Gábor E. Tusnády

Bio: Gábor E. Tusnády is an academic researcher from Hungarian Academy of Sciences. The author has contributed to research in topics: Transmembrane protein & Membrane protein. The author has an hindex of 34, co-authored 159 publications receiving 9913 citations.

The HMMTOP transmembrane topology prediction server

Abstract: Summary: The HMMTOP transmembrane topology prediction server predicts both the localization of helical transmembrane segments and the topology of transmembrane proteins. Recently, several improvements have been introduced to the original method. Now, the user is allowed to submit additional information about segment localization to enhance the prediction power. This option improves the prediction accuracy as well as helps the interpretation of experimental results, i.e. in epitope insertion experiments. Availability: HMMTOP 2.0 is freely available to noncommercial users at http://www.enzim.hu/hmmtop. Source code is also available upon request to academic users.

An Approximation of Partial Sums of Independent RV's, and the Sample DF. II

Abstract: Let S n =X 1+X 2+⋯+X n be the sum of i.i.d.r.v.-s, EX 1=0, EX 1 2 =1, and let T n = Y 1+Y 2+⋯+Y n be the sum of independent standard normal variables. Strassen proved in [14] that if X 1 has a finite fourth moment, then there are appropriate versions of S n and T n (which, of course, are far from being independent) such that ¦S n -T n ¦=O(n 1/4(log n)1/1(log log n)1/4) with probability one. A theorem of Bartfai [1] indicates that even if X 1 has a finite moment generating function, the best possible bound for any version of S n , T n is O(log n). In this paper we introduce a new construction for the pair S n , T n , and prove that if X 1 has a finite moment generating function, and satisfies condition i) or ii) of Theorem 1, then ¦S n -T n ¦=O(log n) with probability one for the constructed S n , T n . Our method will be applicable for the approximation of sample DF., too.

The degree sequence of a scale-free random graph process

Abstract: Recently, Barabasi and Albert [2] suggested modeling complex real-world networks such as the worldwide web as follows: consider a random graph process in which vertices are added to the graph one at a time and joined to a fixed number of earlier vertices, selected with probabilities proportional to their degrees. In [2] and, with Jeong, in [3], Barabasi and Albert suggested that after many steps the proportion P(d) of vertices with degree d should obey a power law P(d)αd−γ. They obtained γ=2.9±0.1 by experiment and gave a simple heuristic argument suggesting that γ=3. Here we obtain P(d) asymptotically for all d≤n1/15, where n is the number of vertices, proving as a consequence that γ=3. © 2001 John Wiley & Sons, Inc. Random Struct. Alg., 18, 279–290, 2001

PSORT-B: Improving protein subcellular localization prediction for Gram-negative bacteria.

Abstract: Automated prediction of bacterial protein subcellular localization is an important tool for genome annotation and drug discovery. PSORT has been one of the most widelyused computational methods for such bacterial protein analysis; however, it has not been updated since it was introduced in 1991. In addition, neither PSORT nor anyof the other computational methods available make predictions for all five of the localization sites characteristic of Gram-negative bacteria. Here we present PSORT-B, an updated version of PSORT for Gram-negative bacteria, which is available as a web-based application at http://www.psort.org. PSORT-B examines a given protein sequence for amino acid composition, similarityto proteins of known localization, presence of a signal peptide, transmembrane alpha-helices and motifs corresponding to specific localizations. A probabilistic method integrates these analyses, returning a list of five possible localization sites with associated probabilityscores. PSORT-B, designed to favor high precision (specificity) over high recall (sensitivity), attained an overall precision of 97% and recall of 75% in 5-fold cross-validation tests, using a dataset we developed of 1443 proteins of experimentallyknown localization. This dataset, the largest of its kind, is freelyavailable, along with the PSORT-B source code (under GNU General Public License).

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

The Structure and Function of Complex Networks

Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

A Practical Guide to Support Vector Classication

Abstract: Support vector machine (SVM) is a popular technique for classication. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but signicant steps. In this guide, we propose a simple procedure, which usually gives reasonable results.