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» Gene set analysis using principal components
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CSDA
2006
83views more  CSDA 2006»
13 years 10 months ago
Linear grouping using orthogonal regression
A new method to detect different linear structures in a data set, called Linear Grouping Algorithm (LGA), is proposed. LGA is useful for investigating potential linear patterns in...
Stefan Van Aelst, Xiaogang Wang, Ruben H. Zamar, R...
BMCBI
2011
13 years 5 months ago
The dChip survival analysis module for microarray data
Background: Genome-wide expression signatures are emerging as potential marker for overall survival and disease recurrence risk as evidenced by recent commercialization of gene ex...
Samir B. Amin, Parantu K. Shah, Aimin Yan, Sophia ...
ICONIP
2007
14 years 6 days ago
Flexible Component Analysis for Sparse, Smooth, Nonnegative Coding or Representation
In the paper, we present a new approach to multi-way Blind Source Separation (BSS) and corresponding 3D tensor factorization that has many potential applications in neuroscience an...
Andrzej Cichocki, Anh Huy Phan, Rafal Zdunek, Liqi...
CSDA
2006
191views more  CSDA 2006»
13 years 10 months ago
Forecasting daily time series using periodic unobserved components time series models
We explore a periodic analysis in the context of unobserved components time series models that decompose time series into components of interest such as trend, seasonal and irregu...
Siem Jan Koopman, Marius Ooms
AINA
2008
IEEE
14 years 5 months ago
Missing Value Estimation for Time Series Microarray Data Using Linear Dynamical Systems Modeling
The analysis of gene expression time series obtained from microarray experiments can be effectively exploited to understand a wide range of biological phenomena from the homeostat...
Connie Phong, Raul Singh