Application of a density based clustering technique on biomedical datasets

Supramolecular Peptide Nanostructures Regulate Catalytic Efficiency and Selectivity

We report three constitutionally isomeric tetrapeptides, each comprising one glutamic acid (E) residue, one histidine (H) residue, and two lysine (KS ) residues functionalized with side-chain hydrophobic S-aroylthiooxime (SATO) groups. Depending on the order of amino acids, these amphiphilic peptides self-assembled in aqueous solution into different nanostructures:nanoribbons, a mixture of nanotoroids and nanoribbons, or nanocoils. Each nanostructure catalyzed hydrolysis of a model substrate, with the nanocoils exhibiting the greatest rate enhancement and the highest enzymatic efficiency. Coarse-grained molecular dynamics simulations, analyzed with unsupervised machine learning, revealed clusters of H residues in hydrophobic pockets along the outer edge of the nanocoils, providing insight for the observed catalytic rate enhancement. Finally, all three supramolecular nanostructures catalyzed hydrolysis of the l-substrate only when a pair of enantiomeric Boc-l/d-Phe-ONp substrates were tested. This study highlights how subtle molecular-level changes can influence supramolecular nanostructures, and ultimately affect catalytic efficiency.

Data Convexity and Parameter Independent Clustering for Biomedical Datasets

In machine learning, the nature of the dataset itself such as convexity of the data point sets affects the right choice of clustering algorithm to give good performance. This brief paper first focuses on how data convexity influences the clustering performance on biomedical datasets. Then it addresses the main challenges of two well-known clustering groups which are centroid-based and density-based clustering. These techniques typically require a set of parameters to be provided by the user before the algorithms can perform well in terms of good clustering and give the optimal number of clusters. Two parameter independent clustering techniques utilizing unique neighborhood sets (UNSs) called Parameter Independent Convex Centroid-based Clustering (ConvexClust) for convex-dominated datasets and Parameter Independent Non-Convex Density-based Clustering (NonConvexClust) for nonconvex-dominated datasets are introduced. The ConvexClust and NonConvex Clust algorithms are extensively evaluated on real-world biomedical datasets. Their performances are also compared with other clustering algorithms using evaluation criteria such as SSE, entropy and purity. The results have revealed the good performance of the proposed parameter-independent clustering techniques and also shown that most of the biomedical datasets in the experiments demonstrated their tendency towards convex-dominated data point sets.

Pediatric Brain Tissue Segmentation from MRI using Clustering: a Preliminary Study

Brain Tissue Segmentation (BTS) in young children and neonates is not a trivial task due to peculiar characteristics of the developing brain. The aim of this study is to present the preliminary results of new atlas-free BTS (afBTS) algorithm of MR images for pediatric applications, based on clustering. The algorithm works on axial T1, T2 and FLAIR sequences. First, the Cerebrospinal Fluid (CSF) is identified using the Region Growing algorithm. The remaining voxels are processed with the k-means algorithm in order to separate White Matter (WM) and Grey Matter (GM). The afBTS algorithm was applied to a population of 13 neonates; the segmentations were evaluated by two expert pediatric neuroradiologists and compared with an atlas-based algorithm. The results were promising: afBTS allowed reconstruction of WM and CSF with an image quality comparable to the reference of standard while lower segmentation quality was obtained for the GM segmentation.