Classifying Candidate Axioms via Dimensionality Reduction Techniques

We assess the role of similarity measures and learning methods in classifying candidate axioms for automated schema induction through kernel-based learning algorithms. The evaluation is based on (i) three different similarity measures between axioms, and (ii) two alternative dimensionality reduction techniques to check the extent to which the considered similarities allow to separate true axioms from false axioms. The result of the dimensionality reduction process is subsequently fed to several learning algorithms, comparing the accuracy of all combinations of similarity, dimensionality reduction technique, and classification method. As a result, it is observed that it is not necessary to use sophisticated semantics-based similarity measures to obtain accurate predictions, and furthermore that classification performance only marginally depends on the choice of the learning method. Our results open the way to implementing efficient surrogate models for axiom scoring to speed up ontology learning and schema induction methods

barocco a randomized phase ii study of weekly paclitaxel vs cediranib olaparib with continuous schedule vs cediranib olaparib with intermittent schedule in advanced platinum resistant ovarian cancer

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An Evolutionary Approach to Class Disjointness Axiom Discovery

International audienceAxiom learning is an essential task in enhancing the quality of an ontology, a task that sometimes goes under the name of ontology enrichment. To overcome some limitations of recent work and to contribute to the growing library of ontology learning algorithms, we propose an evolutionary approach to automatically discover axioms from the abundant RDF data resource of the Semantic Web. We describe a method applying an instance of an Evolutionary Algorithm, namely Grammatical Evolution, to the acquisition of OWL class dis-jointness axioms, one important type of OWL axioms which makes it possible to detect logical inconsistencies and infer implicit information from a knowledge base. The proposed method uses an axiom scoring function based on possibility theory and is evaluated against a Gold Standard, manually constructed by knowledge engineers. Experimental results show that the given method possesses high accuracy and good coverage

Taxon ordering in phylogenetic trees by means of evolutionary algorithms

<p>Abstract</p> <p>Background</p> <p>In in a typical "left-to-right" phylogenetic tree, the vertical order of taxa is meaningless, as only the branch path between them reflects their degree of similarity. To make unresolved trees more informative, here we propose an innovative Evolutionary Algorithm (EA) method to search the best graphical representation of unresolved trees, in order to give a biological meaning to the vertical order of taxa.</p> <p>Methods</p> <p>Starting from a West Nile virus phylogenetic tree, in a (1 + 1)-EA we evolved it by randomly rotating the internal nodes and selecting the tree with better fitness every generation. The fitness is a sum of genetic distances between the considered taxon and the <it>r </it>(radius) next taxa. After having set the radius to the best performance, we evolved the trees with (<it>λ </it>+ <it>μ</it>)-EAs to study the influence of population on the algorithm.</p> <p>Results</p> <p>The (1 + 1)-EA consistently outperformed a random search, and better results were obtained setting the radius to 8. The (<it>λ </it>+ <it>μ</it>)-EAs performed as well as the (1 + 1), except the larger population (1000 + 1000).</p> <p>Conclusions</p> <p>The trees after the evolution showed an improvement both of the fitness (based on a genetic distance matrix, then close taxa are actually genetically close), and of the biological interpretation. Samples collected in the same state or year moved close each other, making the tree easier to interpret. Biological relationships between samples are also easier to observe.</p

Fast Evolutionary Adaptation for Monte Carlo Tree Search

This paper describes a new adaptive Monte Carlo Tree Search (MCTS) algorithm that uses evolution to rapidly optimise its performance. An evolutionary algorithm is used as a source of control parameters to modify the behaviour of each iteration (i.e. each simulation or roll-out) of the MCTS algorithm; in this paper we largely restrict this to modifying the behaviour of the random default policy, though it can also be applied to modify the tree policy

Gene expression profiling of alveolar soft-part sarcoma (ASPS)

<p>Abstract</p> <p>Background</p> <p>Alveolar soft-part sarcoma (ASPS) is an extremely rare, highly vascular soft tissue sarcoma affecting predominantly adolescents and young adults. In an attempt to gain insight into the pathobiology of this enigmatic tumor, we performed the first genome-wide gene expression profiling study.</p> <p>Methods</p> <p>For seven patients with confirmed primary or metastatic ASPS, RNA samples were isolated immediately following surgery, reverse transcribed to cDNA and each sample hybridized to duplicate high-density human U133 plus 2.0 microarrays. Array data was then analyzed relative to arrays hybridized to universal RNA to generate an unbiased transcriptome. Subsequent gene ontology analysis was used to identify transcripts with therapeutic or diagnostic potential. A subset of the most interesting genes was then validated using quantitative RT-PCR and immunohistochemistry.</p> <p>Results</p> <p>Analysis of patient array data versus universal RNA identified elevated expression of transcripts related to angiogenesis (ANGPTL2, HIF-1 alpha, MDK, c-MET, VEGF, TIMP-2), cell proliferation (PRL, IGFBP1, NTSR2, PCSK1), metastasis (ADAM9, ECM1, POSTN) and steroid biosynthesis (CYP17A1 and STS). A number of muscle-restricted transcripts (ITGB1BP3/MIBP, MYF5, MYF6 and TRIM63) were also identified, strengthening the case for a muscle cell progenitor as the origin of disease. Transcript differentials were validated using real-time PCR and subsequent immunohistochemical analysis confirmed protein expression for several of the most interesting changes (MDK, c-MET, VEGF, POSTN, CYP17A1, ITGB1BP3/MIBP and TRIM63).</p> <p>Conclusion</p> <p>Results from this first comprehensive study of ASPS gene expression identifies several targets involved in angiogenesis, metastasis and myogenic differentiation. These efforts represent the first step towards defining the cellular origin, pathogenesis and effective treatment strategies for this atypical malignancy.</p

Withanolides and related steroids

Since the isolation of the first withanolides in the mid-1960s, over 600 new members of this group of compounds have been described, with most from genera of the plant family Solanaceae. The basic structure of withaferin A, a C28 ergostane with a modified side chain forming a δ-lactone between carbons 22 and 26, was considered for many years the basic template for the withanolides. Nowadays, a considerable number of related structures are also considered part of the withanolide class; among them are those containing γ-lactones in the side chain that have come to be at least as common as the δ-lactones. The reduced versions (γ and δ-lactols) are also known. Further structural variations include modified skeletons (including C27 compounds), aromatic rings and additional rings, which may coexist in a single plant species. Seasonal and geographical variations have also been described in the concentration levels and types of withanolides that may occur, especially in the Jaborosa and Salpichroa genera, and biogenetic relationships among those withanolides may be inferred from the structural variations detected. Withania is the parent genus of the withanolides and a special section is devoted to the new structures isolated from species in this genus. Following this, all other new structures are grouped by structural types. Many withanolides have shown a variety of interesting biological activities ranging from antitumor, cytotoxic and potential cancer chemopreventive effects, to feeding deterrence for several insects as well as selective phytotoxicity towards monocotyledoneous and dicotyledoneous species. Trypanocidal, leishmanicidal, antibacterial, and antifungal activities have also been reported. A comprehensive description of the different activities and their significance has been included in this chapter. The final section is devoted to chemotaxonomic implications of withanolide distribution within the Solanaceae. Overall, this chapter covers the advances in the chemistry and biology of withanolides over the last 16 years.Fil: Misico, Rosana Isabel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Orgánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos Aplicados a la Química Orgánica (i); ArgentinaFil: Nicotra, V.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica; ArgentinaFil: Oberti, Juan Carlos María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Orgánica; ArgentinaFil: Barboza, Gloria Estela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto Multidisciplinario de Biología Vegetal (p); Argentina. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Farmacia; ArgentinaFil: Gil, Roberto Ricardo. University Of Carnegie Mellon; Estados UnidosFil: Burton, Gerardo. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Química Orgánica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Unidad de Microanálisis y Métodos Físicos Aplicados a la Química Orgánica (i); Argentin

The Hepatitis E Virus Polyproline Region Is Involved in Viral Adaptation

Genomes of hepatitis E virus (HEV), rubivirus and cutthroat virus (CTV) contain a region of high proline density and low amino acid (aa) complexity, named the polyproline region (PPR). In HEV genotypes 1, 3 and 4, it is the only region within the non-structural open reading frame (ORF1) with positive selection (4–10 codons with dN/dS>1). This region has the highest density of sites with homoplasy values >0.5. Genotypes 3 and 4 show ∼3-fold increase in homoplastic density (HD) in the PPR compared to any other region in ORF1, genotype 1 does not exhibit significant HD (p<0.0001). PPR sequence divergence was found to be 2-fold greater for HEV genotypes 3 and 4 than for genotype 1. The data suggest the PPR plays an important role in host-range adaptation. Although the PPR appears to be hypervariable and homoplastic, it retains as much phylogenetic signal as any other similar sized region in the ORF1, indicating that convergent evolution operates within the major HEV phylogenetic lineages. Analyses of sequence-based secondary structure and the tertiary structure identify PPR as an intrinsically disordered region (IDR), implicating its role in regulation of replication. The identified propensity for the disorder-to-order state transitions indicates the PPR is involved in protein-protein interactions. Furthermore, the PPR of all four HEV genotypes contains seven putative linear binding motifs for ligands involved in the regulation of a wide number of cellular signaling processes. Structure-based analysis of possible molecular functions of these motifs showed the PPR is prone to bind a wide variety of ligands. Collectively, these data suggest a role for the PPR in HEV adaptation. Particularly as an IDR, the PPR likely contributes to fine tuning of viral replication through protein-protein interactions and should be considered as a target for development of novel anti-viral drugs

Bioinformatics tools for cancer metabolomics

It is well known that significant metabolic change take place as cells are transformed from normal to malignant. This review focuses on the use of different bioinformatics tools in cancer metabolomics studies. The article begins by describing different metabolomics technologies and data generation techniques. Overview of the data pre-processing techniques is provided and multivariate data analysis techniques are discussed and illustrated with case studies, including principal component analysis, clustering techniques, self-organizing maps, partial least squares, and discriminant function analysis. Also included is a discussion of available software packages

Raw data from a cross-over controlled inpatient dietary study comparing effects of Mediterranean and high protein diets on insulin resistance and glucose variability in pre-diabetic obese women

Data used for the analyses presented in the research paper &quot;A high protein diet is more effective in improving insulin resistance and glycaemic variability compared to a Mediterranean diet - a cross-over controlled inpatient dietary study&quot;. The three uploaded files contain: - Clinica data (Rawdata_clinical.xlsx)- Continuous glucose monitoring data (Rawdata_cgm.xlsx)- 16S rRNA gene sequences data from participants&apos; stool samples collected at baseline (Rawdata_sequences_trimmed.fastq)THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV