Search results for "Cluster Analysis"
showing 10 items of 848 documents
Phenotyping of type 2 diabetes mellitus at onset on the basis of fasting incretin tone: Results of a two-step cluster analysis.
2015
Aims/Introduction According to some authors, in type 2 diabetes there is a reduced postprandial action of glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP). However, little is known about the role of fasting incretins in glucose homeostasis. Our aim was to evaluate, through a two-step cluster analysis, the possibility of phenotyping patients with type 2 diabetes at onset on the basis of fasting GLP-1, GIP and ghrelin. Materials and Methods A total of 96 patients with type 2 diabetes within 6 months of onset (mean age 62.40 ± 6.36 years) were cross-sectionally studied. Clinical, anthropometric and metabolic parameters were evaluated. At fasting the follow…
Processing of rock core microtomography images: Using seven different machine learning algorithms
2016
The abilities of machine learning algorithms to process X-ray microtomographic rock images were determined. The study focused on the use of unsupervised, supervised, and ensemble clustering techniques, to segment X-ray computer microtomography rock images and to estimate the pore spaces and pore size diameters in the rocks. The unsupervised k-means technique gave the fastest processing time and the supervised least squares support vector machine technique gave the slowest processing time. Multiphase assemblages of solid phases (minerals and finely grained minerals) and the pore phase were found on visual inspection of the images. In general, the accuracy in terms of porosity values and pore…
Improving clustering of Web bot and human sessions by applying Principal Component Analysis
2019
View references (18) The paper addresses the problem of modeling Web sessions of bots and legitimate users (humans) as feature vectors for their use at the input of classification models. So far many different features to discriminate bots’ and humans’ navigational patterns have been considered in session models but very few studies were devoted to feature selection and dimensionality reduction in the context of bot detection. We propose applying Principal Component Analysis (PCA) to develop improved session models based on predictor variables being efficient discriminants of Web bots. The proposed models are used in session clustering, whose performance is evaluated in terms of the purity …
Class discovery from semi-structured EEG data for affective computing and personalisation
2017
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. Many approaches to recognising emotions from metrical data such as EEG signals rely on identifying a very small number of classes and to train a classifier. The interpretation of these classes varies from a single emotion such as stress [24] to features of emotional model such as valence-arousal [4]. There are two major issues here. First classification approach limits the analysis of the data within the selected classes and is also highly dependent on training data/cycles, all of which limits generalisation. Second issue is that it does not exp…
Fast dendrogram-based OTU clustering using sequence embedding
2014
Biodiversity assessment is an important step in a metagenomic processing pipeline. The biodiversity of a microbial metagenome is often estimated by grouping its 16S rRNA reads into operational taxonomic units or OTUs. These metagenomic datasets are typically large and hence require effective yet accurate computational methods for processing.In this paper, we introduce a new hierarchical clustering method called CRiSPy-Embed which aims to produce high-quality clustering results at a low computational cost. We tackle two computational issues of the current OTU hierarchical clustering approach: (1) the compute-intensive sequence alignment operation for building the distance matrix and (2) the …
A Fuzzy Logic C-Means Clustering Algorithm to Enhance Microcalcifications Clusters in Digital Mammograms
2011
The detection of microcalcifications is a hard task, since they are quite small and often poorly contrasted against the background of images. The Computer Aided Detection (CAD) systems could be very useful for breast cancer control. In this paper, we report a method to enhance microcalcifications cluster in digital mammograms. A Fuzzy Logic clustering algorithm with a set of features is used for clustering microcalcifications. The method described was tested on simulated clusters of microcalcifications, so that the location of the cluster within the breast and the exact number of microcalcifications is known.
CRiSPy-CUDA: Computing Species Richness in 16S rRNA Pyrosequencing Datasets with CUDA
2011
Pyrosequencing technologies are frequently used for sequencing the 16S rRNA marker gene for metagenomic studies of microbial communities. Computing a pairwise genetic distance matrix from the produced reads is an important but highly time consuming task. In this paper, we present a parallelized tool (called CRiSPy) for scalable pairwise genetic distance matrix computation and clustering that is based on the processing pipeline of the popular ESPRIT software package. To achieve high computational efficiency, we have designed massively parallel CUDA algorithms for pairwise k-mer distance and pairwise genetic distance computation. We have also implemented a memory-efficient sparse matrix clust…
Parallelized Clustering of Protein Structures on CUDA-Enabled GPUs
2014
Estimation of the pose in which two given molecules might bind together to form a potential complex is a crucial task in structural biology. To solve this so-called "docking problem", most algorithms initially generate large numbers of candidate poses (or decoys) which are then clustered to allow for subsequent computationally expensive evaluations of reasonable representatives. Since the number of such candidates ranges from thousands to millions, performing the clustering on standard CPUs is highly time consuming. In this paper we analyze and evaluate different approaches to parallelize the nearest neighbor chain algorithm to perform hierarchical Ward clustering of protein structures usin…
From cacti to carnivores: Improved phylotranscriptomic sampling and hierarchical homology inference provide further insight into the evolution of Car…
2017
Premise of the study The Caryophyllales contain ~12,500 species and are known for their cosmopolitan distribution, convergence of trait evolution, and extreme adaptations. Some relationships within the Caryophyllales, like those of many large plant clades, remain unclear, and phylogenetic studies often recover alternative hypotheses. We explore the utility of broad and dense transcriptome sampling across the order for resolving evolutionary relationships in Caryophyllales. Methods We generated 84 transcriptomes and combined these with 224 publicly available transcriptomes to perform a phylogenomic analysis of Caryophyllales. To overcome the computational challenge of ortholog detection in s…
Activity of the dietary flavonoid, apigenin, against multidrug-resistant tumor cells as determined by pharmacogenomics and molecular docking
2015
Apigenin is a common dietary flavonoid with considerable cytotoxic activity in vitro and in vivo. Despite many mechanistic studies, less is known about resistance factors hampering apigenin's activity. We investigated the ATP-binding cassette (ABC) transporters BCRP/ABCG2, P-glycoprotein/ABCB1 and its close relative ABCB5. Multidrug-resistant cells overexpressing these ABC transporters were not cross-resistant toward apigenin. Moreover, apigenin inhibited not only P-glycoprotein but also BCRP by increasing cellular uptake of doxorubicin and synergistic inhibition of cell viability in combination with doxorubicin or docetaxel in multidrug-resistant cells. To perform in silico molecular docki…