Genomic variation in myeloma: design, content, and initial application of the Bank On A Cure SNP Panel to detect associations with progression-free survival

<p>Abstract</p> <p>Background</p> <p>We have engaged in an international program designated the <it>Bank On A Cure</it>, which has established DNA banks from multiple cooperative and institutional clinical trials, and a platform for examining the association of genetic variations with disease risk and outcomes in multiple myeloma.</p> <p>We describe the development and content of a novel custom SNP panel that contains 3404 SNPs in 983 genes, representing cellular functions and pathways that may influence disease severity at diagnosis, toxicity, progression or other treatment outcomes. A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions. To explore SNP associations with PFS we compared SNP profiles of short term (less than 1 year, <it>n </it>= 70) versus long term progression-free survivors (greater than 3 years, <it>n </it>= 73) in two phase III clinical trials.</p> <p>Results</p> <p>Quality controls were established, demonstrating an accurate and robust screening panel for genetic variations, and some initial racial comparisons of allelic variation were done. A variety of analytical approaches, including machine learning tools for data mining and recursive partitioning analyses, demonstrated predictive value of the SNP panel in survival. While the entire SNP panel showed genotype predictive association with PFS, some SNP subsets were identified within drug response, cellular signaling and cell cycle genes.</p> <p>Conclusion</p> <p>A targeted gene approach was undertaken to develop an SNP panel that can test for associations with clinical outcomes in myeloma. The initial analysis provided some predictive power, demonstrating that genetic variations in the myeloma patient population may influence PFS.</p

Combination Methods for Automatic Document Organization

Automatic document classification and clustering are useful for a wide range of applications such as organizing Web, intranet, or portal pages into topic directories, filtering news feeds or mail, focused crawling on the Web or in intranets, and many more. This thesis presents ensemble-based meta methods for supervised learning (i.e., classification based on a small amount of hand-annotated training documents). In addition, we show how these techniques can be carried forward to clustering based on unsupervised learning (i.e., automatic structuring of document corpora without training data). The algorithms are applied in a restrictive manner, i.e., by leaving out some \u27uncertain\u27 documents (rather than assigning them to inappropriate topics or clusters with low confidence). We show how restrictive meta methods can be used to combine different document representations in the context of Web document classification and author recognition. As another application for meta methods we study the combination of difierent information sources in distributed environments, such as peer-to-peer information systems. Furthermore we address the problem of semi-supervised classification on document collections using retraining. A possible application is focused Web crawling which may start with very few, manually selected, training documents but can be enhanced by automatically adding initially unlabeled, positively classified Web pages for retraining. The results of our systematic evaluation on real world data show the viability of the proposed approaches.Automatische Dokumentklassifikation und Clustering sind für eine Vielzahl von Anwendungen von Bedeutung, wie beispielsweise Organisation von Web-, Intranet- oder Portalseiten in thematische Verzeichnisse, Filterung von Nachrichtenmeldungen oder Emails, fokussiertes Crawling im Web oder in Intranets und vieles mehr. Diese Arbeit untersucht Ensemble-basierte Metamethoden für Supervised Learning (d.h. Klassifikation basierend auf einer kleinen Anzahl von manuell annotierten Trainingsdokumenten). Weiterhin zeigen wir, wie sich diese Techniken auf Clustering basierend auf Unsupervised Learning (d.h. die automatische Strukturierung von Dokumentkorpora ohne Trainingsdaten) übertragen lassen. Dabei wenden wir die Algorithmen in restriktiver Form an, d.h. wir treffen keine Aussage über eine Teilmenge von "unsicheren" Dokumenten (anstatt sie mit niedriger Konfidenz ungeeigneten Themen oder Clustern zuzuordnen). Wir verwendenen restriktive Metamethoden um unterschiedliche Dokumentrepräsentationen, im Kontext der Klassifikation von Webdokumentem und der Autorenerkennung, miteinander zu kombinieren. Als weitere Anwendung von Metamethoden untersuchen wir die Kombination von unterschiedlichen Informationsquellen in verteilten Umgebungen wie Peer-to-Peer Informationssystemen. Weiterhin betrachten wir das Problem der Semi-Supervised Klassifikation von Dokumentsammlungen durch Retraining. Eine mögliche Anwendung ist fokussiertesWeb Crawling, wo wir mit sehr wenigen, manuell ausgewählten Trainingsdokumenten starten, die durch Hinzufugen von ursprünglich nicht klassifizierten Dokumenten ergänzt werden. Die Resultate unserer systematischen Evaluation auf realen Daten zeigen das gute Leistungsverhalten unserer Methoden