Mapping the Conformational Dynamics and Pathways of Spontaneous Steric Zipper Peptide Oligomerization

The process of protein misfolding and self-assembly into various, polymorphic aggregates is associated with a number of important neurodegenerative diseases. Only recently, crystal structures of several short peptides have provided detailed structural insights into -sheet rich aggregates, known as amyloid fibrils. Knowledge about early events of the formation and interconversion of small oligomeric states, an inevitable step in the cascade of peptide self-assembly, however, remains still limited

Training the random neural network using quasi-Newton methods

Training in the random neural network (RNN) is generally speci®ed as the minimization of an appropriate error function with respect to the parameters of the network (weights corresponding to positive and negative connections). We propose here a technique for error minimization that is based on the use of quasi-Newton optimization techniques. Such techniques o€er more sophisticated exploitation of the gradient information compared to simple gradient descent methods, but are computationally more expensive and di cult to implement. In this work we specify the necessary details for the application of quasi-Newton methods to the training of the RNN, and provide comparative experimental results from the use of these methods to some well-known test problems, which con®rm the superiority of th

Abstract A clustering method based on boosting

It is widely recognized that the boosting methodology provides superior results for classification problems. In this paper, we propose the boost-clustering algorithm which constitutes a novel clustering methodology that exploits the general principles of boosting in order to provide a consistent partitioning of a dataset. The boost-clustering algorithm is a multi-clustering method. At each boosting iteration, a new training set is created using weighted random sampling from the original dataset and a simple clustering algorithm (e.g. k-means) is applied to provide a new data partitioning. The final clustering solution is produced by aggregating the multiple clustering results through weighted voting. Experiments on both artificial and real-world data sets indicate that boost-clustering provides solutions of improved quality. Ó 2004 Elsevier B.V. All rights reserved

Mining Micro-Blogs: Opportunities and Challenges

Summary. This chapter investigates whether and how micro-messaging technologies such as Twitter messages can be harnessed to obtain valuable information. The interesting characteristics of micro-blogging services, such as being user oriented, provide opportunities for different applications to use the content of these sites to their advantage. However, the same characteristics become the weakness of these sites when it comes to data modeling and analysis of the messages. These sites contains very large amount of unstructured, noisy with false or missing data which make the task of data mining difficult. This chapter first reviews some of the potential applications of the micro-messaging services and then provides some insight into different challenges faced by data mining applications. Later in the chapter, characteristics of a real-data collected from the Twitter are analysed. At the end of chapter, application of micro-blogging services is shown by three different case studies.

A Spatially Constrained Mixture Model for Image Segmentation

[3] B. Hammer and P. Tino, “Recurrent neural networks with small weights implement definite memory machines, ” Neural Computat., vol. 15, no