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Building the library of RNA 3D nucleotide conformations using the clustering approach

Zok T., Antczak M., Riedel M., Nebel D., Villmann T., Lukasiak P., Blazewicz J., Szachniuk M.

International Journal of Applied Mathematics and Computer Science

2015

Vol. 25, no. 3

689--700

An increasing number of known RNA 3D structures contributes to the recognition of various RNA families and identification of their features. These tasks are based on an analysis of RNA conformations conducted at different levels of detail. On the other hand, the knowledge of native nucleotide conformations is crucial for structure prediction and understanding of RNA folding. However, this knowledge is stored in structural databases in a rather distributed form. Therefore, only automated methods for sampling the space of RNA structures can reveal plausible conformational representatives useful for further analysis. Here, we present a machine learning-based approach to inspect the dataset of RNA three-dimensional structures and to create a library of nucleotide conformers. A median neural gas algorithm is applied to cluster nucleotide structures upon their trigonometric description. The clustering procedure is two-stage: (i) backbone- and (ii) ribose-driven. We show the resulting library that contains RNA nucleotide representatives over the entire data, and we evaluate its quality by computing normal distribution measures and average RMSD between data points as well as the prototype within each cluster.

Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

Kaden M, Lange M., Nebel D, Riedel M, Geweniger T, Villmann T.

Foundations of Computing and Decision Sciences

2014

Vol. 39, No. 2

79--105

Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different tasks in classification learning. This paper focuses on learning vector quantization classifiers as one of the most intuitive prototype based classification models. Recent extensions and modifications of the basic learning vector quantization algorithm, which are proposed in the last years, are highlighted and also discussed in relation to particular classification task scenarios like imbalanced and/or incomplete data, prior data knowledge, classification guarantees or adaptive data metrics for optimal classification.