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Author

Valadi K. Jayaraman

Other affiliations: Shiv Nadar University
Bio: Valadi K. Jayaraman is an academic researcher from National Chemical Laboratory. The author has contributed to research in topic(s): Ant colony optimization algorithms & Support vector machine. The author has an hindex of 16, co-authored 68 publication(s) receiving 1110 citation(s). Previous affiliations of Valadi K. Jayaraman include Shiv Nadar University.
Papers
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Journal ArticleDOI
TL;DR: A simple pheromone-guided mechanism to improve the performance of PSO method for optimization of multimodal continuous functions is explored and numerical results comparisons with different metaheuristics demonstrate the effectiveness and efficiency of the proposed PSACO method.
Abstract: This paper proposes PSACO (particle swarm ant colony optimization) algorithm for highly non-convex optimization problems. Both particle swarm optimization (PSO) and ant colony optimization (ACO) are co-operative, population-based global search swarm intelligence metaheuristics. PSO is inspired by social behavior of bird flocking or fish schooling, while ACO imitates foraging behavior of real life ants. In this study, we explore a simple pheromone-guided mechanism to improve the performance of PSO method for optimization of multimodal continuous functions. The proposed PSACO algorithm is tested on several benchmark functions from the usual literature. Numerical results comparisons with different metaheuristics demonstrate the effectiveness and efficiency of the proposed PSACO method.

298 citations


Journal ArticleDOI
TL;DR: The jack-knife success rate thus obtained on the benchmark dataset constructed by Shen and Chou is 71.23%, indicating that the novel pseudo amino acid composition approach with PSSM and SVM classifier is very promising and may at least play a complimentary role to the existing methods.
Abstract: Identification of Nuclear protein localization assumes significance as it can provide in depth insight for genome regulation and function annotation of novel proteins. A multiclass SVM classifier with various input features was employed for nuclear protein compartment identification. The input features include factor solution scores and evolutionary information (position specific scoring matrix (PSSM) score) apart from conventional dipeptide composition and pseudo amino acid composition. All the SVM classifiers with different sets of input features performed better than the previously available prediction classifiers. The jack-knife success rate thus obtained on the benchmark dataset constructed by Shen and Chou [Shen, H.B., Chou, K.C., 2005, Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition. Biochem. Biophys. Res. Commun. 337, 752-756] is 71.23%, indicating that the novel pseudo amino acid composition approach with PSSM and SVM classifier is very promising and may at least play a complimentary role to the existing methods.

104 citations


Journal ArticleDOI
01 Feb 2006-Bioinformatics
TL;DR: Six physicochemical properties together with residue and dipeptide-compositions have been used to develop a support vector machine-based classifier to predict the overexpression status in E.coli, and it performs reasonably well in predicting the propensity of a protein to be soluble or to form inclusion bodies.
Abstract: Motivation: Inclusion body formation has been a major deterrent for overexpression studies since a large number of proteins form insoluble inclusion bodies when overexpressed in Escherichia coli. The formation of inclusion bodies is known to be an outcome of improper protein folding; thus the composition and arrangement of amino acids in the proteins would be a major influencing factor in deciding its aggregation propensity. There is a significant need for a prediction algorithm that would enable the rational identification of both mutants and also the ideal protein candidates for mutations that would confer higher solubility-on-overexpression instead of the presently used trial-and-error procedures. Results: Six physicochemical properties together with residue and dipeptide-compositions have been used to develop a support vector machine-based classifier to predict the overexpression status in E.coli. The prediction accuracy is ∼72% suggesting that it performs reasonably well in predicting the propensity of a protein to be soluble or to form inclusion bodies. The algorithm could also correctly predict the change in solubility for most of the point mutations reported in literature. This algorithm can be a useful tool in screening protein libraries to identify soluble variants of proteins. Avalibility: Software is available on request from the authors. Contact:balaji@iitcb.ac.in; vk.jayaraman@ncl.res.in Supplementary information: Supplementary data are available at Bioinformatics Online web site.

97 citations


Journal ArticleDOI
Abstract: The objective of this study was to develop a unified data-driven correlation for the overall gas hold-up for various gas-liquid systems using support vector regression (SVR)-based modeling technique. Over the years, researchers have amply quantified the hydrodynamics of bubble column reactors in terms of the overall gas hold-up. In this work, about 1810 experimental points were collected from 40 open sources spanning the years 1965-2007. The model for overall gas hold-up was established as a function of several parameters which include superficial gas velocity, superficial liquid velocity, gas density, molecular weight of gas, sparger type, sparger hole diameter, number of sparger holes, liquid viscosity, liquid density, liquid surface tension, operating temperature, operating pressure and column diameter of the gas-liquid system. For understanding the hold-up behavior, the data used for training the model was grouped into various gas-liquid systems viz., air-water, gas-aqueous viscous liquids, gas-organic liquids, gas-aqueous electrolyte solutions and gas-liquid systems operated over a wide range of pressure. A generalized model established using SVR was evaluated for its performance for various gas-liquid systems. Statistical analysis showed that the proposed generalized SVR-based correlation for overall gas hold-up has prediction accuracy of 97% with average absolute relative error (% AARE) of 12.11%. A comparison of this correlation with the selected system specific correlations in the literature showed that the developed SVR-based correlation significantly gives enhanced prediction of overall gas hold-up.

40 citations


Journal ArticleDOI
27 Oct 2003-Physics Letters A
TL;DR: This work provides a methodology for the control of complex non- linear systems by compensating for the non-linear part using support vector machines (SVM) and subsequently developing simple linear feedback law for control.
Abstract: This work provides a methodology for the control of complex non-linear systems by compensating for the non-linear part using support vector machines (SVM) and subsequently developing simple linear feedback law for control. The method tested for the benchmark Rossler and Lorenz chaotic oscillators shows excellent performance.

35 citations


Cited by
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BookDOI
01 Jan 2005-
TL;DR: This chapter discusses Kernel Discriminant Learning with Application to Face Recognition, Fast Color Texture-based Object Detection in Images: Application to License Plate Localization, and more.
Abstract: From the contents: Support Vector Machines - An Introduction.- Multiple Model Estimation for Nonlinear Classification.- Componentwise Least Squares Support Vector Machines.- Active Support Vector Learning with Statistical Queries.- Local Learning vs. Global Learning: An Introduction to Maxi-Min Margin Machine.- Active-Set Methods for Support Vector Machines.- Theoretical and Practical Model Selection Methods for Support Vector Classifiers.- Adaptive Discriminant and Quasiconformal Kernel Nearest Neighbor Classification.- Improving the Performance of the Support Vector Machine: Two Geometrical Scaling Methods.- An Accelerated Robust Support Vector Machine Algorithm.- Fuzzy Support Vector Machines with Automatic Membership Setting.- Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and Performance.- Kernel Discriminant Learning with Application to Face Recognition.- Fast Color Texture-based Object Detection in Images: Application to License Plate Localization.

823 citations


Journal ArticleDOI
Kuo-Chen Chou, Hong-Bin Shen1Institutions (1)
01 Jan 2008-Nature Protocols
TL;DR: This protocol is a step-by-step guide on how to use the Web-server predictors in the Cell-PLoc package, a package of Web servers developed recently by hybridizing the 'higher level' approach with the ab initio approach.
Abstract: Information on subcellular localization of proteins is important to molecular cell biology, proteomics, system biology and drug discovery. To provide the vast majority of experimental scientists with a user-friendly tool in these areas, we present a package of Web servers developed recently by hybridizing the 'higher level' approach with the ab initio approach. The package is called Cell-PLoc and contains the following six predictors: Euk-mPLoc, Hum-mPLoc, Plant-PLoc, Gpos-PLoc, Gneg-PLoc and Virus-PLoc, specialized for eukaryotic, human, plant, Gram-positive bacterial, Gram-negative bacterial and viral proteins, respectively. Using these Web servers, one can easily get the desired prediction results with a high expected accuracy, as demonstrated by a series of cross-validation tests on the benchmark data sets that covered up to 22 subcellular location sites and in which none of the proteins included had > or =25% sequence identity to any other protein in the same subcellular-location subset. Some of these Web servers can be particularly used to deal with multiplex proteins as well, which may simultaneously exist at, or move between, two or more different subcellular locations. Proteins with multiple locations or dynamic features of this kind are particularly interesting, because they may have some special biological functions intriguing to investigators in both basic research and drug discovery. This protocol is a step-by-step guide on how to use the Web-server predictors in the Cell-PLoc package. The computational time for each prediction is less than 5 s in most cases. The Cell-PLoc package is freely accessible at http://chou.med.harvard.edu/bioinf/Cell-PLoc.

750 citations


Journal ArticleDOI
TL;DR: The recent advances in the prediction of intrinsically disordered proteins and the use of protein disorder prediction in the fields of molecular biology and bioinformatics are reviewed here, especially with regard to protein function.
Abstract: The recent advances in the prediction of intrinsically disordered proteins and the use of protein disorder prediction in the fields of molecular biology and bioinformatics are reviewed here, especially with regard to protein function. First, a close look is taken at intrinsically disordered proteins and then at the methods used for their experimental characterization. Next, the major statistical properties of disordered regions are summarized, and prediction models developed thus far are described, including their numerous applications in functional proteomics. The future of the prediction of protein disorder and the future uses of such predictions in functional proteomics comprise the last section of this article.

633 citations


Journal ArticleDOI
Dongshu Wang1, Dapei Tan1, Lei Liu2Institutions (2)
01 Jan 2018-
TL;DR: Its origin and background is introduced and the theory analysis of the PSO is carried out, which analyzes its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithms, multi-objective optimization PSO and its engineering applications.
Abstract: Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.

457 citations


Journal ArticleDOI
28 Sep 2009-Natural Science
TL;DR: In this minireview, a systematic introduction is presented to highlight the development of these web-servers by this group during the last three years.
Abstract: Recent advance in large-scale genome sequencing has generated a huge volume of protein sequences. In order to timely utilize the information hidden in these newly discovered sequences, it is highly desired to develop computational methods for efficiently identifying their various attributes because the information thus obtained will be very useful for both basic research and drug development. Particularly, it would be even more useful and welcome if a user-friendly web-server could be provided for each of these methods. In this minireview, a systematic introduction is presented to highlight the development of these web-servers by our group during the last three years.

452 citations


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Performance
Metrics

Author's H-index: 16

No. of papers from the Author in previous years
YearPapers
20161
20141
20131
20103
20096
20086