A semi-supervised spam mail detector

This document describes a novel semi-supervised approach to spam classification, which was successful at the ECML/PKDD 2006 spam classification challenge. A local learning method based on lazy projections was successfully combined with a variant of a standard semi-supervised learning algorithm

MANAGING VARIANT DISCREPANCY IN HEREDITARY CANCER: CLINICAL PRACTICE, BARRIERS, AND DESIRED RESOURCES

Variants are changes in the DNA whose phenotypic effects may or may not be definitively understood. Because variant interpretation is a complex process, sources sometimes disagree on the classification of a variant, which is called a variant discrepancy. This study aimed to determine the practice of genetic counselors regarding variant discrepancies and to identify the barriers to counseling a variant discrepancy in hereditary cancer genetic testing. This investigation was unique because it was the first to address variant discrepancies from a clinical point of view. An electronic survey was sent to genetic counselors in the NSGC Cancer Special Interest Group. The vast majority of counselors (93%) had seen a variant discrepancy in practice. The most commonly selected barriers to counseling a variant discrepancy were lack of data sharing (90%) and lack of a central database (76%). Most counselors responded that the ideal database would be owned by a non-profit (59%) and obtain information directly from laboratories (91%). When asked how they approached counseling sessions involving variant discrepancies, the free responses emphasized that counselors consider family history and psychosocial concerns, showing that genetic counselors tailored the session to each individual. Variant discrepancies are an ongoing concern for clinical cancer genetic counselors, as demonstrated by the fact that counselors desired further resources to aid in addressing variant discrepancies, including a centralized database (89%), guidelines from a major organization (88%), continuing education about the issue (74%) and functional studies (58%)

Quantisation conditions of the quantum Hitchin system and the real geometric Langlands correspondence

Single-valuedness of the eigenfunctions of the quantised Hitchin Hamiltonians is proposed as a natural quantisation condition. Separation of Variables can be used to relate the classification of eigenstates to the classification of projective structures with real holonomy. Using complex Fenchel-Nielsen coordinates one may reformulate the quantisation conditions in terms of the generating function for the variety of opers. These results are interpreted as a variant of the geometric Langlands correspondence.Comment: 30 pages; v2: relevant corrections, close to fina

Mixtures of Shifted Asymmetric Laplace Distributions

A mixture of shifted asymmetric Laplace distributions is introduced and used for clustering and classification. A variant of the EM algorithm is developed for parameter estimation by exploiting the relationship with the general inverse Gaussian distribution. This approach is mathematically elegant and relatively computationally straightforward. Our novel mixture modelling approach is demonstrated on both simulated and real data to illustrate clustering and classification applications. In these analyses, our mixture of shifted asymmetric Laplace distributions performs favourably when compared to the popular Gaussian approach. This work, which marks an important step in the non-Gaussian model-based clustering and classification direction, concludes with discussion as well as suggestions for future work