Iterative class discovery and feature selection using Minimal Spanning Trees

BACKGROUND: Clustering is one of the most commonly used methods for discovering hidden structure in microarray gene expression data. Most current methods for clustering samples are based on distance metrics utilizing all genes. This has the effect of obscuring clustering in samples that may be evident only when looking at a subset of genes, because noise from irrelevant genes dominates the signal from the relevant genes in the distance calculation. RESULTS: We describe an algorithm for automatically detecting clusters of samples that are discernable only in a subset of genes. We use iteration between Minimal Spanning Tree based clustering and feature selection to remove noise genes in a step-wise manner while simultaneously sharpening the clustering. Evaluation of this algorithm on synthetic data shows that it resolves planted clusters with high accuracy in spite of noise and the presence of other clusters. It also shows a low probability of detecting spurious clusters. Testing the algorithm on some well known micro-array data-sets reveals known biological classes as well as novel clusters. CONCLUSIONS: The iterative clustering method offers considerable improvement over clustering in all genes. This method can be used to discover partitions and their biological significance can be determined by comparing with clinical correlates and gene annotations. The MATLAB(© )programs for the iterative clustering algorithm are available fro

Classification and Compression of Multi-Resolution Vectors: A Tree Structured Vector Quantizer Approach

Tree structured classifiers and quantizers have been used withgood success for problems ranging from successive refinement coding of speechand images to classification of texture, faces and radar returns. Althoughthese methods have worked well in practice there are few results on thetheoretical side. We present several existing algorithms for tree structured clustering using multi-resolution data and develop some results on their convergenceand asymptotic performance. We show that greedy growing algorithms will result in asymptoticdistortion going to zero for the case of quantizers and prove terminationin finite time for constraints on the rate. We derive an online algorithmfor the minimization of distortion. We also show that a multiscale LVQalgorithm for the design of a tree structured classifier converges to anequilibrium point of a related ordinary differential equation.Simulation results and description of several applications are used toillustrate the advantages of this approach

Regulation of Global Gene Expression in Human Loa loa Infection Is a Function of Chronicity

Infection with the filarial parasite Loa loa causes a parasite-specific downregulation of T cell responses. However, differences exist (clinical and immunologic) between patients born and living in filarial endemic regions (endemics) and those who become infected during travel or short-term residency (expatriates). T cell responses are more depressed in endemics while expatriates have more clinical “allergic-type” symptoms. In this study, we showed that these differences reflect transcriptional differences within the T cell compartment. Using microarrays, we examined global gene expression in both CD4+ and CD8+ T cells of microfilaremic endemic and expatriate patients and found differences not only ex vivo, but also to parasite and, for CD8+ cells, to nonparasite antigens. Functional analysis showed that endemic patients expressed genes linked to inflammatory disease and caspase associated cell death at homeostasis while expatriates tended to have a more activation-induced gene profile at homeostasis and a CD4+ inflammatory response to parasite antigen. Patient groups were similar in their CD4+ response to nonparasite antigen but strongly differed in their CD8+ responses, demonstrating the potential global ramifications of chronic, longstanding infection. Our study describes potential transcriptional mechanisms for the variability seen in patients with different levels of exposure to and chronicity of filarial infection

Evaluate the Key Management of Identity-Based Digital Signature To Routing In Cluster-Based Wireless Sensor Networks

Cluster-based information transmission in WSNs has been analyzed by scientists keeping in mind the end goal to accomplish the system scalability and administration, which capitalize on hub life and diminish transfer speed use by utilizing nearby coordinated effort as a part of the center of sensor nodes. We suggest two ensured and clever information Transmission (SET) conventions for CWSNs, called SET-IBS and SETIBOOS, by method for the IBS plan and the IBOOS plan, correspondingly. The key proposal of both SET-IBS and SET-IBOOS is to affirm the encrypted detected information, by be legitimate computerized marks to message parcels, which are able in correspondence and applying the key supervision for security

Performance-based assessment of rutting resistance of asphalt mixes designed for hot climate regions

In hot climate regions asphalt mixes can be designed using the classical volumetric approach supplemented by the evaluation of basic mechanical parameters. To minimize the risk of permanent deformation, composition of the mixes can be defined by selecting densely packed aggregates and low binder contents. Despite the effectiveness of such an approach, mix design systems need to be improved by including performance-based tests that focus on the evaluation of the true rutting potential of asphalt mixes. The investigation described in this paper addressed these issues by considering twelve rut-resistant asphalt mixes designed as per the requirements set in the State of Qatar. These mixes, containing neat and polymer-modified binders (PMBs), were subjected to the Hamburg Wheel-Track Test (HWTT), dynamic modulus test and flow number test. Analysis of experimental data led to tentative requirements set on the results of dry HWTTs that can be introduced in the mix design framework currently adopted in the State of Qatar. Calculation of rank correlation coefficients showed that the various tests can be employed in different conditions for the assessment of the true rutting potential of asphalt mixes

The correlated expression of COX-2 and keratin 15 in radicular cysts

The expression of cyclooxygenase-2 (COX-2) and Keratin-15 (K15) in radicular cysts (RCs) is poorly understood. Identifying the expression of these two markers may modify the current treatment of RC. The objective of this study was to evaluate the express

The prevalence of novel periodontal pathogens and bacterial complexes in Stage II generalized periodontitis based on 16S rRNA next generation sequencing

Objective: To define the subgingival microbial profile associated with Stage II generalized periodontitis using next-generation sequencing and to determine the relative abundance of novel periodontal pathogens and bacterial complexes. Methodology: Subgingival biofilm samples were collected from 80 subjects diagnosed with Stage II generalized periodontitis. Bacterial DNA was extracted, and 16S rRNA-based bacterial profiling via next-generation sequencing was carried out. The bacterial composition and diversity of microbial communities based on the age and sex of the patients were analyzed. The bacterial species were organized into groups: bacterial complexes (red, orange, purple, yellow, and green), novel periodontal pathogens, periodontal health-related species, and unclassified periodontal species. The results were analyzed and statistically evaluated. Results: The highest number of bacteria belonged to the phylum Bacteroidetes and Firmicutes. In terms of relative abundance, the orange complex represented 18.99%, novel bacterial species (Fretibacterium spp. and Saccharibacteria spp.) comprised 17.34%, periodontal health-related species accounted for 16.75% and unclassified periodontal species represented (Leptotrichia spp. and Selenomonas spp.) 15.61%. Novel periodontal pathogens had outweighed the periodontal disease-related red complex (5.3%). The one-sample z-test performed was statistically significant at p<0.05. The Beta diversity based on the unweighted UniFrac distance at the species level demonstrated a total variance of 15.77% based on age and 39.19% on sex, which was not statistically significant. Conclusion: The bacterial species corresponding to the disease-related orange complex and novel periodontal pathogens are predominant in Stage II generalized periodontitis