2,415 research outputs found

    Candidate gene prioritization by network analysis of differential expression using machine learning approaches

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    <p>Abstract</p> <p>Background</p> <p>Discovering novel disease genes is still challenging for diseases for which no prior knowledge - such as known disease genes or disease-related pathways - is available. Performing genetic studies frequently results in large lists of candidate genes of which only few can be followed up for further investigation. We have recently developed a computational method for constitutional genetic disorders that identifies the most promising candidate genes by replacing prior knowledge by experimental data of differential gene expression between affected and healthy individuals.</p> <p>To improve the performance of our prioritization strategy, we have extended our previous work by applying different machine learning approaches that identify promising candidate genes by determining whether a gene is surrounded by highly differentially expressed genes in a functional association or protein-protein interaction network.</p> <p>Results</p> <p>We have proposed three strategies scoring disease candidate genes relying on network-based machine learning approaches, such as kernel ridge regression, heat kernel, and Arnoldi kernel approximation. For comparison purposes, a local measure based on the expression of the direct neighbors is also computed. We have benchmarked these strategies on 40 publicly available knockout experiments in mice, and performance was assessed against results obtained using a standard procedure in genetics that ranks candidate genes based solely on their differential expression levels (<it>Simple Expression Ranking</it>). Our results showed that our four strategies could outperform this standard procedure and that the best results were obtained using the <it>Heat Kernel Diffusion Ranking </it>leading to an average ranking position of 8 out of 100 genes, an AUC value of 92.3% and an error reduction of 52.8% relative to the standard procedure approach which ranked the knockout gene on average at position 17 with an AUC value of 83.7%.</p> <p>Conclusion</p> <p>In this study we could identify promising candidate genes using network based machine learning approaches even if no knowledge is available about the disease or phenotype.</p

    Direct Use of Information Extraction from Scientific Text for Modeling and Simulation in the Life Sciences

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    Purpose: To demonstrate how the information extracted from scientific text can be directly used in support of life science research projects. In modern digital-based research and academic libraries, librarians should be able to support data discovery and organization of digital entities in order to foster research projects effectively; thus we speculate that text mining and knowledge discovery tools could be of great assistance to librarians. Such tools simply enable librarians to overcome increasing complexity in the number as well as contents of scientific literature, especially in the emerging interdisciplinary fields of science. In this paper we present an example of how evidences extracted from scientific literature can be directly integrated into in silico disease models in support of drug discovery projects. Design/methodology/approach: The application of text-mining as well as knowledge discovery tools are explained in the form of a knowledge-based workflow for drug target candidate identification. Moreover, we propose an in silico experimentation framework for the enhancement of efficiency and productivity in the early steps of the drug discovery workflow. Findings: Our in silico experimentation workflow has been successfully applied to searching for hit and lead compounds in the World-wide In Silico Docking On Malaria (WISDOM) project and to finding novel inhibitor candidates. Practical implications: Direct extraction of biological information from text will ease the task of librarians in managing digital objects and supporting research projects. We expect that textual data will play an increasingly important role in evidence-based approaches taken by biomedical and translational researchers. Originality / value: Our proposed approach provides a practical example for the direct integration of text- and knowledge-based data into life science research projects, with the emphasis on its application by academic and research libraries in support of scientific projects
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