ePhenotyping for Abdominal Aortic Aneurysm in the Electronic Medical Records and Genomics (eMERGE) Network: Algorithm Development and Konstanz Information Miner Workflow

Structured Query Language, was used to script the algorithm utilizing “Current Procedural Terminology” and “International Classification of Diseases” codes, with demographic and encounter data to classify individuals as case, control, or excluded. The algorithm was validated using blinded manual chart review at three eMERGE Network sites and one non-eMERGE Network site.

Validation comprised evaluation of an equal number of predicted cases and controls selected at random from the algorithm predictions. After validation at the three eMERGE Network sites, the remaining eMERGE Network sites performed verification only. Finally, the algorithm was implemented as a workflow in the Konstanz Information Miner, which represented the logic graphically while retaining intermediate data for inspection at each node. The algorithm was configured to be independent of specific access to data and was exportable (without data) to other sites.

Role of In-silico methods in the identification of Novel Drugs

Drug designing and the molecular dynamic studies are lengthy, intensified and inter-disciplinary activity. Approaches like computational chemistry and molecular modeling are widely applied in the development of in-silico drug design because it is cost effective.  Currently, a vast number of software is used in drug design. Using in-silico drug designing techniques it is possible to produce active lead molecule right from the preclinical discovery stage to late stage clinical development. The lead molecules will be helpful in the selection of potent leads to cure particular diseases. In-silico methods thus are important in target identification and prediction of novel drugs.

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Sequence Features and Subset Selection Technique for the Prediction of Protein Trafficking Phenomenon in Eukaryotic Non Membrane Proteins

Protein trafficking or protein sorting is the mechanism by which a cell transports proteins to the appropriate position in the cell or outside of it. This targeting is based on the information contained in the protein. Many methods predict the sub cellular location of proteins in eukaryotes from the sequence information. However, most of these methods use a flat structure to perform prediction. In this work, we introduce ensemble methods to predict locations in the eukaryotic protein-sorting non membrane pathway hierarchically.

We used features that were extracted exclusively from full length protein sequences with feature subset selection for classification. Sequence driven features, sequence mapped features and sequence auto correlation features were tested with ensemble learners and classifier performances were compared with and without feature subset selection technique. This study shows the new features extracted from full length eukaryotic protein sequences are effective at capturing biological features among compartments in eukaryotic non membrane pathways at two levels. Feature subset selection techniques helped to reduce the time taken for building the classification model.