Current Issue : January - March Volume : 2016 Issue Number : 1 Articles : 5 Articles
The genetic microarrays give to researchers a huge amount of data of many diseases represented\nby intensities of gene expression. In genomic medicine gene expression analysis is guided to find\nstrategies for prevention and treatment of diseases with high rate of mortality like the different\ncancers. So, genomic medicine requires the use of complex information technology. The purpose\nof our paper is to present a multi-agent system developed in order to improve gene expression\nanalysis with the automation of tasks about identification of genes involved in a cancer, and classification\nof tumors according to molecular biology. Agents that integrate the system, carry out\nreading files of intensity data of genes from microarrays, pre-processing of this information, and\nwith machine learning methods make groups of genes involved in the process of a disease as well\nas the classification of samples that could propose new subtypes of tumors difficult to identify\nbased on their morphology. Our results we prove that the multi-agent system requires a minimal\nintervention of user, and the agents generate knowledge that reduce the time and complexity of\nthe work of prevention and diagnosis, and thus allow a more effective treatment of tumors....
Background: There are numerous options available to achieve various tasks in bioinformatics, but until recently,\nthere were no tools that could systematically identify mentions of databases and tools within the literature. In this\npaper we explore the variability and ambiguity of database and software name mentions and compare dictionary\nand machine learning approaches to their identification.\nResults: Through the development and analysis of a corpus of 60 full-text documents manually annotated at the\nmention level, we report high variability and ambiguity in database and software mentions. On a test set of 25\nfull-text documents, a baseline dictionary look-up achieved an F-score of 46 %, highlighting not only variability and\nambiguity but also the extensive number of new resources introduced. A machine learning approach achieved an\nF-score of 63 % (with precision of 74 %) and 70 % (with precision of 83 %) for strict and lenient matching respectively.\nWe characterise the issues with various mention types and propose potential ways of capturing additional database\nand software mentions in the literature.\nConclusions: Our analyses show that identification of mentions of databases and tools is a challenging task that\ncannot be achieved by relying on current manually-curated resource repositories. Although machine learning shows\nimprovement and promise (primarily in precision), more contextual information needs to be taken into account to\nachieve a good degree of accuracy....
Biological networks inherently have uncertain topologies. This arises from many factors. For instance, interactions\nbetween molecules may or may not take place under varying conditions. Genetic or epigenetic mutations may also\nalter biological processes like transcription or translation. This uncertainty is often modeled by associating each\ninteraction with a probability value. Studying biological networks under this probabilistic model has already been\nshown to yield accurate and insightful analysis of interaction data. However, the problem of assigning accurate\nprobability values to interactions remains unresolved. In this paper, we present a novel method for computing\ninteraction probabilities in signaling networks based on transcription levels of genes. The transcription levels define\nthe signal reachability probability between membrane receptors and transcription factors. Our method computes the\ninteraction probabilities that minimize the gap between the observed and the computed signal reachability\nprobabilities. We evaluate our method on four signaling networks from the Kyoto Encyclopedia of Genes and\nGenomes (KEGG). For each network, we compute its edge probabilities using the gene expression profiles for seven\nmajor leukemia subtypes. We use these values to analyze how the stress induced by different leukemia subtypes\naffects signaling interactions....
Big data refers to informationalization technology for extracting valuable information\nthrough the use and analysis of large-scale data and, based on that data, deriving plans for\nresponse or predicting changes. With the development of software and devices for next\ngeneration sequencing, a vast amount of bioinformatics data has been generated recently.\nAlso, bioinformatics data based big-data technology is rising rapidly as a core technology\nby the bioinformatician, biologist and big-data scientist. KEGG pathway is bioinformatics\ndata for understanding high-level functions and utilities of the biological system. However,\nKEGG pathway analysis requires a lot of time and effort because KEGG pathways are high\nvolume and very diverse. In this paper, we proposed a network analysis and visualization\nsystem that crawl user interest KEGG pathways, construct a pathway network based on a\nhierarchy structure of pathways and visualize relations and interactions of pathways by\nclustering and selecting core pathways from the network. Finally, we construct a pathway\nnetwork collected by starting with an Alzheimer�s disease pathway and show the results on\nclustering and selecting core pathways from the pathway network....
High-throughput technologies, such as next-generation sequencing, have turned molecular biology into a\ndata-intensive discipline, requiring bioinformaticians to use high-performance computing resources and carry out\ndata management and analysis tasks on large scale. Workflow systems can be useful to simplify construction of\nanalysis pipelines that automate tasks, support reproducibility and provide measures for fault-tolerance. However,\nworkflow systems can incur significant development and administration overhead so bioinformatics pipelines are\noften still built without them. We present the experiences with workflows and workflow systems within the\nbioinformatics community participating in a series of hackathons and workshops of the EU COST action SeqAhead.\nThe organizations are working on similar problems, but we have addressed them with different strategies and\nsolutions. This fragmentation of efforts is inefficient and leads to redundant and incompatible solutions. Based on our\nexperiences we define a set of recommendations for future systems to enable efficient yet simple bioinformatics\nworkflow construction and execution....
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