Paper Info
Title
Dimension reduction for p53 protein recognition by using incremental partial least squares.
Abstract
As an important tumor suppressor protein, reactivating mutated p53 was found in many kinds of human cancers and that restoring active p53 would lead to tumor regression. In recent years, more and more data extracted from biophysical simulations, which makes the modelling of mutant p53 transcriptional activity suffering from the problems of huge amount of instances and high feature dimension. Incremental feature extraction is effective to facilitate analysis of large-scale data. However, most current incremental feature extraction methods are not suitable for processing big data with high feature dimension. Partial Least Squares (PLS) has been demonstrated to be an effective dimension reduction technique for classification. In this paper, we design a highly efficient and powerful algorithm named Incremental Partial Least Squares (IPLS), which conducts a two-stage extraction process. In the first stage, the PLS target function is adapted to be incremental with updating historical mean to extract the leading projection direction. In the last stage, the other projection directions are calculated through equivalence between the PLS vectors and the Krylov sequence. We compare IPLS with some state-of-the-arts incremental feature extraction methods like Incremental Principal Component Analysis, Incremental Maximum Margin Criterion and Incremental Inter-class Scatter on real p53 proteins data. Empirical results show IPLS performs better than other methods in terms of balanced classification accuracy.
Year
DOI
Venue
2013
10.1109/TNB.2014.2319234
IEEE Transactions on Nanobioscience
Keywords
Field
DocType
p53 protein,incremental maximum margin criterion,big data,mutant p53 transcriptional activity,human cancers,proteins,balanced classification accuracy,p53 protein recognition,incremental interclass scatter,tumor suppressor protein,large-scale data analysis,krylov sequence,molecular biophysics,least squares approximations,cancer,feature extraction,ipls,tumor regression,incremental feature extraction,pls target function,mutated p53,tumours,incremental learning,incremental principal component analysis,partial least squares,dimension reduction,bioinformatics,principal component analysis,incremental partial least squares,data analysis
Effective dimension,Dimensionality reduction,Regression,Pattern recognition,Computer science,Partial least squares regression,Feature extraction,Equivalence (measure theory),Artificial intelligence,Principal component analysis,Machine learning,Feature Dimension
Conference
Volume
Issue
ISSN
13
2
1558-2639
Citations 
PageRank 
References 
0
0.34
9
Authors
2
Name
Order
Citations
PageRank
Xue-qiang Zeng1767.91
Guo-Zheng Li236842.62