Cellular Automata Image Research Articles

New distance measure for comparing protein using cellular automata image.

One of the first steps in protein sequence analysis is comparing sequences to look for similarities. We propose an information theoretical distance to compare cellular automata representing protein sequences, and determine similarities. Our approach relies in a stationary Hamming distance for the evolution of the automata according to a properly chosen rule, and to build a pairwise similarity matrix and determine common ancestors among different species in a simpler and less computationally demanding computer codes when compared to other methods.

IAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types.

Predicting antimicrobial peptides (AMPs') function is an important and difficult problem, particularly when AMPs have many multiplex functions, i.e. some AMPs simultaneously have two or three functional classes. By introducing the 'CNN-BiLSTM-SVM classifier' and 'cellular automata image', a new predictor, called iAMP-CA2L, has been developed that can be used to deal with the systems containing both monofunctional and multifunctional AMPs. iAMP-CA2L is a 2-level predictor. The 1st level is to identify whether a given query peptide is an AMP or a non-AMP, while the 2nd level is to predict if it belongs to one or more functional types. As demonstration, the jackknife cross-validation was performed with iAMP-CA2L on a benchmark dataset of AMPs classified into the following 10 functional classes: (1) antibacterial peptides, (2) antiviral peptides, (3) antifungal peptides, (4) antibiofilm peptides, (5) antiparasital peptides, (6) anti-HIV peptides, (7) anticancer (antitumor) peptides, (8) chemotactic peptides, (9) anti-MRSA peptides and (10) antiendotoxin peptides, where none of AMPs included has ≥90% pairwise sequence identity to any other in the same subset. Experiments show that iAMP-CA2L has greatly improved the prediction performance compared with the existing predictors. iAMP-CA2L is freely accessible to the public at the web site http://www.jci-bioinfo.cn/ iAMP-CA2L, and the predictor program has been uploaded to https://github.com/liujin66/iAMP-CA2L.

Establishment of cellular automata image model and its application in image dimension measurement

Aiming at how to improve the efficiency of image edge detection, an image edge detection method based on least squares support vector machine (LSSVM) and cellular automata is proposed. Firstly, a new kernel function is constructed based on the Gauss radial basis kernel and polynomial kernel, which enables the LSSVM to fit the gray values of the image pixels accurately. Then, the gradient operator of the image is deduced, and the gradient value of the image is obtained by convolution with the gray value of the image. Then, the cellular automata evolves the gradient value according to the designed local rules to locate and detect the image edge. Simulation results show that the proposed edge detection algorithm is effective, and the new algorithm has higher detection performance than Sobel and Canny algorithms.

Structural classification of proteins using texture descriptors extracted from the cellular automata image.

Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods. In this work, a binary representation of protein sequences is introduced based on reduced amino acids alphabets according to surrounding hydrophobicity index. Many important features which are hidden in these long binary sequences can be clearly displayed through their cellular automata images. The extracted features from these images are used to build a classification model by support vector machine. Comparing to previous studies on the several benchmark datasets, the promising classification rates obtained by tenfold cross-validation imply that the current approach can help in revealing some inherent features deeply hidden in protein sequences and improve the quality of predicting protein structural class.

Using adaptive K-nearest neighbor algorithm and cellular automata images to predicting G-protein-coupled receptor classes

G-Protein-Coupled Receptors (GPCRs) are the largest of cell surface receptor, accounting for >1% of the human genome. They play a key role in cellular signaling networks that regulate various physiological processes. The functions of many of GPCRs are unknown, because they are difficult to crystallize and most of them will not dissolve in normal solvents. This difficulty has motivated and challenged the development of a computational method which can predict the classification of the families and subfamilies of GPCRs based on their primary sequence so as to help us classify drugs. In this paper the adaptive K-nearest neighbor algorithm and protein cellular automata image (CAI) is introduced. Based on the CAI, the complexity measure factors derived from each of the protein sequences concerned are adopted for its Pseudo amino acid composition. GPCRs were categorized into nine subtypes. The overall success rate in identifying GPCRs among their nine family classes was about 83.5%. The high success rate suggests that the adaptive K-nearest neighbor algorithm and protein CAI holds very high potential to become a useful tool for understanding the actions of drugs that target GPCRs and designing new medications with fewer side effects and greater efficacy.

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

Summary.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.

Using cellular automata to generate image representation for biological sequences

Summary.A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419–424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their “fingerprint”. It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246–255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.