Clustering is an unsupervised machine learning technique that serves to extract patterns in unlabeled datasets by grouping their elements based on a similarity measure. A priori knowledge of the number of clusters is needed in most of the clustering techniques, which is both difficult and necessary for an effective and accurate pattern recognition and latent (not directly observable) feature analysis. Recently, graph based Symmetric Non-negative Matrix factorization (SymmNMF) has been demonstrated to perform better than k-means and spectral clustering. Here, we present a consensus clustering based on robust resampling technique which in conjunction with SymmNMF and Proportion of Ambiguous Clustering (PAC) criterion performs a robust graphical clustering and accurate identification of the number of clusters in several non-convex benchmark datasets.
We compare numerical calculations and experimental data showing that large, slow thermally-induced openings of double stranded DNA coincide with the location of functionally relevant sites for transcription. Investigating a bacteriophage DNA gene promoter segment, we find that the large opening tends to occur at the transcription start site. Other probable large openings appear to be related to other regulatory sites. Sequence specificity, nonlinearity and entropy, are the basic elements for controlling coherent dynamics. To further characterize the dynamics related to the bubble formation we investigate the temperature dependence on the dynamic structure factor. A distinct feature in the dynamics structure factor
is identified and attributed to the denaturation bubbles.
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