GLAD: a mixed-membership model for heterogeneous tumor subtype classification
نویسندگان
چکیده
منابع مشابه
GLAD: a mixed-membership model for heterogeneous tumor subtype classification
MOTIVATION Genomic analyses of many solid cancers have demonstrated extensive genetic heterogeneity between as well as within individual tumors. However, statistical methods for classifying tumors by subtype based on genomic biomarkers generally entail an all-or-none decision, which may be misleading for clinical samples containing a mixture of subtypes and/or normal cell contamination. RESUL...
متن کاملBayesian Mixed Membership Models for Soft Classification
The paper describes and applies a fully Bayesian approach to soft classification using mixed membership models. Our model structure has assumptions on four levels: population, subject, latent variable, and sampling scheme. Population level assumptions describe the general structure of the population that is common to all subjects. Subject level assumptions specify the distribution of observable...
متن کاملA dynamic lattice model for heterogeneous materials
In this paper, the mechanical behavior of three-phase inhomogeneous materials is modeled using the meso-scale model with lattice beams for static and dynamic analyses. The Timoshenko beam theory is applied instead of the classical Euler-Bernoulli beam theory and the mechanical properties of lattice beam connection are derived based on the continuum medium using the non-local continuum theory. T...
متن کاملBayesian Mixed Membership Models for Soft Clustering and Classification
The paper describes and applies a fully Bayesian approach to soft clustering and classification using mixed membership models. Our model structure has assumptions on four levels: population, subject, latent variable, and sampling scheme. Population level assumptions describe the general structure of the population that is common to all subjects. Subject level assumptions specify the distributio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bioinformatics
سال: 2014
ISSN: 1460-2059,1367-4803
DOI: 10.1093/bioinformatics/btu618