NDLI: A novel sparse coding algorithm for classification of tumors based on gene expression data

Content Provider	Springer Nature Link
Author	Kolali Khormuji, Morteza Bazrafkan, Mehrosh
Copyright Year	2015
Abstract	High-dimensional genomic and proteomic data play an important role in many applications in medicine such as prognosis of diseases, diagnosis, prevention and molecular biology, to name a few. Classifying such data is a challenging task due to the various issues such as curse of dimensionality, noise and redundancy. Recently, some researchers have used the sparse representation (SR) techniques to analyze high-dimensional biological data in various applications in classification of cancer patients based on gene expression datasets. A common problem with all SR-based biological data classification methods is that they cannot utilize the topological (geometrical) structure of data. More precisely, these methods transfer the data into sparse feature space without preserving the local structure of data points. In this paper, we proposed a novel SR-based cancer classification algorithm based on gene expression data that takes into account the geometrical information of all data. Precisely speaking, we incorporate the local linear embedding algorithm into the sparse coding framework, by which we can preserve the geometrical structure of all data. For performance comparison, we applied our algorithm on six tumor gene expression datasets, by which we demonstrate that the proposed method achieves higher classification accuracy than state-of-the-art SR-based tumor classification algorithms.
Starting Page	869
Ending Page	876
Page Count	8
File Format	PDF
ISSN	01400118
Journal	Medical and Biological Engineering and Computing
Volume Number	54
Issue Number	6
e-ISSN	17410444
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-09-04
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Sparse representation Gene expression data Local linear embedding Linear classification Logistic loss function Human Physiology Biomedical Engineering Imaging Radiology Computer Applications
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering Computer Science Applications

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