A simultaneous reconstruction of missing data in DNA microarrays
نویسندگان
چکیده
منابع مشابه
A Simultaneous Reconstruction of Missing Data in DNA Microarrays
We suggest here a new method of the estimation of missing entries in a gene expression matrix, which is done simultaneously— i.e., the estimation of one missing entry influences the estimation of other entries. Our method is closely related to the methods and techniques used for solving inverse eigenvalue problems. 2000 Mathematical Subject Classification: 15A18, 92D10
متن کاملMissing value estimation methods for DNA microarrays
MOTIVATION Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values....
متن کاملA system for reconstruction of missing data
This paper presents a new technique for interpolating missing data in image sequences. A 3D autoregressive (AR) model is employed and a sampling based interpolator is developed in which reconstructed data is generated as a typical realization from the underlying AR process. In this way a perceptually improved result is achieved. A hierarchical gradient-based motion estimator, robust in regions ...
متن کاملIncidence of missing values in hierarchical clustering of microarrays data
Microarrays allow to determine expressed genes in a given cell type, at a given time and under particular experimental conditions. These experiments are performed on a huge scale and produce numerous data. Methods like Hierarchical Clustering (HC) [1] or Self Organizing Map [2] are often used to identify co-expressed genes. The data often contain several missing values (MV) due to experimental ...
متن کاملMissing Value Estimation in DNA Microarrays Using B-Splines
Gene expression profiles generated by the highthroughput microarray experiments are usually in the form of large matrices with high dimensionality. Unfortunately, microarray experiments can generate data sets with multiple missing values, which significantly affect the performance of subsequent statistical analysis and machine learning algorithms. Numerous imputation algorithms have been propos...
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: Linear Algebra and its Applications
سال: 2006
ISSN: 0024-3795
DOI: 10.1016/j.laa.2005.05.009