101,341 research outputs found
Informatik in den Biowissenschaften
No full textHofestädt R, Lengauer T, Löffler M, eds. Informatik in den Biowissenschaften. Informationstechnik und technische Informatik. 1996;38(5)
Computer Science and Biology - Proceedings of the German Conference on Bioinformatics (GCB 96)
No full textHofestädt R, Lengauer T, Löffler M, Schomburg D, eds. Computer Science and Biology - Proceedings of the German Conference on Bioinformatics (GCB 96). Report. 1996;1:341
The correlation between the complexities of the non-hierarchical and hierarchical versions of graph problems
No full textThe correlation between the complexities of the non-hierarchical and hierarchical versions of graph problems / T. Lengauer ; K. W. Wagner. - In: STACS : STACS 87 / ed. by F. J. Brandenburg ... - Berlin u. a. : Springer, 1987. - S. 100-113. - (Lecture notes in computer science ; 247)
Bioinformatics
No full textHofestädt R, Lengauer T, Löffler M, Schomburg D, eds. Bioinformatics. LNCS. Vol 1278 1. Edition. Berlin: Springer; 1997
Informatik in den Biowissenschaften. 1. Fachtagung der GI-FG 4.0.2 „Informatik in den Biowissenschaften“, Bonn, 15./16. Februar 1993
No full textHofestädt R, Krückeberg F, Lengauer T, eds. Informatik in den Biowissenschaften. 1. Fachtagung der GI-FG 4.0.2 „Informatik in den Biowissenschaften“, Bonn, 15./16. Februar 1993. Informatik Aktuell. 1993:VIII, 214
Simple consensus procedures are effective and sufficient in secondary structure prediction
No full textWe have analyzed the performance of majority voting on minimal combination sets of three state-of-the-art secondary structure prediction methods in order to obtain a consensus prediction. Using three large benchmark sets from the EVA server, our results show a significant improvement in the average Q3 prediction accuracy of up to 1.5 percentage points by consensus formation. The application of an additional trivial filtering procedure for predicted secondary structure elements that are too short, does not significantly affect the prediction accuracy. Our analysis also provides valuable insight into the similarity of the results of the prediction methods that we combine as well as the higher confidence in consistently predicted secondary structure
Decomposing protein networks into domain-domain interactions
No full textThe application of novel experimental techniques has generated large networks of protein-protein interactions. Frequently, important information on the structure and cellular function of protein-protein interactions can be gained from the domains of interacting proteins. We have designed a Cytoscape plugin that decomposes interacting proteins into their respective domains and computes a putative network of corresponding domain-domain interactions. To this end, the network graph of proteins has been extended by additional node and edge types for domain interactions, including different node and edge shapes and coloring schemes used for visualization. An additional plugin provides supplementary web links to Internet resources on domain function and structure. AVAILABILITY: Both Cytoscape plugins can be downloaded from http://www.cytoscape.or
Letter, [Author unclear] to Paulina T. Merritt
No full textHandwritten letter to Paulina Merritt from an unknown author, October 1, 1876.
Improving the quality of protein structure models by selecting from alignment alternatives
No full textAbstract Background In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish high quality protein structure models from inferior models. Here, we propose a new method to use an MQAP to improve the quality of models. With a given target sequence and template structure, we construct a number of different alignments and corresponding models for the sequence. The quality of these models is scored with an MQAP and used to choose the most promising model. An SVM-based selection scheme is suggested for combining MQAP partial potentials, in order to optimize for improved model selection. Results The approach has been tested on a representative set of proteins. The ability of the method to improve models was validated by comparing the MQAP-selected structures to the native structures with the model quality evaluation program TM-score. Using the SVM-based model selection, a significant increase in model quality is obtained (as shown with a Wilcoxon signed rank test yielding p-values below 10-15). The average increase in TMscore is 0.016, the maximum observed increase in TM-score is 0.29. Conclusion In template-based protein structure prediction alignment is known to be a bottleneck limiting the overall model quality. Here we show that a combination of systematic alignment variation and modern model scoring functions can significantly improve the quality of alignment-based models.</p
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