A Joint Matrix Completion and Filtering Model for Influenza Serological Data Integration
نویسندگان
چکیده
منابع مشابه
A Joint Matrix Completion and Filtering Model for Influenza Serological Data Integration
Antigenic characterization based on serological data, such as Hemagglutination Inhibition (HI) assay, is one of the routine procedures for influenza vaccine strain selection. In many cases, it would be impossible to measure all pairwise antigenic correlations between testing antigens and reference antisera in each individual experiment. Thus, we have to combine and integrate the HI tables from ...
متن کاملA Unified Joint Matrix Factorization Framework for Data Integration
Nonnegative matrix factorization (NMF) is a powerful tool in data exploratory analysis by discovering the hidden features and part-based patterns from high-dimensional data. NMF and its variants have been successfully applied into diverse fields such as pattern recognition, signal processing, data mining, bioinformatics and so on. Recently, NMF has been extended to analyze multiple matrices sim...
متن کاملStructured Matrix Completion with Applications to Genomic Data Integration.
Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of struct...
متن کاملCo-Regularized Collective Matrix Factorization for Joint Matrix Completion
Collective matrix factorization (CMF) is a popular technique for joint matrix completion. However, it relies on a strong assumption that matrices share a common low-rank structure, which may not be easily satisfied in practice. To lift this limitation, this paper introduces a novel joint matrix completion method based on a relaxed assumption. Specifically, we allow the matrix structures to be d...
متن کاملBayesian Joint Matrix Decomposition for Data Integration with Heterogeneous Noise
Matrix decomposition is a popular and fundamental approach in machine learning and data mining. It has been successfully applied into various fields. Most matrix decomposition methods focus on decomposing a data matrix from one single source. However, it is common that data are from different sources with heterogeneous noise. A few of matrix decomposition methods have been extended for such mul...
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: PLoS ONE
سال: 2013
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0069842