From Theoretical Framework To Generic Semantic Measures Library

International audienceThanks to the ever-increasing use of the Semantic Web, a growing number of entities (e.g. documents) are characterized by non-ambiguous mean-ings. Based on this characterization, entities can subsequently be compared us-ing semantic measures. A plethora of measures have been designed given their critical importance in numerous treatments relying on ontologies. However, the improvement and use of semantic measures are currently hampered by the lack of a dedicated theoretical framework and an extensive generic software solution dedicated to them. To meet these needs, this paper presents a unified theoretical framework of graph-based semantic measures, from which we developed the open source Semantic Measures Library and toolkit; a solution that paves the way for straightforward design, computation and analysis of semantic measures for both users and developers. Downloads, documentation and technical support at dedicated website http://www.semantic-measures-library.org

Analysis of the human diseasome reveals phenotype modules across common, genetic, and infectious diseases

Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text- mining approach to identify the phenotypes (signs and symptoms) associated with over 8,000 diseases. We demonstrate that our method generates phenotypes that correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that share signs and symptoms cluster together, and we use this network to identify phenotypic disease modules

SANA NetGO: A combinatorial approach to using Gene Ontology (GO) terms to score network alignments

Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure the GO similarity between two proteins in isolation, but pairs of proteins in a network alignment are not isolated: each pairing is implicitly dependent upon every other pairing via the alignment itself. Current methods fail to take into account the frequency of GO terms across the networks, and attempt to account for common GO terms in an ad hoc fashion by imposing arbitrary rules on when to "allow" GO terms based on their location in the GO hierarchy, rather than using readily available frequency information in the PPI networks themselves. Here we develop a new measure, NetGO, that naturally weighs infrequent, informative GO terms more heavily than frequent, less informative GO terms, without requiring arbitrary cutoffs. In particular, NetGO down-weights the score of frequent GO terms according to their frequency in the networks being aligned. This is a global measure applicable only to alignments, independent of pairwise GO measures, in the same sense that the edge-based EC or S3 scores are global measures of topological similarity independent of pairwise topological similarities. We demonstrate the superiority of NetGO by creating alignments of predetermined quality based on homologous pairs of nodes and show that NetGO correlates with alignment quality much better than any existing GO-based alignment measures. We also demonstrate that NetGO provides a measure of taxonomic similarity between species, consistent with existing taxonomic measures--a feature not shared with existing GO-based network alignment measures. Finally, we re-score alignments produced by almost a dozen aligners from a previous study and show that NetGO does a better job than existing measures at separating good alignments from bad ones

A temporal switch model for estimating transcriptional activity in gene expression

Motivation: The analysis and mechanistic modelling of time series gene expression data provided by techniques such as microarrays, NanoString, reverse transcription–polymerase chain reaction and advanced sequencing are invaluable for developing an understanding of the variation in key biological processes. We address this by proposing the estimation of a flexible dynamic model, which decouples temporal synthesis and degradation of mRNA and, hence, allows for transcriptional activity to switch between different states. Results: The model is flexible enough to capture a variety of observed transcriptional dynamics, including oscillatory behaviour, in a way that is compatible with the demands imposed by the quality, time-resolution and quantity of the data. We show that the timing and number of switch events in transcriptional activity can be estimated alongside individual gene mRNA stability with the help of a Bayesian reversible jump Markov chain Monte Carlo algorithm. To demonstrate the methodology, we focus on modelling the wild-type behaviour of a selection of 200 circadian genes of the model plant Arabidopsis thaliana. The results support the idea that using a mechanistic model to identify transcriptional switch points is likely to strongly contribute to efforts in elucidating and understanding key biological processes, such as transcription and degradation

Benchmarking some Portuguese S&T system research units: 2nd Edition

The increasing use of productivity and impact metrics for evaluation and comparison, not only of individual researchers but also of institutions, universities and even countries, has prompted the development of bibliometrics. Currently, metrics are becoming widely accepted as an easy and balanced way to assist the peer review and evaluation of scientists and/or research units, provided they have adequate precision and recall. This paper presents a benchmarking study of a selected list of representative Portuguese research units, based on a fairly complete set of parameters: bibliometric parameters, number of competitive projects and number of PhDs produced. The study aimed at collecting productivity and impact data from the selected research units in comparable conditions i.e., using objective metrics based on public information, retrievable on-line and/or from official sources and thus verifiable and repeatable. The study has thus focused on the activity of the 2003-06 period, where such data was available from the latest official evaluation. The main advantage of our study was the application of automatic tools, achieving relevant results at a reduced cost. Moreover, the results over the selected units suggest that this kind of analyses will be very useful to benchmark scientific productivity and impact, and assist peer review.Comment: 26 pages, 20 figures F. Couto, D. Faria, B. Tavares, P. Gon\c{c}alves, and P. Verissimo, Benchmarking some portuguese S\&T system research units: 2nd edition, DI/FCUL TR 13-03, Department of Informatics, University of Lisbon, February 201

Exploiting biomedical web resources: a case study

An increasing number of web resources continue to be extensively used by healthcare operators to obtain more accurate diagnostic results. In particular, health care is reaping the benefits of technological advances in genomic for facing the demand of genetic tests that allow a better comprehension of diagnostic results. Within this context, Gene Ontology (GO) is a popular and effective mean for extracting knowledge from a list of genes and evaluating their semantic similarity. This paper investigates about the potential and any limits of GO ontology as support for capturing information about a set of genes which are supposed to play a significant role in a pathological condition. In particular, we present a case study that exploits some biomedical web resources for devising several groups of functionally coherent genes and experiments about the evaluation of their semantic similarity over GO. Due to the GO structure and content, results reveal limitations that not affect the evaluation of the semantic similarity when genes exhibit simple correlations but influence the estimation of the relatedness of genes belonging to complex organizations