The unforgiving pace of growth of available biological data has increased the demand for efficient and scalable paradigms, models and methodologies for automatic annotation. In this paper, we present a novel structure-based protein function prediction and structural classification method: Cutoff Scanning Matrix (CSM). CSM generates feature vectors that represent distance patterns between protein residues. These feature vectors are then used as evidence for classification. Singular value decomposition is used as a preprocessing step to reduce dimensionality and noise. The aspect of protein function considered in the present work is enzyme activity. A series of experiments was performed on datasets based on Enzyme Commission (EC) numbers and mechanistically different enzyme superfamilies as well as other datasets derived from SCOP release 1.75.

This paper addresses the problem of hosting multiple applications on a provider's virtualized multi-tier infrastructure. Building from a previous model, we design a new self-adaptive capacity management framework, which combines a two-level SLA-driven pricing model, an optimization model and an analytical queuing-based performance model to maximize the provider's business objective. Our main contributions are the more accurate multi-queue performance model, which captures application specific bottlenecks and the parallelism inherent to multi-tier platforms, as well as the solution of the extended and much more complex optimization model. Our approach is evaluated via simulation with synthetic as well as realistic workloads, in various scenarios. The results show that our solution is significantly more cost-effective, in terms of the provider's achieved revenues, than the approach it is built upon, which uses a single-resource performance model. It also significantly outperforms a multi-tier static allocation strategy for heavy and unbalanced workloads. Finally, preliminary experiments assess the applicability of our framework to virtualized environments subjected to capacity variations caused by the processing of management and security-related tasks.

Background: Singular value decomposition (SVD) is a powerful technique for information retrieval; it helps uncover relationships between elements that are notprima facierelated. SVD was initially developed to reduce the time needed for information retrieval and analysis of very large data sets in the complex internet environment. Since information retrieval from large-scale genome and proteome data sets has a similar level of complexity, SVD-based methods could also facilitate data analysis in this research area.Results: We found that SVD applied to amino acid sequences demonstrates relationships and provides a basis for producing clusters and cladograms, demonstrating evolutionary relatedness of species that correlates well with Linnaean taxonomy. The choice of a reasonable number of singular values is crucial for SVD-based studies. We found that fewer singular values are needed to produce biologically significant clusters when SVD is employed. Subsequently, we developed a method to determine the lowest number of singular values and fewest clusters needed to guarantee biological significance; this system was developed and validated by comparison with Linnaean taxonomic classification.Conclusions: By using SVD, we can reduce uncertainty concerning the appropriate rank value necessary to perform accurate information retrieval analyses. In tests, clusters that we developed with SVD perfectly matched what was expected based on Linnaean taxonomy.

Sepsis-related transcriptomic signatures may differ across tissues and clinical settings, complicating their reproducibility and biological interpretation. We analyzed three public human microarray datasets spanning blood and skeletal muscle (GSE12624, GSE13205, and GSE69063; 26, 21, and 30 participants, respectively) using regularized log-odds least-squares feature screening and repeated participant-level cross-validation, with feature selection restricted to training folds. Stable candidates were defined by selection in at least 80% of training partitions and characterized using exploratory within-cohort expression comparisons and separate functional annotations. Mean internally cross-validated areas under the receiver operating characteristic curve (AUCs) were 0.735 ± 0.080, 1.000 ± 0.000, and 0.974 ± 0.020, respectively. Eighteen genes met the stability criterion, six per dataset, but none did so in more than one dataset. In GSE13205, AQP4, CA14, CHI3L1, LGI1, MYH7B, and SLPI showed complete apparent univariate separation (AUC = 1.000; Benjamini–Hochberg-adjusted p = 2.78 × 10⁻⁵), although these comparisons were not independent of candidate selection. Functional characterization retrieved 211 distinct Gene Ontology terms, 41 gene–pathway associations, and five database-derived functional associations. These findings support context-dependent candidate characterization, rather than independent biomarker validation, and motivate evaluation in distinct clinical populations.

Neste trabalho, estruturas de proteínas são mapeadas em um vector space model para sua manipulação em ambientes que remetem às modernas máquinas de busca (Yahoo, Google etc). Instrumentos da álgebra linear junto com a regressão logística com modificações esteiam um novo algoritmo, aqui denominado Adhoc, para a busca de assinaturas de estruturas tridimensionais. Este método transcende a aplicação aqui discutida; pode ser utilizado de forma geral em outros problemas de recuperação de informação. Para a validação, testamos as ideias para classificar proteínas em seus respectivos grupos e famílias, a partir da estrutura já determinada. Os resultados apresentados são animadores e outros experimentos estão sendo providenciados.