Angelo Nuzzo

Phenotype forecasting with SNPs data through gene-based Bayesian networks

BackgroundBayesian 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.ResultsWe 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%.ConclusionThe 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.

Text Mining approaches for automated literature knowledge extraction and representation.

Due to the overwhelming volume of published scientific papers, information tools for automated literature analysis are essential to support current biomedical research. We have developed a knowledge extraction tool to help researcher in discovering useful information which can support their reasoning process. The tool is composed of a search engine based on Text Mining and Natural Language Processing techniques, and an analysis module which process the search results in order to build annotation similarity networks. We tested our approach on the available knowledge about the genetic mechanism of cardiac diseases, where the target is to find both known and possible hypothetical relations between specific candidate genes and the trait of interest. We show that the system i) is able to effectively retrieve medical concepts and genes and ii) plays a relevant role assisting researchers in the formulation and evaluation of novel literature-based hypotheses.

Inferring gene regulatory networks by integrating static and dynamic data

OBJECTIVES The purpose of the paper is to propose a methodology for learning gene regulatory networks from DNA microarray data based on the integration of different data and knowledge sources. We applied our method to Saccharomyces cerevisiae experiments, focusing our attention on cell cycle regulatory mechanisms. We exploited data from deletion mutant experiments (static data), gene expression time series (dynamic data) and the knowledge encoded in the Gene Ontology. METHODS The proposed method is based on four phases. An initial gene network was derived from static data by means of a simple statistical approach. Then, the genes classified in the Gene Ontology as being involved in the cell cycle were selected. As a third step, the network structure was used to initialize a linear dynamic model of gene expression profiles. Finally, a genetic algorithm was applied to update the gene network exploiting data coming from an experiment on the yeast cell cycle. RESULTS We compared the network models provided by our approach with those obtained with a fully data-driven approach, by looking at their AIC scores and at the percentage of preserved connections in the best solutions. The results show that several nearly equivalent solutions, in terms of AIC scores, can be found. This problem is greatly mitigated by following our approach, which is able to find more robust models by fixing a portion of the network structure on the basis of prior knowledge. The best network structure was biologically evaluated on a set of 22 known cell cycle genes against independent knowledge sources. CONCLUSIONS An approach able to integrate several sources of information is needed to infer gene regulatory networks, as a fully data-driven search is in general prone to overfitting and to unidentifiability problems. The learned networks encode hypotheses on regulatory relationships that need to be verified by means of wet-lab experiments.

An automated reasoning framework for translational research

In 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.

Genephony: a knowledge management tool for genome-wide research

BackgroundOne of the consequences of the rapid and widespread adoption of high-throughput experimental technologies is an exponential increase of the amount of data produced by genome-wide experiments. Researchers increasingly need to handle very large volumes of heterogeneous data, including both the data generated by their own experiments and the data retrieved from publicly available repositories of genomic knowledge. Integration, exploration, manipulation and interpretation of data and information therefore need to become as automated as possible, since their scale and breadth are, in general, beyond the limits of what individual researchers and the basic data management tools in normal use can handle. This paper describes Genephony, a tool we are developing to address these challenges.ResultsWe describe how Genephony can be used to manage large datesets of genomic information, integrating them with existing knowledge repositories. We illustrate its functionalities with an example of a complex annotation task, in which a set of SNPs coming from a genotyping experiment is annotated with genes known to be associated to a phenotype of interest. We show how, thanks to the modular architecture of Genephony and its user-friendly interface, this task can be performed in a few simple steps.ConclusionGenephony is an online tool for the manipulation of large datasets of genomic information. It can be used as a browser for genomic data, as a high-throughput annotation tool, and as a knowledge discovery tool. It is designed to be easy to use, flexible and extensible. Its knowledge management engine provides fine-grained control over individual data elements, as well as efficient operations on large datasets.

Phenotypic and genotypic data integration and exploration through a web-service architecture

BackgroundLinking 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.ResultsWe 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.ConclusionIn 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.

Supporting Translational Research on Inherited Cardiomyopathies through Information Technology

OBJECTIVES The INHERITANCE project, funded by the European Commission, is aimed at studying genetic or inherited Dilated cardiomyopathies (DCM) and at understanding the impact and management of the disease within families that suffer from heart conditions that are caused by DCMs. The biomedical informatics research activity of the project aims at implementing information technology solutions to support the project team in the different phases of their research, in particular in genes screening prioritization and new gene-disease association discovery. METHODS In order to manage the huge quantity of scientific, clinical and patient data generated by the project several advanced biomedical informatics tools have been developed. The paper describes a layer of software instruments to support translation of the results of the project in clinical practice as well as to support the scientific discovery process. This layer includes data warehousing, intelligent querying of the phenotype data, integrated search of biological data and knowledge repositories, text mining of the relevant literature, and case based reasoning. RESULTS At the moment, a set of 1,394 patients and 9,784 observations has been stored into the INHERITANCE data warehouse. The literature database contains more than 1,100,000 articles retrieved from the Pubmed and generically related to cardiac diseases, already analyzed for extracting medical concepts and genes. CONCLUSIONS After two years of project the data warehouse has been completely set up and the text mining tools for automatic literature analysis have been implemented and tested. A first prototype of the decision support tool for knowledge discovery and gene prioritization is available, but a more complete release is still under development.

Information technology solutions to support translational research on inherited cardiomyopathies.

The INHERITANCE project, funded by the European Commission, is aimed at studying genetic or inherited Dilated cardiomyopathies (DCM) and at understanding the impact and management of the condition within families that suffer from heart conditions that are caused by DCMs. The project is supported by a number of advanced biomedical informatics tools, including data warehousing, automated literature search and decision support. The paper describes the design of these tools and the current status of implementation.

A data mining library for miRNA annotation and analysis

Understanding the key role that miRNAs play in the regulation of gene expression is one of the most important challenges in modern molecular biology. Standard gene set enrichment analysis (GSEA) is not appropriate in this context, due to the low specificity of the relation between miRNAs and their target genes. We developed alternative strategies to gain better insights in the differences in biological processes involved in different experimental conditions. We here describe a novel method to analyze and interpret miRNA expression data correctly, and demonstrate that annotating miRNA directly to biological processes through their target genes (which is nevertheless the only way possible) is a non-trivial task. We are currently employing the same strategy to relate miRNA expression patterns directly to pathway information, to generate new hypotheses, which may be relevant for the interpretation of their role in the gene expression regulatory processes.

Translational bioinformatics: challenges and opportunities for case-based reasoning and decision support

Translational bioinformatics is bioinformatics applied to human health. Although, up to now, its main focus has been to support molecular medicine research, translational bioinformatics has now the opportunity to design clinical decision support systems based on the combination of -omics data and internet-based knowledge resources. The paper describes the state-of-art of translational bioinformatics highlighting challenges and opportunities for decision support tools and case-based reasoning. It finally reports the design of a new system for supporting diagnosis in dilated cardiomyopathy. The system is able to combine text mining, literature search and case-based retrieval.