DeepSF: deep convolutional neural network for mapping protein sequences to folds

Motivation Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a tar get protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice. Results We develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein se quence into one of 1195 known folds, which is useful for both fold recognition and the study of se quence-structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and map it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding a classification accuracy of 80.4%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 77.0%. We compare our method with a top profile profile alignment method - HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 14.5%-29.1% higher than HHSearch on template-free modeling targets and 4.5%-16.7% higher on hard template-based modeling targets for top 1, 5, and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking.Comment: 28 pages, 13 figure

Speckle pattern processing by digital image correlation

Testing the method of speckle pattern processing based on the digital image correlation is carried out in the current work. Three the most widely used formulas of the correlation coefficient are tested. To determine the accuracy of the speckle pattern processing, test speckle patterns with known displacement are used. The optimal size of a speckle pattern template used for determination of correlation and corresponding the speckle pattern displacement is also considered in the work

SYSTEM IN LETTER IMAGE RECOGNITION USING ZONING FEATURE EXTRACTION AND INTEGRAL PROJECTION FEATURE EXTRACTION

Abstrack Pattern recognition system has been widely applied in pattern recognition, especially the letters image. In letter pattern recognition, feature extraction process is also required to obtain characteristics and specific feature of each image to be recognizable. There are various kinds of extraction of characteristics that can be used in the process of pattern recognition. In this research used two feature extraction of zoning and integral projection. In this study there are several stages of the process undertaken the design and implementation of systems for image object recognition alphabet capital letters A, I, U, E, O, B, C, D, F, G with a font style Arial created with Microsoft Word and printed on paper. The first stage is capturing which is the process of taking pictures the image of the letter. The second stage is the conversion of RGB image of the letter to the image intensity and image intensity will be segmented using bi-level luminance thresholding method. The next process is the labeling and filtering, followed by the process of feature extraction using zoning and integral projection methods and the results of this extraction will be recognized by the template system matching. Tests conducted on 450 images obtained letters from the image of alphabet capital letters A, I, U, E, O, B, C, D, F, G with font size 30, 40, 45, 50, 60, 65, 70, 80 , 85, 90, 100, 105, 110, 115, 120 and the distance between camera and letter image are 15 cm, 20 cm and 25 cm. In this study would also analyze the performance of template matching system for each feature extraction of test data that has previously been used as a database system and the test data outside the database system. Test results show that the template matching will work optimally if the data are used as test data, have previously been used as a database system and the system will work less than optimal if the data are used as test data outside the database system. The percentage of the template matching recognition for testing with test data that has become a database system for each feature extraction is 100% and the average percentage of template matching recognition for testing with test data outside the database system is 67.3% for couples zoning and template matching and 72.2% for couples integral projections and template matching

THE PERFORMANCE ANALYSIS OF TEMPLATE MATCHING SYSTEM IN LETTER IMAGE RECOGNITION USING ZONING FEATURE EXTRACTION AND INTEGRAL PROJECTION FEATURE EXTRACTION

Abstrack Pattern recognition system has been widely applied in pattern recognition, especially the letters image. In letter pattern recognition, feature extraction process is also required to obtain characteristics and specific feature of each image to be recognizable. There are various kinds of extraction of characteristics that can be used in the process of pattern recognition. In this research used two feature extraction of zoning and integral projection. In this study there are several stages of the process undertaken the design and implementation of systems for image object recognition alphabet capital letters A, I, U, E, O, B, C, D, F, G with a font style Arial created with Microsoft Word and printed on paper. The first stage is capturing which is the process of taking pictures the image of the letter. The second stage is the conversion of RGB image of the letter to the image intensity and image intensity will be segmented using bi-level luminance thresholding method. The next process is the labeling and filtering, followed by the process of feature extraction using zoning and integral projection methods and the results of this extraction will be recognized by the template system matching. Tests conducted on 450 images obtained letters from the image of alphabet capital letters A, I, U, E, O, B, C, D, F, G with font size 30, 40, 45, 50, 60, 65, 70, 80 , 85, 90, 100, 105, 110, 115, 120 and the distance between camera and letter image are 15 cm, 20 cm and 25 cm. In this study would also analyze the performance of template matching system for each feature extraction of test data that has previously been used as a database system and the test data outside the database system. Test results show that the template matching will work optimally if the data are used as test data, have previously been used as a database system and the system will work less than optimal if the data are used as test data outside the database system. The percentage of the template matching recognition for testing with test data that has become a database system for each feature extraction is 100% and the average percentage of template matching recognition for testing with test data outside the database system is 67.3% for couples zoning and template matching and 72.2% for couples integral projections and template matching

DeepSF: Deep convolutional neural network for mapping protein sequences to folds

Motivation Protein fold recognition is an important problem in structural bioinformatics. Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a target protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold. Only a few methods had been developed to classify protein sequences into a small number of folds due to methodological limitations, which are not generally useful in practice. Results We develop a deep 1D-convolution neural network (DeepSF) to directly classify any protein sequence into one of 1195 known folds, which is useful for both fold recognition and the study of sequence-structure relationship. Different from traditional sequence alignment (comparison) based methods, our method automatically extracts fold-related features from a protein sequence of any length and maps it to the fold space. We train and test our method on the datasets curated from SCOP1.75, yielding an average classification accuracy of 75.3%. On the independent testing dataset curated from SCOP2.06, the classification accuracy is 73.0%. We compare our method with a top profile-profile alignment method -HHSearch on hard template-based and template-free modeling targets of CASP9-12 in terms of fold recognition accuracy. The accuracy of our method is 12.63-26.32% higher than HHSearch on template-free modeling targets and 3.39-17.09% higher on hard template-based modeling targets for top 1, 5 and 10 predicted folds. The hidden features extracted from sequence by our method is robust against sequence mutation, insertion, deletion and truncation, and can be used for other protein pattern recognition problems such as protein clustering, comparison and ranking

FACE IMAGE RECOGNITION BASED ON PARTIAL FACE MATCHING USING GENETIC ALGORITHM

In various real-world face recognition applications such as forensics and surveillance, only partial face image is available. Hence, template matching and recognition are strongly needed. In this paper, a genetic algorithm to match a pattern of an image and then recognize this image by this pattern is proposed. This algorithm can use any pattern of an image such as eye, mouth or ear to recognize the image. The proposed genetic algorithm uses a small length chromosome to decrease the search space, and hence the results could be obtained in a short time. Two datasets were used to test the proposed method which are AR Face database and LFW database of face, the overall matching and recognition accuracy were calculated based on conducting sequences of experiments on random sub-datasets, where the overall matching and recognition accuracy was 91.7% and 90% respectively. The results of the proposed algorithm demonstrate the robustness and efficiency compared with other state-of-the-art algorithm