Using cellular automata images and pseudo amino acid composition to predict protein subcellular location

Abstract

The avalanche of newly found protein sequences in the post-genomic era has motivated and challenged us to develop an automated method that can rapidly and accurately predict the localization of an uncharacterized protein in cells because the knowledge thus obtained can greatly speed up the process in finding its biological functions. However, it is very difficult to establish such a desired predictor by acquiring the key statistical information buried in a pile of extremely complicated and highly variable sequences. In this paper, based on the concept of the pseudo amino acid composition (Chou, K. C. PROTEINS: Structure, Function, and Genetics, 2001, 43: 246-255), the approach of cellular automata image is introduced to cope with this problem. Many important features, which are originally hidden in the long amino acid sequences, can be clearly displayed through their cellular automata images. One of the remarkable merits by doing so is that many image recognition tools can be straightforwardly applied to the target aimed here. High success rates were observed through the self-consistency, jackknife, and independent dataset tests, respectively.

Autonomously Implemented Versatile Path Planning for Mobile Robots Based on Cellular Automata and Ant Colony

“…For these reasons, CA has been extensively used in technology, computer science, mathematics and natural science. Examples include image processing 8 , reconfigurable robots 9 , amino acid composition 10 and modeling of phenomena such as urban growth 11 , earthquakes 12 and galaxy formation 13 . It has also been used for high speed simulation of scientific models and for computational tasks (see Ref.…”

High performance set of PseAAC and sequence based descriptors for protein classification

“…For example, cellular 4 automata image Xiao et al, 2008a;Xiao et al, 2009a;Xiao et al, 2006a), complexity measure factor (Xiao et al, 2006b;Xiao et al, 2005); Grey dynamic model Xiao et al, 2008b); functional domain composition (Xiao et al, 2009b).…”

Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition

“…In an earlier paper (Chou 2000), the physicochemical distance among the 20 amino acids (Schnieder and Wrede 1994) was adopted to define PseAA. Subsequently, some investigators used complexity measure factor , some used the values derived from the cellular automata (Xiao et al 2005b(Xiao et al , 2005c(Xiao et al , 2006(Xiao et al , 2006b, some used hydrophobic and/or hydrophilic values , Feng 2002, Wang et al 2004, Gao et al 2005, Chen et al 2006, and some were through Fourier A c c e p t e d m a n u s c r i p t 3 transform (Guo et al 2006. In view of this, the author's finding might have a series of impacts to the aforementioned work.…”