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2000


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Identification of Drug Target Proteins

Zien, A., Küffner, R., Mevissen, T., Zimmer, R., Lengauer, T.

ERCIM News, 43, pages: 16-17, October 2000 (article)

Web [BibTex]

2000

Web [BibTex]


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Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites

Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T., Müller, K.

Bioinformatics, 16(9):799-807, September 2000 (article)

Abstract
Motivation: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called translation initiation sites (TIS). Results: The task of finding TIS can be modeled as a classification problem. We demonstrate the applicability of support vector machines for this task, and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.

Web DOI [BibTex]

Web DOI [BibTex]


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A Meanfield Approach to the Thermodynamics of a Protein-Solvent System with Application to the Oligomerization of the Tumour Suppressor p53.

Noolandi, J., Davison, TS., Vokel, A., Nie, F., Kay, C., Arrowsmith, C.

Proceedings of the National Academy of Sciences of the United States of America, 97(18):9955-9960, August 2000 (article)

Web [BibTex]

Web [BibTex]


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New Support Vector Algorithms

Schölkopf, B., Smola, A., Williamson, R., Bartlett, P.

Neural Computation, 12(5):1207-1245, May 2000 (article)

Abstract
We propose a new class of support vector algorithms for regression and classification. In these algorithms, a parameter {nu} lets one effectively control the number of support vectors. While this can be useful in its own right, the parameterization has the additional benefit of enabling us to eliminate one of the other free parameters of the algorithm: the accuracy parameter {epsilon} in the regression case, and the regularization constant C in the classification case. We describe the algorithms, give some theoretical results concerning the meaning and the choice of {nu}, and report experimental results.

Web DOI [BibTex]

Web DOI [BibTex]


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Bounds on Error Expectation for Support Vector Machines

Vapnik, V., Chapelle, O.

Neural Computation, 12(9):2013-2036, 2000 (article)

Abstract
We introduce the concept of span of support vectors (SV) and show that the generalization ability of support vector machines (SVM) depends on this new geometrical concept. We prove that the value of the span is always smaller (and can be much smaller) than the diameter of the smallest sphere containing th e support vectors, used in previous bounds. We also demonstate experimentally that the prediction of the test error given by the span is very accurate and has direct application in model selection (choice of the optimal parameters of the SVM)

GZIP [BibTex]

GZIP [BibTex]