NDLI: Improved technique for automated classification of protein Subcellular Location patterns in fluorescence microscope images

Content Provider	IEEE Xplore Digital Library
Author	Kheirkhah, F.M. Haghipour, S.
Copyright Year	2010
Description	Author affiliation: Electronic engineering, Islamic Azad University, Tabriz Branch, Iran (Kheirkhah, F.M.) \|\| Biomedical engineering Prof., Islamic Azad University, Tabriz Branch, Iran (Haghipour, S.)
Abstract	The genomic revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. As proteins are integral components of cell function, it is critical to understand their properties such as structure and localization. Knowledge of a protein's subcellular distribution can contribute to a complete understanding of its function. Processing of subcellular image sets is still mostly manual and it causes the process inefficient and error-prone. But in recent years, try to perform high-resolution; high-throughput analysis for ten thousands of expressed proteins in the many cell types and cellular conditions under which they may be found creates. In this review, we describe a systematic approach for interpreting protein subcellular distributions using modified threshold adjacency statistics (MTAS) set of Subcellular Location Features (SLF). Previous work that uses threshold adjacency statistics (TAS), introduces a set of Subcellular Location Features which are computed by counting the number of threshold pixels adjacent. But here a novel method has been used that determines a modified features set, to improve the recognition of protein subcellular location patterns in 2D fluorescence microscope images with high accuracy and high speed.
Starting Page	1
Ending Page	5
File Size	761950
Page Count	5
File Format	PDF
ISBN	9781424474837
e-ISBN	9781424474844
DOI	10.1109/ICBME.2010.5705023
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-03
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Proteins Support vector machines Protein engineering Pattern Accuracy Modified threshold adjacency statistics Subcellular location Fluorescence Classification algorithms SVM Pixel
Content Type	Text
Resource Type	Article

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