Статті в журналах: "Classification des motifs"

1

Meranggi, Dewa Gede Trika, Novanto Yudistira, and Yuita Arum Sari. "Batik Classification Using Convolutional Neural Network with Data Improvements." JOIV : International Journal on Informatics Visualization 6, no. 1 (March 25, 2022): 6. http://dx.doi.org/10.30630/joiv.6.1.716.

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Batik is one of the Indonesian cultures that UNESCO has recognized. Batik has a variety of unique and distinctive patterns that reflect the area of origin of the batik motif. Batik motifs usually have a 'core motif' printed repeatedly on the fabric. The entry of digitization makes batik motif designs more diverse and unique. However, with so many batik motifs spread on the internet, it is difficult for ordinary people to recognize the types of batik motifs. This makes an automatic classification of batik motifs must continue to be developed. Automation of batik motif classification can be assisted with artificial intelligence. Machine learning and deep learning have produced much good performance in image recognition. In this study, we use deep learning based on a Convolutional Neural Network (CNN) to automate the classification of batik motifs. There are two datasets used in this study. The old dataset comes from a public repository with 598 data with five types of motifs. Meanwhile, the new dataset updates the old dataset by replacing the anomalous data in the old dataset with 621 data with five types of motifs. The lereng motif is changed to pisanbali due to the difficulty of obtaining the lereng motif. Each dataset was divided into three ways: original, balance patch, and patch. We used ResNet-18 architecture, which used a pre-trained model to shorten the training time. The best test results were obtained in the new dataset with the patch way of 88.88 % ±0.88, and in the old dataset, the best accuracy was found in the patch way on the test data of 66.14 % ±3.7. The data augmentation in this study did not significantly affect the accuracy because the most significant increase in accuracy is only up to 1.22%.

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Kangkachit, Thanapat, Kitsana Waiyamai, and Philippe Lenca. "Enzyme classification using reactive motifs." International Journal of Functional Informatics and Personalised Medicine 4, no. 3/4 (2014): 243. http://dx.doi.org/10.1504/ijfipm.2014.068173.

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Reddy, Ayaluri Mallikarjuna, Vakulabharanam Venkata Krishna, Lingamgunta Sumalatha, and Avuku Obulesh. "Age Classification Using Motif and Statistical Features Derived On Gradient Facial Images." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 965–76. http://dx.doi.org/10.2174/2213275912666190417151247.

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Background: Age estimation using face images has become increasingly significant in the recent years, due to diversity of potentially useful applications. Age group feature extraction, the local features, has received a great deal of attention. Objective: This paper derived a new age estimation operator called “Gradient Dual-Complete Motif Matrix (GD-CMM)” on the 3 x 3 neighborhood of gradient image. The GD-CMM divides the 3 x 3 neighborhood in to dual grids of size 2 x 2 each and on each 2 x 2 grid complete motif matrices are derived. Methods: The local features are extracted by using Motif Co-occurrence Matrix (MCM) and it is derived on 2 x 2 grid and the main disadvantage of this Motifs or Peano Scan Motifs (PSM) is they are static i.e. the initial position on a 2 x2 grid is fixed in deriving motifs, resulting with six different motifs. The advantage 3 x 3 neighborhood approaches over 2x 2 grids is the 3x3 grid identify the spatial relations among the pixels more precisely. The gradient images represent facial features more efficiently and human beings are more sensitive to gradient changes than original grey level intensities. Results: The proposed method is compared with other existing methods on FGNET, Google and scanned facial image databases. The experimental outcomes exhibited the superiority of proposed method than existing methods. Conclusion: On the GD-CMM, this paper derived co-occurrence features and machine learning classifiers are used for age group classification.

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Nepomnyashchikh, N. A. "Hagiographicals Plots and Motives on the Modern Period: On Issue of Research and Classification." Studies in Theory of Literary Plot and Narratology, no. 1 (2019): 123–38. http://dx.doi.org/10.25205/2410-7883-2019-1-123-138.

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Studying the hagiographic traditions in the literature the modern period, one faces several issues. Firstly, it is assumed that a scholar who compares the hagiographic motifs and plots to the motifs and plots of the modern period, deals with the a hypothetical complex of the already identified and described the hagiographic motifs and plots and all he has to do is to compare them to the motifs and plots of the literature works written in the later times. However, there is no yet such a thing as a complete research on all hagiographic motifs and plots. There are studies of the certain hagiographic texts and the certain types of hagiographic texts, but there is no comprehensive list or index of the hagiographic motifs and plots. Secondly, there is no uniformly accepted scholarly terminology defining what a hagiographic motif is. Can any element of a hagiographic text be considered as such? Should the motif be defined as a unit of a theme or of a plot depending on the theory a scholar chooses to base on: Veselovsky’s vs Tomashevsky’s? How is the term “topos”, which becomes more and more popular among the researchers of the hagiography, related to the hagiographic motifs and plots? Thirdly, the number of the hagiographic and literary texts as well as researches and studies on the subject is so huge that it is only possible to specify some trends of development of the hagiographic elements and traditions in the succeeding literature. The article summarizes and analyzes the existing scientific approaches to the issues of analysis, classification, typology of the hagiographic plots and some hagiographic motifs in the Russian literature of the modern period. The conclusion of the survey is that the modern literature does not need the hagiography as a sapless model; such components of the hagiographic framework as its structure, plot, character, details are transformed from typical, similar to each other, copying the model ones to the unique ones interpreted in new original ways. Usage of the hagiographic sources can not be associated with a certain single genre; the hagiographic elements can be assimilated in any narrative or even in poetry. The literature of the modern period borrows plots, motifs and other elements of the hagiography but it lacks the utilitarian role that the hagiographic texts used to play as calendar reading. Names and dates become just literary symbols, references to some significant analogues but they do not demand to treat a literary character as a hagiographic character. The main purpose of this work is to actualize the discussion on how an index of the hagiographic plots and motifs should be compiled, what a hagiographic motif and plot are, what should be considered hagiographic plots and motifs in later narratives, taking into account that not all the topoi and elements as well as rhetoric and periods, which are recognizable as the indicators of the “hagiographic tradition” in the narrative, can be defined as the motifs in the strict sense of the term.

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Xie, Wen-Jie, Rui-Qi Han, and Wei-Xing Zhou. "Time series classification based on triadic time series motifs." International Journal of Modern Physics B 33, no. 21 (August 20, 2019): 1950237. http://dx.doi.org/10.1142/s0217979219502370.

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It is of great significance to identify the characteristics of time series to quantify their similarity and classify different classes of time series. We define six types of triadic time-series motifs and investigate the motif occurrence profiles extracted from the time series. Based on triadic time series motif profiles, we further propose to estimate the similarity coefficients between different time series and classify these time series with high accuracy. We validate the method with time series generated from nonlinear dynamic systems (logistic map, chaotic logistic map, chaotic Henon map, chaotic Ikeda map, hyperchaotic generalized Henon map and hyperchaotic folded-tower map) and retrieved from the UCR Time Series Classification Archive. Our analysis shows that the proposed triadic time series motif analysis performs better than the classic dynamic time wrapping method in classifying time series for certain datasets investigated in this work.

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Petrov, A. I., C. L. Zirbel, and N. B. Leontis. "Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas." RNA 19, no. 10 (August 22, 2013): 1327–40. http://dx.doi.org/10.1261/rna.039438.113.

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Cobanoglu, M. C., Y. Saygin, and U. Sezerman. "Classification of GPCRs Using Family Specific Motifs." IEEE/ACM Transactions on Computational Biology and Bioinformatics 8, no. 6 (November 2011): 1495–508. http://dx.doi.org/10.1109/tcbb.2010.101.

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Degtyarenko, K. "Bioinorganic motifs: towards functional classification of metalloproteins." Bioinformatics 16, no. 10 (October 1, 2000): 851–64. http://dx.doi.org/10.1093/bioinformatics/16.10.851.

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Nguyen, Hai-Long, Wee-Keong Ng, and Yew-Kwong Woon. "Closed motifs for streaming time series classification." Knowledge and Information Systems 41, no. 1 (June 7, 2013): 101–25. http://dx.doi.org/10.1007/s10115-013-0662-6.

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Kari, Rabiatuadawiyah, Mohd Azhar Samin, and Rafeah Legino. "The Flora Motif as Design Identity in Local Traditional Block Batik." Environment-Behaviour Proceedings Journal 5, SI3 (December 28, 2020): 123–27. http://dx.doi.org/10.21834/ebpj.v5isi3.2542.

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This study discusses that floral motifs as the dominant traditional motifs in Malaysian block batik. In the 20th century, the block makers created any motif with purpose—the traditional block batik motifs not revealed due to lack of secure identity upon the development of high-tech modernisation. Based on the sequence of this issue, this study aims to classify the various types of local block motifs and designs. The classification base on their features using a suitable procedure. The crucial outcomes where the motifs of block batik still show the elements and innovation of the local motif identity. Keywords: Block Batik; Design; Motif; Identity eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bsby e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5iSI3.2542

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Hendrix, Donna K., Steven E. Brenner, and Stephen R. Holbrook. "RNA structural motifs: building blocks of a modular biomolecule." Quarterly Reviews of Biophysics 38, no. 3 (August 2005): 221–43. http://dx.doi.org/10.1017/s0033583506004215.

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1. Introduction 2222. What is an RNA motif? 2222.1 Sequence vs. structural motifs 2222.2 RNA structural motifs 2232.3 RNA structural elements vs. motifs 2232.4 Specific recognition motifs 2242.5 Tools for identifying and classifying elements and motifs 2263. Types of RNA structural motifs 2283.1 Helices 2283.2 Hairpin loops 2283.3 Internal loops 2303.4 Junction loops/multiloops 2303.5 Binding motifs 2323.5.1 Metal binding 2323.5.2 Natural and selected aptamers 2343.6 Tertiary interactions 2344. Future directions 2365. Acknowledgments 2396. References 239RNAs are modular biomolecules, composed largely of conserved structural subunits, or motifs. These structural motifs comprise the secondary structure of RNA and are knit together via tertiary interactions into a compact, functional, three-dimensional structure and are to be distinguished from motifs defined by sequence or function. A relatively small number of structural motifs are found repeatedly in RNA hairpin and internal loops, and are observed to be composed of a limited number of common ‘structural elements’. In addition to secondary and tertiary structure motifs, there are functional motifs specific for certain biological roles and binding motifs that serve to complex metals or other ligands. Research is continuing into the identification and classification of RNA structural motifs and is being initiated to predict motifs from sequence, to trace their phylogenetic relationships and to use them as building blocks in RNA engineering.

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Szigeti, Balázs, Ajinkya Deogade, and Barbara Webb. "Searching for motifs in the behaviour of larval Drosophila melanogaster and Caenorhabditis elegans reveals continuity between behavioural states." Journal of The Royal Society Interface 12, no. 113 (December 2015): 20150899. http://dx.doi.org/10.1098/rsif.2015.0899.

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We present a novel method for the unsupervised discovery of behavioural motifs in larval Drosophila melanogaster and Caenorhabditis elegans . A motif is defined as a particular sequence of postures that recurs frequently. The animal's changing posture is represented by an eigenshape time series, and we look for motifs in this time series. To find motifs, the eigenshape time series is segmented, and the segments clustered using spline regression. Unlike previous approaches, our method can classify sequences of unequal duration as the same motif. The behavioural motifs are used as the basis of a probabilistic behavioural annotator, the eigenshape annotator (ESA). Probabilistic annotation avoids rigid threshold values and allows classification uncertainty to be quantified. We apply eigenshape annotation to both larval Drosophila and C. elegans and produce a good match to hand annotation of behavioural states. However, we find many behavioural events cannot be unambiguously classified. By comparing the results with ESA of an artificial agent's behaviour, we argue that the ambiguity is due to greater continuity between behavioural states than is generally assumed for these organisms.

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Taylor, Dewey T., John W. Cain, Danail G. Bonchev, Stephen S. Fong, Advait A. Apte, and Lauren E. Pace. "Toward a classification of isodynamic feed-forward motifs." Journal of Biological Dynamics 4, no. 2 (July 28, 2009): 196–211. http://dx.doi.org/10.1080/17513750903144461.

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Debroutelle, Teddy, Sylvie Treuillet, Aladine Chetouani, Matthieu Exbrayat, Lionel Martin, and Sebastien Jesset. "Automatic classification of ceramic sherds with relief motifs." Journal of Electronic Imaging 26, no. 2 (March 24, 2017): 023010. http://dx.doi.org/10.1117/1.jei.26.2.023010.

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Vens, Celine, Marie-Noëlle Rosso, and Etienne G. J. Danchin. "Identifying discriminative classification-based motifs in biological sequences." Bioinformatics 27, no. 9 (March 3, 2011): 1231–38. http://dx.doi.org/10.1093/bioinformatics/btr110.

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Popenda, Mariusz, Joanna Miskiewicz, Joanna Sarzynska, Tomasz Zok, and Marta Szachniuk. "Topology-based classification of tetrads and quadruplex structures." Bioinformatics 36, no. 4 (October 7, 2019): 1129–34. http://dx.doi.org/10.1093/bioinformatics/btz738.

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Abstract Motivation Quadruplexes attract the attention of researchers from many fields of bio-science. Due to a specific structure, these tertiary motifs are involved in various biological processes. They are also promising therapeutic targets in many strategies of drug development, including anticancer and neurological disease treatment. The uniqueness and diversity of their forms cause that quadruplexes show great potential in novel biological applications. The existing approaches for quadruplex analysis are based on sequence or 3D structure features and address canonical motifs only. Results In our study, we analyzed tetrads and quadruplexes contained in nucleic acid molecules deposited in Protein Data Bank. Focusing on their secondary structure topology, we adjusted its graphical diagram and proposed new dot-bracket and arc representations. We defined the novel classification of these motifs. It can handle both canonical and non-canonical cases. Based on this new taxonomy, we implemented a method that automatically recognizes the types of tetrads and quadruplexes occurring as unimolecular structures. Finally, we conducted a statistical analysis of these motifs found in experimentally determined nucleic acid structures in relation to the new classification. Availability and implementation https://github.com/tzok/eltetrado/ Supplementary information Supplementary data are available at Bioinformatics online.

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Wang, Yin, Rudong Li, Yuhua Zhou, Zongxin Ling, Xiaokui Guo, Lu Xie, and Lei Liu. "Motif-Based Text Mining of Microbial Metagenome Redundancy Profiling Data for Disease Classification." BioMed Research International 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6598307.

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Background. Text data of 16S rRNA are informative for classifications of microbiota-associated diseases. However, the raw text data need to be systematically processed so that features for classification can be defined/extracted; moreover, the high-dimension feature spaces generated by the text data also pose an additional difficulty.Results. Here we present a Phylogenetic Tree-Based Motif Finding algorithm (PMF) to analyze 16S rRNA text data. By integrating phylogenetic rules and other statistical indexes for classification, we can effectively reduce the dimension of the large feature spaces generated by the text datasets. Using the retrieved motifs in combination with common classification methods, we can discriminate different samples of both pneumonia and dental caries better than other existing methods.Conclusions. We extend the phylogenetic approaches to perform supervised learning on microbiota text data to discriminate the pathological states for pneumonia and dental caries. The results have shown that PMF may enhance the efficiency and reliability in analyzing high-dimension text data.

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Leontis, Neocles B., and Eric Westhof. "The Annotation of RNA Motifs." Comparative and Functional Genomics 3, no. 6 (2002): 518–24. http://dx.doi.org/10.1002/cfg.213.

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The recent deluge of new RNA structures, including complete atomic-resolution views of both subunits of the ribosome, has on the one hand literally overwhelmed our individual abilities to comprehend the diversity of RNA structure, and on the other hand presented us with new opportunities for comprehensive use of RNA sequences for comparative genetic, evolutionary and phylogenetic studies. Two concepts are key to understanding RNA structure: hierarchical organization of global structure and isostericity of local interactions. Global structure changes extremely slowly, as it relies on conserved long-range tertiary interactions. Tertiary RNA–RNA and quaternary RNA–protein interactions are mediated by RNA motifs, defined as recurrent and ordered arrays of non-Watson–Crick base-pairs. A single RNA motif comprises a family of sequences, all of which can fold into the same three-dimensional structure and can mediate the same interaction(s). The chemistry and geometry of base pairing constrain the evolution of motifs in such a way that random mutations that occur within motifs are accepted or rejected insofar as they can mediate a similar ordered array of interactions. The steps involved in the analysis and annotation of RNA motifs in 3D structures are: (a) decomposition of each motif into non-Watson–Crick base-pairs; (b) geometric classification of each basepair; (c) identification of isosteric substitutions for each basepair by comparison to isostericity matrices; (d) alignment of homologous sequences using the isostericity matrices to identify corresponding positions in the crystal structure; (e) acceptance or rejection of the null hypothesis that the motif is conserved.

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WU, CATHY H., HSI-LIEN CHEN, and SHENG-CHIH CHEN. "GENE CLASSIFICATION ARTIFICIAL NEURAL SYSTEM." International Journal on Artificial Intelligence Tools 04, no. 04 (December 1995): 501–10. http://dx.doi.org/10.1142/s0218213095000255.

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A gene classification artificial neural system has been developed for rapid annotation of the molecular sequencing data being generated by the Human Genome Project. Three neural networks have been implemented, one full-scale system to classify protein sequences according to PIR (Protein Identification Resources) superfamilies, one system to classify ribosomal RNA sequences into RDP (Ribosomal Database Project) phylogenetic classes, and one pilot system to classify proteins according to Blocks motifs. The sequence encoding schema involved an n-gram hashing method to convert molecular sequences into neural input vectors, a SVD (singular value decomposition) method to compress vectors, and a term weighting method to extract motif information. The neural networks used were three-layered, feed-forward networks that employed back-propagation or counter-propagation learning paradigms. The system runs faster by one to two orders of magnitude than existing method and has a sensitivity of 85 to 100%. The gene classification artificial neural system is available on the Internet, and may be extended into a gene identification system for classifying indiscriminately sequenced DNA fragments.

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Oktarina, Dwi. "KEBERAGAMAN MOTIF DALAM CERITA RAKYAT ULAR RENGGIONG DAN PUTRI GUNUNG LABU DARI BELITUNG TIMUR: ANALISIS MOTIF MODEL STITH THOMPSON." Sirok Bastra 8, no. 1 (June 30, 2020): 35–46. http://dx.doi.org/10.37671/sb.v8i1.199.

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Kabupaten Belitung Timur yang masuk ke wilayah Provinsi Kepulauan Bangka Belitung me miliki kekayaan budaya sastra lisan, khususnya cerita rakyat yang belum banyak dikaji. Selain cerita rakyat, wilayah ini juga kaya akan pantun, syair, mantra, juga peribahasa. Penelitian ini bertujuan untuk mendeskripsikan keberagaman motif cerita rakyat dalam dua legenda, yakni “Ular Renggiong” dan “Putri Gunung Labu” berdasarkan klasifikasi Motif Indeks Stith Thompson. Kajian ini termasuk ke dalam jenis penelitian kualitatif. Metode yang digunakan adalah metode deskriptif analisis. Kedua cerita menggambarkan kondisi sosial budaya masyarakat Melayu Belitung yang masih sangat kental menjaga tradisi dan adat dalam kehidupan. Setelah dianalisis, didapatkan hasil cerita “Ular Renggiong” memiliki sembilan motif, sementara “Putri Gunung Labu” memiliki 15 motif. Hal ini menandakan keberagaman motif cerita rakyat yang ada di wilayah Belitung Timur. East Belitung Regency in Bangka Belitung Province has it cultural richness includes diversity in anything that has to do with how people live. This region has it oral literary culture, especially folklore like folktale, pantun, syair, mantras, as well as proverbs. This study is focused on the motifs in the folktale. This research aimed to describe the motifs of the folktale "Ular Renggiong" and "Putri Gunung Labu" based on Thompson motif index classification. This research is a qualitative research using descriptive analysis method. Both folktale showed the socio-cultural conditions of the Belitung’s people who are still very strong in maintaining traditions and customs in life. The story of "Ular Renggiong" has 9 motives while "Putri Gunung Labu" has 15 motifs based on the Thompson Index Motif theory. This indicates the diversity of folktale motifs in the East Belitung region.

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Kinjo, Akira R., and Haruki Nakamura. "Comprehensive Structural Classification of Ligand-Binding Motifs in Proteins." Structure 17, no. 2 (February 2009): 234–46. http://dx.doi.org/10.1016/j.str.2008.11.009.

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Ramakrishnan, Remya, M. A. Niyas, M. P. Lijina, and Mahesh Hariharan. "Distinct Crystalline Aromatic Structural Motifs: Identification, Classification, and Implications." Accounts of Chemical Research 52, no. 11 (August 26, 2019): 3075–86. http://dx.doi.org/10.1021/acs.accounts.9b00320.

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Srinivasan, Satish M., Suleyman Vural, Brian R. King, and Chittibabu Guda. "Mining for class-specific motifs in protein sequence classification." BMC Bioinformatics 14, no. 1 (2013): 96. http://dx.doi.org/10.1186/1471-2105-14-96.

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Segura, J., B. Oliva, and N. Fernandez-Fuentes. "CAPS-DB: a structural classification of helix-capping motifs." Nucleic Acids Research 40, no. D1 (October 22, 2011): D479—D485. http://dx.doi.org/10.1093/nar/gkr879.

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Andono, Pulung Nurtantio, and Eko Hari Rachmawanto. "Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 1 (February 13, 2021): 1–9. http://dx.doi.org/10.29207/resti.v5i1.2615.

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Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was used with various coastal batik motifs in Central Java. The algorithm for classification is selected Support Vector Machine (SVM) with feature extraction of the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). SVM has the advantage of grouping data with small amounts and short operation times. GLCM as an extractive feature for recognizing batik textures and LBP was chosen to do spot pattern recognition. In the experiment, we have used 160 images of batik motifs which are divided into two, namely 128 training data and 32 testing data. The accuracy results obtained from the SVM, GLCM and LBP algorithms produce 100% accuracy in polyniomial, linear and gaussian kernels with distances at GLCM 1, 3, and 5, where at a distance of 1 linear kernel is 78.1%, gaussian 93.7%. At a distance of 3 linear kernels 75%, gaussian 87.5% and at a distance of 5 linear kernels 84.3%, gaussian 87.5%. In the SVM and GLCM algorithms the resulting accuracy is at a distance of 1 with a polynomial kernel 96.8%, linear 68.7%, and gaussian 75%. At distance 3, the polynomial kernel is 100%, linear 71.8%, and gaussian 78.1%, while for distance 5, the polynomial kernel is 87.5%, linear 75%, and gaussian 81.2%.

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Alkaff, Muhammad, Husnul Khatimi, Nur Lathifah, and Yuslena Sari. "Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)." MATEC Web of Conferences 280 (2019): 05023. http://dx.doi.org/10.1051/matecconf/201928005023.

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Sasirangan is one of the traditional cloth from Indonesia. Specifically, it comes from South Borneo. It has many variations of motifs with a different meaning for each pattern. This paper proposes a prototype of Sasirangan motifs classification using four (4) type of Sasirangan motifs namely Hiris Gagatas, Gigi Haruan, Kulat Kurikit, and Hiris Pudak. We used primary data of Sasirangan images collected from Kampung Sasirangan, Banjarmasin, South Kalimantan. After that, the images are processed using Scale-Invariant Feature Transform (SIFT) to extract its features. Furthermore, the extracted features vectors obtained is classified using the Support Vector Machine (SVM). The result shows that the Scale- Invariant Feature Transform (SIFT) feature extraction with Support Vector Machine (SVM) classification able to classify Sasirangan motifs with an overall accuracy of 95%.

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Brylinski, Michał, Leszek Konieczny, Patryk Czerwonko, Wiktor Jurkowski, and Irena Roterman. "Early-Stage Folding in Proteins(In Silico)Sequence-to-Structure Relation." Journal of Biomedicine and Biotechnology 2005, no. 2 (2005): 65–79. http://dx.doi.org/10.1155/jbb.2005.65.

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A sequence-to-structure library has been created based on the complete PDB database. The tetrapeptide was selected as a unit representing a well-defined structural motif. Seven structural forms were introduced for structure classification. The early-stage folding conformations were used as the objects for structure analysis and classification. The degree of determinability was estimated for the sequence-to-structure and structure-to-sequence relations. Probability calculus and informational entropy were applied for quantitative estimation of the mutual relation between them. The structural motifs representing different forms of loops and bends were found to favor particular sequences in structure-to-sequence analysis.

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Hermoso, Antoni, Jordi Espadaler, E. Enrique Querol, Francesc X. Aviles, Michael J. E. Sternberg, Baldomero Oliva, and Narcis Fernandez-Fuentes. "Including Functional Annotations and Extending the Collection of Structural Classifications of Protein Loops (ArchDB)." Bioinformatics and Biology Insights 1 (January 2007): 117793220700100. http://dx.doi.org/10.1177/117793220700100004.

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Loops represent an important part of protein structures. The study of loop is critical for two main reasons: First, loops are often involved in protein function, stability and folding. Second, despite improvements in experimental and computational structure prediction methods, modeling the conformation of loops remains problematic. Here, we present a structural classification of loops, ArchDB, a mine of information with application in both mentioned fields: loop structure prediction and function prediction. ArchDB ( http://sbi.imim.es/archdb ) is a database of classified protein loop motifs. The current database provides four different classification sets tailored for different purposes. ArchDB-40, a loop classification derived from SCOP40, well suited for modeling common loop motifs. Since features relevant to loop structure or function can be more easily determined on well-populated clusters, we have developed ArchDB-95, a loop classification derived from SCOP95. This new classification set shows a ~40% increase in the number of subclasses, and a large 7-fold increase in the number of putative structure/function-related subclasses. We also present ArchDB-EC, a classification of loop motifs from enzymes, and ArchDB-KI, a manually annotated classification of loop motifs from kinases. Information about ligand contacts and PDB sites has been included in all classification sets. Improvements in our classification scheme are described, as well as several new database features, such as the ability to query by conserved annotations, sequence similarity, or uploading 3D coordinates of a protein. The lengths of classified loops range between 0 and 36 residues long. ArchDB offers an exhaustive sampling of loop structures. Functional information about loops and links with related biological databases are also provided. All this information and the possibility to browse/query the database through a web-server outline an useful tool with application in the comparative study of loops, the analysis of loops involved in protein function and to obtain templates for loop modeling.

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29

Shao, Ping, Yang Yang, Shengyao Xu, and Chunping Wang. "Network Embedding via Motifs." ACM Transactions on Knowledge Discovery from Data 16, no. 3 (June 30, 2022): 1–20. http://dx.doi.org/10.1145/3473911.

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Network embedding has emerged as an effective way to deal with downstream tasks, such as node classification [ 16 , 31 , 42 ]. Most existing methods leverage multi-similarities between nodes such as connectivity, which considers vertices that are closely connected to be similar and structural similarity, which is measured by assessing their relations to neighbors; while these methods only focus on static graphs. In this work, we bridge connectivity and structural similarity in a uniform representation via motifs, and consequently present an algorithm for Learning Embeddings by leveraging Motifs Of Networks (LEMON), which aims to learn embeddings for vertices and various motifs. Moreover, LEMON is inherently capable of dealing with inductive learning tasks for dynamic graphs. To validate the effectiveness and efficiency, we conduct various experiments on two real-world datasets and five public datasets from diverse domains. Through comparison with state-of-the-art baseline models, we find that LEMON achieves significant improvements in downstream tasks. We release our code on Github at https://github.com/larry2020626/LEMON.

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30

Patra, Sabyasachi, and Anjali Mohapatra. "Motif discovery in biological network using expansion tree." Journal of Bioinformatics and Computational Biology 16, no. 06 (December 2018): 1850024. http://dx.doi.org/10.1142/s0219720018500245.

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Networks are powerful representation of topological features in biological systems like protein interaction and gene regulation. In order to understand the design principles of such complex networks, the concept of network motifs emerged. Network motifs are recurrent patterns with statistical significance that can be seen as basic building blocks of complex networks. Identification of network motifs leads to many important applications, such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, protein function annotation, etc. However, identification of network motifs is challenging as it involves graph isomorphism which is computationally hard. Though this problem has been studied extensively in the literature using different computational approaches, we are far from satisfactory results. Motivated by the challenges involved in this field, an efficient and scalable network Motif Discovery algorithm based on Expansion Tree (MODET) is proposed. Pattern growth approach is used in this proposed motif-centric algorithm. Each node of the expansion tree represents a non-isomorphic pattern. The embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of the parent node through vertex addition and edge addition. Further, the proposed algorithm does not involve any graph isomorphism check and the time complexities of these processes are [Formula: see text] and [Formula: see text], respectively. The proposed algorithm has been tested on Protein–Protein Interaction (PPI) network obtained from the MINT database. The computational efficiency of the proposed algorithm outperforms most of the existing network motif discovery algorithms.

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Anggraini, Najla. "TIPOLOGI MOTIF HIAS TEMBIKAR SITUS PULAU KAMPAI, SUMATERA UTARA." Berkala Arkeologi Sangkhakala 24, no. 1 (June 9, 2021): 64–75. http://dx.doi.org/10.24832/bas.v24i1.448.

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Pottery is a human creation in the form of objects or containers made of clay which are burned at a burning temperature of 3500C-10000C. The pottery tradition began to be discovered during the cultivation period, in Indonesia pottery became known around 6000 BC, since then pottery has become one of the most important tools in human life. Pottery artifacts are often found at archaeological sites, either intact or in fragments. From the pottery data, there are several aspects that can be studied both in terms of form, decoration and function. The method used in this research is in the form of a special analysis, namely, by observing the attributes of decorative motifs on pottery at the Kampai Island Site, North Sumatra. The data used in this study were the findings of pottery from the excavation of the North Sumatra Archeology Center in 2013. The total number of pottery analyzed in total amounted to 974 shards. The purpose of this study was to determine the typology of decorative pottery motifs at the Kampai Island Site. The results of the research on the analysis of Kampai Island pottery motifs show that there are various decorative motifs so that the classification process of pottery decorative motifs is carried out which can produce several types or typologies of pottery decorative motifs in the Kampai Island Site, namely the types of motifs of lines, squares, circles, and triangles. Tembikar merupakan suatu hasil karya cipta manusia berupa benda atau wadah yang terbuat dari tanah liat yang dibakar pada suhu pembakaran 3500 C-10000 C. Tradisi tembikar mulai ditemukan pada masa bercocok tanam, di Indonesia tembikar mulai dikenal sekitar 6000 SM, yang sejak saat itu tembikar menjadi salah satu alat perlengkapan yang penting di kehidupan manusia. Artefak tembikar sering ditemukan pada situs arkeologi, baik utuh maupun pecahan. Data tembikar juga dapat diteliti dari beberapa aspek baik dari segi bentuk, hiasan maupun fungsi. Metode yang digunakan dalam penelitian ini berupa Analisis Khusus yaitu, dengan mengamati atribut motif hias pada tembikar Situs Pulau Kampai, Sumatera Utara. Data yang digunakan pada penelitian ini berupa temuan tembikar dari hasil ekskavasi Balai Arkeologi Sumatera Utara tahun 2013. Jumlah tembikar yang dianalisis secara keseluruhan berjumlah 974 pecahan. Adapun tujuan penelitian ini untuk mengetahui tipologi motif hias tembikar Situs Pulau Kampai. Hasil penelitian analisis motif hias tembikar Pulau Kampai menunjukan bahwa terdapat motif hias yang beragam sehingga dilakukan proses klasifikasi motif hias tembikar yang dapat menghasilkan beberapa tipe-tipe atau tipologi motif hias tembikar Situs Pulau Kampai, yaitu tipe motif garis, kotak, lingkaran, dan segitiga.

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32

Roy, P. M., V. Dérogis, A. Kétowobiakou, C. Roueé, and C. Barbeau. "Priorisation des malades par l'infirmiere: Classification des motifs d'admission (CMA)." Réanimation Urgences 7, no. 2 (March 1998): 122. http://dx.doi.org/10.1016/s1164-6756(98)80166-7.

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33

Wintjens, René T., Marianne J. Rooman та Shoshana J. Wodak. "Automatic Classification and Analysis of αα-Turn Motifs in Proteins". Journal of Molecular Biology 255, № 1 (січень 1996): 235–53. http://dx.doi.org/10.1006/jmbi.1996.0020.

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34

Lemieux, S. "Automated extraction and classification of RNA tertiary structure cyclic motifs." Nucleic Acids Research 34, no. 8 (April 28, 2006): 2340–46. http://dx.doi.org/10.1093/nar/gkl120.

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35

Plewczynski, Dariusz, Adrian Tkacz, Lucjan Stanisław Wyrwicz, Adam Godzik, Andrzej Kloczkowski, and Leszek Rychlewski. "Support-vector-machine classification of linear functional motifs in proteins." Journal of Molecular Modeling 12, no. 4 (December 10, 2005): 453–61. http://dx.doi.org/10.1007/s00894-005-0070-2.

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36

Seo, Sungbo, Jaewoo Kang, and Keun Ho Ryu. "Multivariable stream data classification using motifs and their temporal relations." Information Sciences 179, no. 20 (September 29, 2009): 3489–504. http://dx.doi.org/10.1016/j.ins.2009.06.036.

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37

Blokhuis, Alex, David Lacoste, and Philippe Nghe. "Universal motifs and the diversity of autocatalytic systems." Proceedings of the National Academy of Sciences 117, no. 41 (September 28, 2020): 25230–36. http://dx.doi.org/10.1073/pnas.2013527117.

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Autocatalysis is essential for the origin of life and chemical evolution. However, the lack of a unified framework so far prevents a systematic study of autocatalysis. Here, we derive, from basic principles, general stoichiometric conditions for catalysis and autocatalysis in chemical reaction networks. This allows for a classification of minimal autocatalytic motifs called cores. While all known autocatalytic systems indeed contain minimal motifs, the classification also reveals hitherto unidentified motifs. We further examine conditions for kinetic viability of such networks, which depends on the autocatalytic motifs they contain and is notably increased by internal catalytic cycles. Finally, we show how this framework extends the range of conceivable autocatalytic systems, by applying our stoichiometric and kinetic analysis to autocatalysis emerging from coupled compartments. The unified approach to autocatalysis presented in this work lays a foundation toward the building of a systems-level theory of chemical evolution.

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38

D'Ascenzo, Luigi, and Pascal Auffinger. "A comprehensive classification and nomenclature of carboxyl–carboxyl(ate) supramolecular motifs and related catemers: implications for biomolecular systems." Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials 71, no. 2 (March 24, 2015): 164–75. http://dx.doi.org/10.1107/s205252061500270x.

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Carboxyl and carboxylate groups form important supramolecular motifs (synthons). Besides carboxyl cyclic dimers, carboxyl and carboxylate groups can associate through a single hydrogen bond. Carboxylic groups can further form polymeric-like catemer chains within crystals. To date, no exhaustive classification of these motifs has been established. In this work, 17 association types were identified (13 carboxyl–carboxyl and 4 carboxyl–carboxylate motifs) by taking into account thesynandanticarboxyl conformers, as well as thesynandantilone pairs of the O atoms. From these data, a simple rule was derived stating that only eight distinct catemer motifs involving repetitive combinations ofsynandanticarboxyl groups can be formed. Examples extracted from the Cambridge Structural Database (CSD) for all identified dimers and catemers are presented, as well as statistical data related to their occurrence and conformational preferences. The inter-carboxyl(ate) and carboxyl(ate)–water hydrogen-bond properties are described, stressing the occurrence of very short (strong) hydrogen bonds. The precise characterization and classification of these supramolecular motifs should be of interest in crystal engineering, pharmaceutical and also biomolecular sciences, where similar motifs occur in the form of pairs of Asp/Glu amino acids or motifs involving ligands bearing carboxyl(ate) groups. Hence, we present data emphasizing how the analysis of hydrogen-containing small molecules of high resolution can help understand structural aspects of larger and more complex biomolecular systems of lower resolution.

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39

Patra, Sabyasachi, and Anjali Mohapatra. "Application of dynamic expansion tree for finding large network motifs in biological networks." PeerJ 7 (May 17, 2019): e6917. http://dx.doi.org/10.7717/peerj.6917.

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Network motifs play an important role in the structural analysis of biological networks. Identification of such network motifs leads to many important applications such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, and protein function annotation. However, identification of large network motifs is a challenging task as it involves the graph isomorphism problem. Although this problem has been studied extensively in the literature using different computational approaches, still there is a lot of scope for improvement. Motivated by the challenges involved in this field, an efficient and scalable network motif finding algorithm using a dynamic expansion tree is proposed. The novelty of the proposed algorithm is that it avoids computationally expensive graph isomorphism tests and overcomes the space limitation of the static expansion tree (SET) which makes it enable to find large motifs. In this algorithm, the embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of a parent node, either by adding a vertex or by adding an edge. This process does not involve any graph isomorphism check. The time complexity of vertex addition and edge addition are O(n) and O(1), respectively. The growth of a dynamic expansion tree (DET) depends on the availability of patterns in the target network. Pruning of branches in the DET significantly reduces the space requirement of the SET. The proposed algorithm has been tested on a protein–protein interaction network obtained from the MINT database. The proposed algorithm is able to identify large network motifs faster than most of the existing motif finding algorithms.

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40

Kurumatani, Natsumi, Hiroyuki Monji, and Takenao Ohkawa. "Binding Site Extraction by Similar Subgraphs Mining from Protein Molecular Surfaces and Its Application to Protein Classification." International Journal on Artificial Intelligence Tools 23, no. 03 (May 28, 2014): 1460007. http://dx.doi.org/10.1142/s0218213014600070.

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Most proteins express their functions by binding with other proteins or molecular compounds (ligands). Since the characteristics of the local portion involved in binding (binding site) often determine the function of the protein, clarifying the location of the binding site of the protein helps analyze the function of proteins. Binding sites that bind to similar ligands often have common surface structures (surface motifs). Extracting the surface motifs among several proteins with similar functions improves binding site prediction. We propose a method that predicts binding sites by extracting the surface motifs that are frequently observed in only a specific set of proteins that bind to the same ligand (group). Since most binding sites have concave structures (pockets), the pockets are compared and common structures are searched for to extract the surface motifs by applying similar graph mining to the pocket data, which are represented as graphs. Common binding sites across several groups can be predicted in such a way to integrate more than one group. We also proposed a method of protein classification, in which the surface motifs extracted using the above method are evaluated on the assumption that a protein belongs to each one of the groups.

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41

Hou, Renkui, and Chu-Ren Huang. "Robust stylometric analysis and author attribution based on tones and rimes." Natural Language Engineering 26, no. 1 (April 10, 2019): 49–71. http://dx.doi.org/10.1017/s135132491900010x.

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AbstractIn this article, we propose an innovative and robust approach to stylometric analysis without annotation and leveraging lexical and sub-lexical information. In particular, we propose to leverage the phonological information of tones and rimes in Mandarin Chinese automatically extracted from unannotated texts. The texts from different authors were represented by tones, tone motifs, and word length motifs as well as rimes and rime motifs. Support vector machines and random forests were used to establish the text classification model for authorship attribution. From the results of the experiments, we conclude that the combination of bigrams of rimes, word-final rimes, and segment-final rimes can discriminate the texts from different authors effectively when using random forests to establish the classification model. This robust approach can in principle be applied to other languages with established phonological inventory of onset and rimes.

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42

Rao, Preeti, Joe Cheri Ross, Kaustuv Kanti Ganguli, Vedhas Pandit, Vignesh Ishwar, Ashwin Bellur, and Hema A. Murthy. "Classification of Melodic Motifs in Raga Music with Time-series Matching." Journal of New Music Research 43, no. 1 (January 2, 2014): 115–31. http://dx.doi.org/10.1080/09298215.2013.873470.

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43

Winkler, H., P. Zhu, KH Roux, and KA Taylor. "Tomographic Subvolume Averaging and Subvolume Classification of SIV Envelope Spike Motifs." Microscopy and Microanalysis 14, S2 (August 2008): 1300–1301. http://dx.doi.org/10.1017/s1431927608087655.

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44

Busk, Peter Kamp, and Lene Lange. "Function-Based Classification of Carbohydrate-Active Enzymes by Recognition of Short, Conserved Peptide Motifs." Applied and Environmental Microbiology 79, no. 11 (March 22, 2013): 3380–91. http://dx.doi.org/10.1128/aem.03803-12.

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ABSTRACTFunctional prediction of carbohydrate-active enzymes is difficult due to low sequence identity. However, similar enzymes often share a few short motifs, e.g., around the active site, even when the overall sequences are very different. To exploit this notion for functional prediction of carbohydrate-active enzymes, we developed a simple algorithm, peptide pattern recognition (PPR), that can divide proteins into groups of sequences that share a set of short conserved sequences. When this method was used on 118 glycoside hydrolase 5 proteins with 9% average pairwise identity and representing four characterized enzymatic functions, 97% of the proteins were sorted into groups correlating with their enzymatic activity. Furthermore, we analyzed 8,138 glycoside hydrolase 13 proteins including 204 experimentally characterized enzymes with 28 different functions. There was a 91% correlation between group and enzyme activity. These results indicate that the function of carbohydrate-active enzymes can be predicted with high precision by finding short, conserved motifs in their sequences. The glycoside hydrolase 61 family is important for fungal biomass conversion, but only a few proteins of this family have been functionally characterized. Interestingly, PPR divided 743 glycoside hydrolase 61 proteins into 16 subfamilies useful for targeted investigation of the function of these proteins and pinpointed three conserved motifs with putative importance for enzyme activity. Furthermore, the conserved sequences were useful for cloning of new, subfamily-specific glycoside hydrolase 61 proteins from 14 fungi. In conclusion, identification of conserved sequence motifs is a new approach to sequence analysis that can predict carbohydrate-active enzyme functions with high precision.

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45

André, I., C. Foces-Foces, F. H. Cano, and M. Martinez-Ripoll. "Packing Modes in Nitrobenzene Derivatives. II. The `Pseudo-Herringbone' Mode." Acta Crystallographica Section B Structural Science 53, no. 6 (December 1, 1997): 996–1005. http://dx.doi.org/10.1107/s0108768197010835.

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Nitrobenzene derivatives pack in two main motifs: stacks [see André , Foces-Foces, Cano & Martinez-Ripoll (1997). Acta Cryst. B53, 984–995] and `pseudo- herringbone' together account for some 61.5% of the structures classified as BCLASS = 15 by the Cambridge Structural Database. Several geometrical parameters allow the classification of the `pseudo-herringbone' category into four main motifs. Weak interactions appear to play an important role in this packing mode.

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46

Nurhaida, Ida, Vina Ayumi, Devi Fitrianah, Remmy A. M. Zen, Handrie Noprisson, and Hong Wei. "Implementation of deep neural networks (DNN) with batch normalization for batik pattern recognition." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 2 (April 1, 2020): 2045. http://dx.doi.org/10.11591/ijece.v10i2.pp2045-2053.

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One of the most famous cultural heritages in Indonesia is batik. Batik is a specially made drawing cloth by writing Malam (wax) on the cloth, then processed in a certain way. The diversity of motifs both in Indonesia and the allied countries raises new research topics in the field of information technology, both for conservation, storage, publication and the creation of new batik motifs. In computer science research area, studies about Batik pattern have been done by researchers and some algorithms have been successfully applied in Batik pattern recognition. This study was focused on Batik motif recognition using texture fusion feature which is Gabor, Log-Gabor, and GLCM; and using PCA feature reduction to improve the classification accuracy and reduce the computational time. To improve the accuracy, we proposed a Deep Neural Network model to recognise batik pattern and used batch normalisation as a regularises to generalise the model and to reduce time complexity. From the experiments, the feature extraction, selection, and reduction gave better accuracy than the raw dataset. The feature selection and reduction also reduce time complexity. The DNN+BN significantly improve the accuracy of the classification model from 65.36% to 83.15%. BN as a regularization has successfully made the model more general, hence improve the accuracy of the model. The parameters tuning also improved accuracy from 83.15% to 85.57%.

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47

Tran, Tran Trung, Jim McKie, Wim D. F. Meutermans, Gregory T. Bourne, Peter R. Andrews та Mark L. Smythe. "Topological side-chain classification of β-turns: Ideal motifs for peptidomimetic development". Journal of Computer-Aided Molecular Design 19, № 8 (серпень 2005): 551–66. http://dx.doi.org/10.1007/s10822-005-9006-2.

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48

Velissariou, Aggeliki. "Two folktales (Vampire beings in Greek folktales)." Bulletin of the Transilvania University of Brasov. Series IV: Philology and Cultural Studies 14 (63), Special Issue (January 2022): 215–36. http://dx.doi.org/10.31926/but.pcs.2021.63.14.3.15.

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This paper attempts to elaborate how the vampire theme is conceptualized in Greek folktales. It’s a case study of the Greek folk tales: “Gelloudi” and “The Lamia bride” found in the compilation Paramythokores (2002). The folktale complies with a strict formulaic style of oral narration and the most time-resilient elements of storytelling are the motifs that create the story. We find similar or echoing motifs in folktales globally; some motifs are darker than others, enhancing the agony and thrill of storytelling. Concerning the Greek folktale, the research led to the classification of six dark motifs. Bloodsucking creatures such as Strigla (in Latin: Strigula, Strix), Gello (gelloudi) and Lamia are found in the dark motif of the supernatural. The tales, in this case study, are horror stories, in a sense, but they evolve in a broad form of narration, depriving the reader of gruesome details and delivering a cathartic ending. The vampire theme is not dominating in the first folktale as a result of the combination of three folktale types, whereas the second one focuses solely on this theme. In both cases the creatures are female attacking animals, men and community, symbolizing the heavy price of the birth of a girl in the family, as it was perceived in these traditional communities. A baby girl and a new bride, attack the world of men. They are powerful and feared, they are ‘horse-eaters’ symbolizing the threat of depriving the established status for men, first by eating their horse and then by eating them.

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49

Dey, Asim K., Yulia R. Gel, and H. Vincent Poor. "What network motifs tell us about resilience and reliability of complex networks." Proceedings of the National Academy of Sciences 116, no. 39 (September 11, 2019): 19368–73. http://dx.doi.org/10.1073/pnas.1819529116.

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Network motifs are often called the building blocks of networks. Analysis of motifs has been found to be an indispensable tool for understanding local network structure, in contrast to measures based on node degree distribution and its functions that primarily address a global network topology. As a result, networks that are similar in terms of global topological properties may differ noticeably at a local level. This phenomenon of the impact of local structure has been recently documented in network fragility analysis and classification. At the same time, many studies of networks still tend to focus on global topological measures, often failing to unveil hidden mechanisms behind vulnerability of real networks and their dynamic response to malfunctions. In this paper, a study of motif-based analysis of network resilience and reliability under various types of intentional attacks is presented, with the goal of shedding light on local dynamics and vulnerability of networks. These methods are demonstrated on electricity transmission networks of 4 European countries, and the results are compared with commonly used resilience and reliability measures.

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50

Kumar, Vinod, Gopal Singh, A. K. Verma, and Sanjeev Agrawal. "In Silico Characterization of Histidine Acid Phytase Sequences." Enzyme Research 2012 (December 5, 2012): 1–8. http://dx.doi.org/10.1155/2012/845465.

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Histidine acid phytases (HAPhy) are widely distributed enzymes among bacteria, fungi, plants, and some animal tissues. They have a significant role as an animal feed enzyme and in the solubilization of insoluble phosphates and minerals present in the form of phytic acid complex. A set of 50 reference protein sequences representing HAPhy were retrieved from NCBI protein database and characterized for various biochemical properties, multiple sequence alignment (MSA), homology search, phylogenetic analysis, motifs, and superfamily search. MSA using MEGA5 revealed the presence of conserved sequences at N-terminal “RHGXRXP” and C-terminal “HD.” Phylogenetic tree analysis indicates the presence of three clusters representing different HAPhy, that is, PhyA, PhyB, and AppA. Analysis of 10 commonly distributed motifs in the sequences indicates the presence of signature sequence for each class. Motif 1 “SPFCDLFTHEEWIQYDYLQSLGKYYGYGAGNPLGPAQGIGF” was present in 38 protein sequences representing clusters 1 (PhyA) and 2 (PhyB). Cluster 3 (AppA) contains motif 9 “KKGCPQSGQVAIIADVDERTRKTGEAFAAGLAPDCAITVHTQADTSSPDP” as a signature sequence. All sequences belong to histidine acid phosphatase family as resulted from superfamily search. No conserved sequence representing 3- or 6-phytase could be identified using multiple sequence alignment. This in silico analysis might contribute in the classification and future genetic engineering of this most diverse class of phytase.

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