518 research outputs found
Phenotypic and genotypic data integration and exploration through a web-service architecture
<p>Abstract</p> <p>Background</p> <p>Linking genotypic and phenotypic information is one of the greatest challenges of current genetics research. The definition of an Information Technology infrastructure to support this kind of studies, and in particular studies aimed at the analysis of complex traits, which require the definition of multifaceted phenotypes and the integration genotypic information to discover the most prevalent diseases, is a paradigmatic goal of Biomedical Informatics. This paper describes the use of Information Technology methods and tools to develop a system for the management, inspection and integration of phenotypic and genotypic data.</p> <p>Results</p> <p>We present the design and architecture of the Phenotype Miner, a software system able to flexibly manage phenotypic information, and its extended functionalities to retrieve genotype information from external repositories and to relate it to phenotypic data. For this purpose we developed a module to allow customized data upload by the user and a SOAP-based communications layer to retrieve data from existing biomedical knowledge management tools. In this paper we also demonstrate the system functionality by an example application of the system in which we analyze two related genomic datasets.</p> <p>Conclusion</p> <p>In this paper we show how a comprehensive, integrated and automated workbench for genotype and phenotype integration can facilitate and improve the hypothesis generation process underlying modern genetic studies.</p
An automated reasoning framework for translational research
AbstractIn this paper we propose a novel approach to the design and implementation of knowledge-based decision support systems for translational research, specifically tailored to the analysis and interpretation of data from high-throughput experiments. Our approach is based on a general epistemological model of the scientific discovery process that provides a well-founded framework for integrating experimental data with preexisting knowledge and with automated inference tools.In order to demonstrate the usefulness and power of the proposed framework, we present its application to Genome-Wide Association Studies, and we use it to reproduce a portion of the initial analysis performed on the well-known WTCCC dataset. Finally, we describe a computational system we are developing, aimed at assisting translational research. The system, based on the proposed model, will be able to automatically plan and perform knowledge discovery steps, to keep track of the inferences performed, and to explain the obtained results
Filtering and Mapping Public Health Data with an Innovative Kriging Approach, Accounting for Single Observation Variance
AbstractThe main scope of the paper is performing appropriate kriging interpolation of the diabetes prevalence data coming from the Pavia (Italy) Local Health Care Agency (ASL). The original dataset is analyzed, the Bayesian regularization is evaluated, which is applied by other authors and finally prevalence data are simulated by means of random fields, in order to tune and evaluate kriging interpolation
Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes
Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998
A Hybrid Input-Output Approach to Model Metabolic Systems: An Application to Intracellular Thiamine Kinetics
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Dynamic Assessment of Personal Exposure to Air Pollution for Everyone: a Smartphone-Based Approach
Abstract. In Epidemiology, exposure assessment is the process of measuring or estimating the intensity of human exposures to an environmental agent such as air pollution. Healthcare agencies typically take into consideration yearly averaged pollution values and apply them to all citizens, in risk models. However distinct parts of cities can have significantly different levels of pollution and individual habits can influence exposure, too. Consequently, in epidemiology and public health, there is an increasing interest for personal exposure assessment, i.e. the capability of measuring the exposure of individuals. Within the EU H2020 PULSE project, an innovative mechanism for the individual and dynamic assessment of exposure to air pollution has been implemented. The present paper illustrates its technological and scientific components. The system has already been deployed to several pilot cities of the project and Pavia, Italy, has been the first one. In that city several hundreds of tracks have already been acquired and processed. Therefore, the paper thoroughly illustrates the assessment procedure with examples
Phenotype forecasting with SNPs data through gene-based Bayesian networks
<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p
Phenotype forecasting with SNPs data through gene-based Bayesian networks
<p>Abstract</p> <p>Background</p> <p>Bayesian networks are powerful instruments to learn genetic models from association studies data. They are able to derive the existing correlation between genetic markers and phenotypic traits and, at the same time, to find the relationships between the markers themselves. However, learning Bayesian networks is often non-trivial due to the high number of variables to be taken into account in the model with respect to the instances of the dataset. Therefore, it becomes very interesting to use an abstraction of the variable space that suitably reduces its dimensionality without losing information. In this paper we present a new strategy to achieve this goal by mapping the SNPs related to the same gene to one meta-variable. In order to assign states to the meta-variables we employ an approach based on classification trees.</p> <p>Results</p> <p>We applied our approach to data coming from a genome-wide scan on 288 individuals affected by arterial hypertension and 271 nonagenarians without history of hypertension. After pre-processing, we focused on a subset of 24 SNPs. We compared the performance of the proposed approach with the Bayesian network learned with SNPs as variables and with the network learned with haplotypes as meta-variables. The results were obtained by running a hold-out experiment five times. The mean accuracy of the new method was 64.28%, while the mean accuracy of the SNPs network was 58.99% and the mean accuracy of the haplotype network was 54.57%.</p> <p>Conclusion</p> <p>The new approach presented in this paper is able to derive a gene-based predictive model based on SNPs data. Such model is more parsimonious than the one based on single SNPs, while preserving the capability of highlighting predictive SNPs configurations. The prediction performance of this approach was consistently superior to the SNP-based and the haplotype-based one in all the test sets of the evaluation procedure. The method can be then considered as an alternative way to analyze the data coming from association studies.</p
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