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Manimekalai, M.
- Literature Survey on the Prediction of Secondary Structure of Proteins Using Radial Basis Function Neural Networks (RBFNN) and Support Vector Machines (SVM)
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Authors
K. Meena
1,
M. Manimekalai
2
Affiliations
1 Bharathidasan University, Tiruchirappalli, Tamil Nadu, IN
2 Department of MCA, Shrimati Indira Gandhi College, Tiruchirappalli, Tamil Nadu, IN
1 Bharathidasan University, Tiruchirappalli, Tamil Nadu, IN
2 Department of MCA, Shrimati Indira Gandhi College, Tiruchirappalli, Tamil Nadu, IN
Source
International Journal of Information and Computation Technology, Vol 3, No 1 (2013), Pagination: 33-43Abstract
The Protein structure prediction has been an active research area for the last 40 years or so. The technical progress in computational Molecular Biology during the last decades has contributed significantly to the progress we see today. The major goal of predicting Protein structures underpins the correct assumption that three dimensional structures confer protein function. The linear Amino Acid sequences must transform to nonlinear Secondary Structures and then to Tertiary and Quaternary Structures that are responsible for biological functions. Biological functions may remain similar or change in the related organisms through the evolutionary process. By considering the importance of the prediction of secondary structure of protein a detailed literature study of the same using Radial Basis Function Neural Networks (RBFNN) AND Support Vector Machines (SVM) has been reviewed in this paper.Keywords
Radial Basis Function Neural Networks, Support Vector Machines, Secondary Structures, Tertiary Structure And Quaternary Structures
References
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- Comparision of Prediction of Structure of Protein of Soy Beans Using Radial Basis Function Neural Networks with other Methods for Rs126 and PDB Data Sets
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Authors
K. Meena
1,
M. Manimekalai
2
Affiliations
1 Bharathidasan University, Tiruchirappalli, Tamil Nadu, IN
2 Department of MCA, Shrimati Indira Gandhi College, Tiruchirappalli, Tamil Nadu, IN
1 Bharathidasan University, Tiruchirappalli, Tamil Nadu, IN
2 Department of MCA, Shrimati Indira Gandhi College, Tiruchirappalli, Tamil Nadu, IN
Source
Journal of Computational Intelligence in Bioinformatics, Vol 6, No 1 (2013), Pagination: 49-57Abstract
In this paper Prediction of structure of Protein of Soy Beans using Radial Basis Function Neural Networks for RS126 Data set and PDB Data set has been made and compared with other traditional methods namely Chou-Fasman, GOR, APSSP, PHD, Prospect and SSpro.The training and testing sets for both have been taken into consideration to train and test the networks respectively. The major parameter for finding the accuracy of the protein secondary structure prediction is the per-residue prediction accuracy, Q3, which gives the percentage of all correctly predicted residues within the three-state (H, E, C) classes, and has also been employed for assessment of prediction approaches. The performance of the RBFNN protein secondary structure prediction models is evaluated based on their prediction accuracy . The accuracy of the developed approach is compared with other traditional methods to explore the performance of the proposed approach. It is found that the proposed techniques provide a prediction accuracy of about 81% which is very significant. The accuracy for different width of sliding windows. It clearly shows that, with the increase in the sliding window width the accuracy also increases.Keywords
RBFNN, Prediction Accuracy, Training Set, Test Set, Sliding WindowReferences
- Boscott, P.E., Barton, G.J. and Richards, W.G., “Secondary Structure Prediction for Modelling by Homology”, PEDS, Vol. 6, Issue 3, pp.261–266, January 1993.
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- Pollastri, G., Przybylski, D., Rost, B. and Baldi, P., “Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins”, 47(2), 228–235, 2002.
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- Garnier, J.J., Gibrat, J.F. and Robson, “GOR method for predicting protein secondary structure from amino acid sequence, Methods Enzymol”, 266, 540– 550, 1996.
- Marti-Renom et al., “Comparative protein structure modeling of genes and genomes,” Rev. Biophys. Biomol, Struct, 29:291-325, 2000
- Rost, B., “Rising accuracy of protein secondary structure prediction”, D.Chasman, Ed., “Protein structure determination, analysis, and modeling for drug discovery”, New York: Dekker, pp. 207–249, 2003.
- Histological Differentiation along Turtle Ductus Epididymidis, with a Note on Secretion of Seminal Proteins as Discrete Granules
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Authors
Affiliations
1 Department of Animal Science, School of Life Sciences, Bharathidasan University, Tiruchirappalli - 620 024, IN
2 Department of Zoology, Periyar EVR College, Tiruchirappalli - 620 023, IN
1 Department of Animal Science, School of Life Sciences, Bharathidasan University, Tiruchirappalli - 620 024, IN
2 Department of Zoology, Periyar EVR College, Tiruchirappalli - 620 023, IN
Source
Journal of Endocrinology and Reproduction, Vol 3, No 1&2 (1999), Pagination: 36-46Abstract
Histological analysis of the male reproductive tract of the peninsular flap-shelled turtle Lissemys p. punctata revealed that several minute ductuli efferentes reach the epididymis to form into a large thin-walled duct which probably, forms a temporary storage region of sperm arriving from the testis. Originating from this duct, the single long ductus epididymidis takes a highly tortuous course when it differentiates along its length, in terms of diameter and epithelial organization, into four regions, viz., the initial segment, caput, corpus and cauda. Turtle ductus epididymidis differs from that of lizards in the extensive pattern of folding of the epithelium of caput, corpus and cauda regions. The differentiation along the ductus epididymidis of the turtle signifies different functional attributes to the different regions. The epithelium of the initial segment of the turtle epididymis secretes large glycoprotein granules (4-8 μm) and that of the caput secretes minute granules (1-2 μm). The large granules possess a central core and a peripheral coat, whereas the minute granules are uniformly dense. Thus, turtle ductus epididymidis differentiates into initial segment, caput, corpus and cauda regions, and the initial segment and caput secrete seminal proteins in the lacertilian pattern as discrete granules.Keywords
Epididymis, Glycoprotein Granules, Seminal Protein.- Prediction of Secondary Structure of Using Neural Networks and Machine Learning Techniques
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Source
Biometrics and Bioinformatics, Vol 4, No 1 (2012), Pagination: 46-51Abstract
One of the most significant problems in biomedical research today is the prediction of protein structure from knowledge of the primary amino acid sequence. Secondary Structure Prediction (SSP) is a very typical problem in the field of bioinformatics.Prediction of secondary structure of Proteins can be done from the Protein sequence. In the Protein structure prediction, the Amino Acid sequence of a Protein, the so-called primary structure, can be easily determined from the sequence on the Gene that codes for it. This primary structure exclusively determines a structure in its native environment. Thus primary structure plays a key role in understanding the function of the Protein. Majority of the previous research have ignored the influence of residue conformational preference on structure prediction of proteins. The primary focus of this research is to investigate a variety of approaches for employing ANN and Machine Learning techniques in order to predict the secondary structure of proteins in soybeans.
Keywords
Protein Structure Prediction, RBFNN, MELM, SVM, Amino Acid, Soybeans.- Protein Structure Prediction in Soybeans Using Neural Networks
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Authors
K. Meena
1,
M. Manimekalai
2
Affiliations
1 Shrimati Indira Gandhi College, Trichy, IN
2 Dept. of IT & Applications, Shrimati Indira Gandhi College, Trichy, IN
1 Shrimati Indira Gandhi College, Trichy, IN
2 Dept. of IT & Applications, Shrimati Indira Gandhi College, Trichy, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 2, No 2 (2010), Pagination: 43-47Abstract
Proteins are a definite kind of biological macromolecules that is present in all biological organisms. Amino acids are the building blocks of proteins. They are primary structure, secondary structure, tertiary structure and quaternary structure. Most of the existing algorithms for predicting the content of the protein secondary structure elements have been based on the conventional amino acid composition, where no sequence coupling effects are taken into consideration. Prediction of three dimensional structure, secondary structure, and functional sites of proteins from primary structure are the three major problems in structural bioinformatics. More than a few different approaches have been previously used in these kind of predictions among which, artificial neural networks have been of great interest due to their capability of learning from observations and prediction of the structures for non classified instances. This paper proposes a technique for prediction of protein structure in soybeans using neural networks. This paper uses RBFNN in order to predict the secondary structure. In our approach, genetic encoding scheme is used to generate the centers and widths of radial basis function. In our approach, genetic encoding scheme is used to generate the centers and widths of radial basis function. The neural network architecture used in our approach is a feed forward and fully connected neural network whose Gaussian centers are optimized by genetic algorithm. Experimental are carried on dataset obtained from Protein Data Bank (PDB) to predict the structure of the protein present in it.Keywords
Amino Acids (AA), Bioinformatics, Protein Structure Prediction, Secondary Structure Content, Neural Networks, Radial Basis Function Neural Networks (RBFNN), Genetic Algorithm (GA), Protein Data Bank (PDB).- A Study of Nachiyar Koil Lamp Manufacturers- Business Model
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Authors
Affiliations
1 School of Management, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
1 School of Management, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 27 (2016), Pagination:Abstract
Objectives: The Nachiyar Koil lamp manufacturers are popular for their handcrafted brass lamps. The study is mainly based on the understanding of business model of the lamp manufacturers about their administration, cluster formation revenue formation, production, labor turnover, annual sales, export range, product distribution, borrowings & saving pattern and barriers in the business. The primary data has been collected from 20 lamp manufacturers on all above aspects were collected and analyzed using SPSS software. Methodology: The study has 20 lamp manufacturers chosen by means of simple random sampling method from Nacihyar Koil. The data collected has been analyzed by using the correlation & Regresssion analysis with the help of SPSS software.The analysis includes consumption of raw material, investment, employment opportunities, business administration, exporting, Types of transportation, sales & profit margin, borrowings, license, saving pattern, Bank A/C, Insurance policy of Nachiyar Koil lamp manufacturers. Findings: The study analyse the perspective of business model,lifestyle and growth oppurtunities of Nachiyar koil lamp manufacturers. From the study it is clear that the age of Nachiyar koil lamp manufacturers influence the lifestyle (31.2%), business model & operation 12% and growth opportunity of 10%. Applications/Improvements: The study will be an eye opener for the economist, government officials and social activist in enhancing the welfare measures of Nachiyar koil lamp mannufacturers.Keywords
Growth Oppurtunities, Lamp Manufacturer, Life Style, Revenue Production.Full Text