Lipid Metabolism and Comparative Genomics

Unilever asked the Study Group to focus on two problems. The first concerned dysregulated lipid metabolism which is a feature of many diseases including metabolic syndrome, obesity and coronary heart disease. The Study Group was asked to develop a model of the kinetics of lipoprotein metabolism between healthy and obese states incorporating the activities of key enzymes. The second concerned the use of comparative genomics in understanding and comparing metabolic networks in bacterium. Comparative genomics is a method to make inferences on the genome of a new organism using information of a previously charaterised organism. The first mathematical question is how one would quantify such a metabolic map in a statistical sense, in particular, where there are different levels of confidence for presense of different parts of the map. The next and most important question is how one can design a measurement strategy to maximise the confidence in the accuracy of the metabolic map

Lipid-induced insulin resistance in human skeletal muscle

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Ontology learning with text mining: Two use cases in lipoprotein metabolism and toxicology

Background: The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. Results: We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. Secondly we present a use case for ontology-based search for toxicological methods. Conclusions: Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking automatic term recognition results as input. Availability: The automatic term recognition method is available as web service, described at http://gopubmed4.biotec.tu- dresden.de/IdavollWebService/services/CandidateTermGeneratorService?wsd

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain-5

In relevant terms within the top retrieved terms. The chart shows the overlap within the top predicted terms with the manual evaluation. For example, from the top 10 predicted terms by Termine, 100% are relevant to lipoprotein metabolism.<p><b>Copyright information:</b></p><p>Taken from "Terminologies for text-mining; an experiment in the lipoprotein metabolism domain"</p><p>http://www.biomedcentral.com/1471-2105/9/S4/S2</p><p>BMC Bioinformatics 2008;9(Suppl 4):S2-S2.</p><p>Published online 25 Apr 2008</p><p>PMCID:PMC2367629.</p><p></p

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain-2

In relevant terms within the top retrieved terms. The chart shows the overlap within the top predicted terms with the manual evaluation. For example, from the top 10 predicted terms by Termine, 100% are relevant to lipoprotein metabolism.<p><b>Copyright information:</b></p><p>Taken from "Terminologies for text-mining; an experiment in the lipoprotein metabolism domain"</p><p>http://www.biomedcentral.com/1471-2105/9/S4/S2</p><p>BMC Bioinformatics 2008;9(Suppl 4):S2-S2.</p><p>Published online 25 Apr 2008</p><p>PMCID:PMC2367629.</p><p></p

Precision at a certain rank represents each method's capability to recognize domain relevant terms within the top retrieved terms

The chart shows the overlap within the top predicted terms with LMO. For example, from the top 20 predicted terms by TFIDF, 65% are in LMO.<p><b>Copyright information:</b></p><p>Taken from "Terminologies for text-mining; an experiment in the lipoprotein metabolism domain"</p><p>http://www.biomedcentral.com/1471-2105/9/S4/S2</p><p>BMC Bioinformatics 2008;9(Suppl 4):S2-S2.</p><p>Published online 25 Apr 2008</p><p>PMCID:PMC2367629.</p><p></p

The Winchcombe Meteorite: one year on

It was the first UK meteorite fall for 30 years. Here we gather the story of a remarkable community hunt involving pandemic precautions, social media spikes and some very lucky guinea pigs