Browsing by Subject "Database Management Systems"

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    Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.
    (Med Phys, 2006-08) Jesneck, Jonathan Lee
    As more diagnostic testing options become available to physicians, it becomes more difficult to combine various types of medical information together in order to optimize the overall diagnosis. To improve diagnostic performance, here we introduce an approach to optimize a decision-fusion technique to combine heterogeneous information, such as from different modalities, feature categories, or institutions. For classifier comparison we used two performance metrics: The receiving operator characteristic (ROC) area under the curve [area under the ROC curve (AUC)] and the normalized partial area under the curve (pAUC). This study used four classifiers: Linear discriminant analysis (LDA), artificial neural network (ANN), and two variants of our decision-fusion technique, AUC-optimized (DF-A) and pAUC-optimized (DF-P) decision fusion. We applied each of these classifiers with 100-fold cross-validation to two heterogeneous breast cancer data sets: One of mass lesion features and a much more challenging one of microcalcification lesion features. For the calcification data set, DF-A outperformed the other classifiers in terms of AUC (p < 0.02) and achieved AUC=0.85 +/- 0.01. The DF-P surpassed the other classifiers in terms of pAUC (p < 0.01) and reached pAUC=0.38 +/- 0.02. For the mass data set, DF-A outperformed both the ANN and the LDA (p < 0.04) and achieved AUC=0.94 +/- 0.01. Although for this data set there were no statistically significant differences among the classifiers' pAUC values (pAUC=0.57 +/- 0.07 to 0.67 +/- 0.05, p > 0.10), the DF-P did significantly improve specificity versus the LDA at both 98% and 100% sensitivity (p < 0.04). In conclusion, decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.
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    ItemOpen Access
    The Chado Natural Diversity module: a new generic database schema for large-scale phenotyping and genotyping data.
    (Database : the journal of biological databases and curation, 2011-01) Jung, Sook; Menda, Naama; Redmond, Seth; Buels, Robert M; Friesen, Maren; Bendana, Yuri; Sanderson, Lacey-Anne; Lapp, Hilmar; Lee, Taein; MacCallum, Bob; Bett, Kirstin E; Cain, Scott; Clements, Dave; Mueller, Lukas A; Main, Dorrie
    Linking phenotypic with genotypic diversity has become a major requirement for basic and applied genome-centric biological research. To meet this need, a comprehensive database backend for efficiently storing, querying and analyzing large experimental data sets is necessary. Chado, a generic, modular, community-based database schema is widely used in the biological community to store information associated with genome sequence data. To meet the need to also accommodate large-scale phenotyping and genotyping projects, a new Chado module called Natural Diversity has been developed. The module strictly adheres to the Chado remit of being generic and ontology driven. The flexibility of the new module is demonstrated in its capacity to store any type of experiment that either uses or generates specimens or stock organisms. Experiments may be grouped or structured hierarchically, whereas any kind of biological entity can be stored as the observed unit, from a specimen to be used in genotyping or phenotyping experiments, to a group of species collected in the field that will undergo further lab analysis. We describe details of the Natural Diversity module, including the design approach, the relational schema and use cases implemented in several databases.
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    ItemOpen Access
    The ocean sampling day consortium.
    (Gigascience, 2015) Kopf, Anna; Bicak, Mesude; Kottmann, Renzo; Schnetzer, Julia; Kostadinov, Ivaylo; Lehmann, Katja; Fernandez-Guerra, Antonio; Jeanthon, Christian; Rahav, Eyal; Ullrich, Matthias; Wichels, Antje; Gerdts, Gunnar; Polymenakou, Paraskevi; Kotoulas, Giorgos; Siam, Rania; Abdallah, Rehab Z; Sonnenschein, Eva C; Cariou, Thierry; O'Gara, Fergal; Jackson, Stephen; Orlic, Sandi; Steinke, Michael; Busch, Julia; Duarte, Bernardo; Caçador, Isabel; Canning-Clode, João; Bobrova, Oleksandra; Marteinsson, Viggo; Reynisson, Eyjolfur; Loureiro, Clara Magalhães; Luna, Gian Marco; Quero, Grazia Marina; Löscher, Carolin R; Kremp, Anke; DeLorenzo, Marie E; Øvreås, Lise; Tolman, Jennifer; LaRoche, Julie; Penna, Antonella; Frischer, Marc; Davis, Timothy; Katherine, Barker; Meyer, Christopher P; Ramos, Sandra; Magalhães, Catarina; Jude-Lemeilleur, Florence; Aguirre-Macedo, Ma Leopoldina; Wang, Shiao; Poulton, Nicole; Jones, Scott; Collin, Rachel; Fuhrman, Jed A; Conan, Pascal; Alonso, Cecilia; Stambler, Noga; Goodwin, Kelly; Yakimov, Michael M; Baltar, Federico; Bodrossy, Levente; Van De Kamp, Jodie; Frampton, Dion Mf; Ostrowski, Martin; Van Ruth, Paul; Malthouse, Paul; Claus, Simon; Deneudt, Klaas; Mortelmans, Jonas; Pitois, Sophie; Wallom, David; Salter, Ian; Costa, Rodrigo; Schroeder, Declan C; Kandil, Mahrous M; Amaral, Valentina; Biancalana, Florencia; Santana, Rafael; Pedrotti, Maria Luiza; Yoshida, Takashi; Ogata, Hiroyuki; Ingleton, Tim; Munnik, Kate; Rodriguez-Ezpeleta, Naiara; Berteaux-Lecellier, Veronique; Wecker, Patricia; Cancio, Ibon; Vaulot, Daniel; Bienhold, Christina; Ghazal, Hassan; Chaouni, Bouchra; Essayeh, Soumya; Ettamimi, Sara; Zaid, El Houcine; Boukhatem, Noureddine; Bouali, Abderrahim; Chahboune, Rajaa; Barrijal, Said; Timinouni, Mohammed; El Otmani, Fatima; Bennani, Mohamed; Mea, Marianna; Todorova, Nadezhda; Karamfilov, Ventzislav; Ten Hoopen, Petra; Cochrane, Guy; L'Haridon, Stephane; Bizsel, Kemal Can; Vezzi, Alessandro; Lauro, Federico M; Martin, Patrick; Jensen, Rachelle M; Hinks, Jamie; Gebbels, Susan; Rosselli, Riccardo; De Pascale, Fabio; Schiavon, Riccardo; Dos Santos, Antonina; Villar, Emilie; Pesant, Stéphane; Cataletto, Bruno; Malfatti, Francesca; Edirisinghe, Ranjith; Silveira, Jorge A Herrera; Barbier, Michele; Turk, Valentina; Tinta, Tinkara; Fuller, Wayne J; Salihoglu, Ilkay; Serakinci, Nedime; Ergoren, Mahmut Cerkez; Bresnan, Eileen; Iriberri, Juan; Nyhus, Paul Anders Fronth; Bente, Edvardsen; Karlsen, Hans Erik; Golyshin, Peter N; Gasol, Josep M; Moncheva, Snejana; Dzhembekova, Nina; Johnson, Zackary; Sinigalliano, Christopher David; Gidley, Maribeth Louise; Zingone, Adriana; Danovaro, Roberto; Tsiamis, George; Clark, Melody S; Costa, Ana Cristina; El Bour, Monia; Martins, Ana M; Collins, R Eric; Ducluzeau, Anne-Lise; Martinez, Jonathan; Costello, Mark J; Amaral-Zettler, Linda A; Gilbert, Jack A; Davies, Neil; Field, Dawn; Glöckner, Frank Oliver
    Ocean Sampling Day was initiated by the EU-funded Micro B3 (Marine Microbial Biodiversity, Bioinformatics, Biotechnology) project to obtain a snapshot of the marine microbial biodiversity and function of the world's oceans. It is a simultaneous global mega-sequencing campaign aiming to generate the largest standardized microbial data set in a single day. This will be achievable only through the coordinated efforts of an Ocean Sampling Day Consortium, supportive partnerships and networks between sites. This commentary outlines the establishment, function and aims of the Consortium and describes our vision for a sustainable study of marine microbial communities and their embedded functional traits.