Metrics for GO based protein semantic similarity: a systematic evaluation

<p>Abstract</p> <p>Background</p> <p>Several semantic similarity measures have been applied to gene products annotated with Gene Ontology terms, providing a basis for their functional comparison. However, it is still unclear which is the best approach to semantic similarity in this context, since there is no conclusive evaluation of the various measures. Another issue, is whether electronic annotations should or not be used in semantic similarity calculations.</p> <p>Results</p> <p>We conducted a systematic evaluation of GO-based semantic similarity measures using the relationship with sequence similarity as a means to quantify their performance, and assessed the influence of electronic annotations by testing the measures in the presence and absence of these annotations. We verified that the relationship between semantic and sequence similarity is not linear, but can be well approximated by a rescaled Normal cumulative distribution function. Given that the majority of the semantic similarity measures capture an identical behaviour, but differ in resolution, we used the latter as the main criterion of evaluation.</p> <p>Conclusions</p> <p>This work has provided a basis for the comparison of several semantic similarity measures, and can aid researchers in choosing the most adequate measure for their work. We have found that the hybrid <it>simGIC</it> was the measure with the best overall performance, followed by Resnik's measure using a best-match average combination approach. We have also found that the average and maximum combination approaches are problematic since both are inherently influenced by the number of terms being combined. We suspect that there may be a direct influence of data circularity in the behaviour of the results including electronic annotations, as a result of functional inference from sequence similarity.</p

Finding New Genes for Non-Syndromic Hearing Loss through an In Silico Prioritization Study

At present, 51 genes are already known to be responsible for Non-Syndromic hereditary Hearing Loss (NSHL), but the knowledge of 121 NSHL-linked chromosomal regions brings to the hypothesis that a number of disease genes have still to be uncovered. To help scientists to find new NSHL genes, we built a gene-scoring system, integrating Gene Ontology, NCBI Gene and Map Viewer databases, which prioritizes the candidate genes according to their probability to cause NSHL. We defined a set of candidates and measured their functional similarity with respect to the disease gene set, computing a score () that relies on the assumption that functionally related genes might contribute to the same (disease) phenotype. A Kolmogorov-Smirnov test, comparing the pair-wise distribution on the disease gene set with the distribution on the remaining human genes, provided a statistical assessment of this assumption. We found at a p-value that the former pair-wise is greater than the latter, justifying a prioritization strategy based on the functional similarity of candidate genes respect to the disease gene set. A cross-validation test measured to what extent the ranking for NSHL is different from a random ordering: adding 15% of the disease genes to the candidate gene set, the ranking of the disease genes in the first eight positions resulted statistically different from a hypergeometric distribution with a p-value and a power. The twenty top-scored genes were finally examined to evaluate their possible involvement in NSHL. We found that half of them are known to be expressed in human inner ear or cochlea and are mainly involved in remodeling and organization of actin formation and maintenance of the cilia and the endocochlear potential. These findings strongly indicate that our metric was able to suggest excellent NSHL candidates to be screened in patients and controls for causative mutations

The Future Interaction of Science and Innovation Policy for Climate Change and National Security

Atlanta Conference on Science and Innovation Policy 2009This presentation was part of the session : Methods, Measures, and DataThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Recent efforts to characterize the interactions among climate change and national security issues raise challenges of relating disparate bodies of scientific (both physical and social) knowledge as well as determining the role of innovation in meeting these challenges. Technological innovation has been called for to combat climate change, increase food production, and discover new ways of generating energy, and proposals for increased investments in R&D and technology deployment are to be met with everywhere. However, such policy decisions in one domain have impacts in other domains - often unexpected, often negative, but often capable of being addressed in planning stages. This technological tool allows its users to embody the knowledge of different domains, to keep that knowledge up to date, and to define relationships, via both a model and an analytic game, such that policy makers can foresee problems and plan to forestall or mitigate them. Capturing and dynamically updating knowledge is the accomplishment of the Knowledge Encapsulation Framework. A systems dynamic model, created in STELLA®, simulates the relationships among different domains, so that relevant knowledge is applied to a seemingly independent issue. An analytic game provides a method to use that knowledge as it might be used in real-world settings.United States. Dept. of Energ

Multi-stage gas separation membrane processes used in post-combustion capture: energetic and economic analysis

Using CO2/N-2 gas separation membranes for post-combustion capture, the most important problem is how to create the driving force efficiently because the feed flue gas has only ambient pressure and a relatively low CO2 content. In order to fulfill the separation target -95 mol% CO2 purity and appropriate degree of CO2 separation - multi-stage systems are necessary using feasible membranes. This paper describes a detailed parametric study for multi-stage membrane systems used in a coal-fired power plant.According to the above-mentioned boundary conditions, the investigation process was divided into two steps: (a) energy consumption and (b) capture cost analyses. In the first step, by varying the position of the compressors and vacuum pumps and recycling the flue gas to the feed side, cascade variants were developed and analyzed in detail. The cascade system was integrated in the 600 MW North RhineWestphalia reference power plant and compared with the chemical absorption process. In the second step, an economic model was developed to make a further analysis of the cascade system. A correlation was established between the membrane parameters (selectivity, permeability) and system performance (energy consumption, capture cost). (C) 2010 Elsevier B.V. All rights reserved