula cannot necessarily be applied in these more general settings

A multi-site perfusion monitoring sub-system

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references (p. 123-125).by Myev A. Bodenhofer.Ph.D