Kuldip K. Paliwal

Griffith University, Southport, Queensland, Australia

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Publications (291)336.9 Total impact

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    ABSTRACT: Protein fold recognition is an important step towards solving protein function and tertiary structure prediction problems. Among a wide range of approaches proposed to solve this problem, pattern recognition based techniques have achieved the best results. The most effective pattern recognition-based techniques for solving this problem have been based on extracting evolutionary-based features. Most studies have relied on the Position Specific Scoring Matrix (PSSM) to extract these features. However it is known that profile-profile sequence alignment techniques can identify more remote homologs than sequence-profile approaches like PSIBLAST. In this study we use a profile-profile sequence alignment technique, namely HHblits, to extract HMM profiles. We will show that unlike previous studies, using the HMM profile to extract evolutionary information can significantly enhance the protein fold prediction accuracy. We develop a new pattern recognition based system called HMMFold which extracts HMM based evolutionary information and captures remote homology information better than previous studies. Using HMMFold we achieve up to 93.8% and 86.0% prediction accuracies when the sequential similarity rates are less than 40% and 25%, respectively. These results are up to 10% better than previously reported results for this task. Our results show significant enhancement especially for benchmarks with sequential similarity as low as 25% which highlights the effectiveness of HMMFold to address this problem and its superiority over previously proposed approaches found in the literature. The HMMFold is available online at: http://sparks-lab.org/pmwiki/download/index.php?Download=HMMFold.tar.bz2.
    IEEE transactions on nanobioscience 07/2015; DOI:10.1109/TNB.2015.2457906 · 1.77 Impact Factor
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    ABSTRACT: Direct prediction of protein structure from sequence is a challenging problem. An effective approach is to break it up into independent sub-problems. These sub-problems such as prediction of protein secondary structure can then be solved independently. In a previous study, we found that an iterative use of predicted secondary structure and backbone torsion angles can further improve secondary structure and torsion angle prediction. In this study, we expand the iterative features to include solvent accessible surface area and backbone angles and dihedrals based on Cα atoms. By using a deep learning neural network in three iterations, we achieved 82% accuracy for secondary structure prediction, 0.76 for the correlation coefficient between predicted and actual solvent accessible surface area, 19° and 30° for mean absolute errors of backbone φ and ψ angles, respectively, and 8° and 32° for mean absolute errors of Cα-based θ and τ angles, respectively, for an independent test dataset of 1199 proteins. The accuracy of the method is slightly lower for 72 CASP 11 targets but much higher than those of model structures from current state-of-the-art techniques. This suggests the potentially beneficial use of these predicted properties for model assessment and ranking. The Spider 2 is publicly available at: http://sparks-lab.org/yueyang/server/SPIDER2/
    Scientific Reports 06/2015; 5(11476). DOI:10.1038/srep11476 · 5.58 Impact Factor
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    ABSTRACT: Identification of the tertiary structure (3D structure) of a protein is a fundamental problem in biology which helps in identifying its functions. Predicting a protein's fold is considered to be an intermediate step for identifying the tertiary structure of a protein. Computational methods have been applied to determine a protein's fold by assembling information from its structural, physicochemical and/or evolutionary properties. In this study, we propose a scheme in which a feature extraction technique that extracts probabilistic expressions of amino acid dimers, which have varying degree of spatial separation in the primary sequences of proteins, from the Position Specific Scoring Matrix (PSSM). SVM classifier is used to create a model from extracted features for fold recognition. The performance of the proposed scheme is evaluated against three benchmarked datasets, namely the Ding and Dubchak, Extended Ding and Dubchak, and Taguchi and Gromiha datasets. The proposed scheme performed well in the experiments conducted, providing improvements over previously published results in literature. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Journal of Theoretical Biology 06/2015; 380. DOI:10.1016/j.jtbi.2015.05.030 · 2.30 Impact Factor
  • Alok Sharma · Kuldip K. Paliwal
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    ABSTRACT: The regularized linear discriminant analysis (RLDA) technique is one of the popular methods for dimensionality reduction used for small sample size problems. In this technique, regularization parameter is conventionally computed using a cross-validation procedure. In this paper, we propose a deterministic way of computing the regularization parameter in RLDA for small sample size problem. The computational cost of the proposed deterministic RLDA is significantly less than the cross-validation based RLDA technique. The deterministic RLDA technique is also compared with other popular techniques on a number of datasets and favorable results are obtained.
    Neurocomputing 03/2015; 151:207-214. DOI:10.1016/j.neucom.2014.09.051 · 2.01 Impact Factor
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    ABSTRACT: The functioning of a protein relies on its location in the cell. Therefore, predicting protein subcellular localization is an important step towards protein function prediction. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve the prediction performance. However, for newly sequenced proteins, the GO is not available. Therefore, for these cases, the prediction performance of GO based methods degrade significantly. In this study, we develop a method to effectively employ physicochemical and evolutionary-based information in the protein sequence. To do this, we propose segmentation based feature extraction method to explore potential discriminatory information based on physicochemical properties of the amino acids to tackle Gram-positive and Gram-negative subcellular localization. We explore our proposed feature extraction techniques using 10 attributes that have been experimentally selected among a wide range of physicochemical attributes. Finally by applying the Rotation Forest classification technique to our extracted features, we enhance Gram-positive and Gram-negative subcellular localization accuracies up to 3.4% better than previous studies which used GO for feature extraction. By proposing segmentation based feature extraction method to explore potential discriminatory information based on physicochemical properties of the amino acids as well as using Rotation Forest classification technique, we are able to enhance the Gram-positive and Gram-negative subcellular localization prediction accuracies, significantly.
    BMC Bioinformatics 02/2015; 16(Suppl 4):S1. DOI:10.1186/1471-2105-16-S4-S1 · 2.67 Impact Factor
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    Abdollah Dehzangi · Alok Sharma · James Lyons · Kuldip K Paliwal · Abdul Sattar
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    ABSTRACT: Recent advancement in the pattern recognition field stimulates enormous interest in Protein Fold Recognition (PFR). PFR is considered as a crucial step towards protein structure prediction and drug design. Despite all the recent achievements, the PFR still remains as an unsolved issue in biological science and its prediction accuracy still remains unsatisfactory. Furthermore, the impact of using a wide range of physicochemical-based attributes on the PFR has not been explored adequately. In this study, we propose a novel mixture of physicochemical and evolutionary-based feature extraction methods based on the concepts of segmented distribution and density. We also explore the impact of 55 different physicochemical-based attributes on the PFR. Our results show that by providing more local discriminatory information as well as obtaining benefit from both physicochemical and evolutionary-based features simultaneously, we can enhance the protein fold prediction accuracy up to 5% better than previously reported results found in the literature.
    International Journal of Data Mining and Bioinformatics 01/2015; 11(1):115-138. DOI:10.1504/IJDMB.2015.066359 · 0.66 Impact Factor
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    Kuldip K Paliwal · Alok Sharma · James Lyons · Abdollah Dehzangi
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    ABSTRACT: Deciphering three dimensional structure of a protein sequence is a challenging task in biological science. Protein fold recognition and protein secondary structure prediction are transitional steps in identifying the three dimensional structure of a protein. For protein fold recognition, evolutionary-based information of amino acid sequences from the position specific scoring matrix (PSSM) has been recently applied with improved results. On the other hand, the SPINE-X predictor has been developed and applied for protein secondary structure prediction. Several reported methods for protein fold recognition have only limited accuracy. In this paper, we have developed a strategy of combining evolutionary-based information (from PSSM) and predicted secondary structure using SPINE-X to improve protein fold recognition. The strategy is based on finding the probabilities of amino acid pairs (AAP). The proposed method has been tested on several protein benchmark datasets and an improvement of 8.9% recognition accuracy has been achieved. We have achieved, for the first time over 90% and 75% prediction accuracies for sequence similarity values below 40% and 25%, respectively. We also obtain 90.6% and 77.0% prediction accuracies, respectively, for the Extended Ding and Dubchak and Taguchi and Gromiha benchmark protein fold recognition datasets widely used for in the literature.
    BMC Bioinformatics 12/2014; 15(Suppl 16):S12. DOI:10.1186/1471-2105-15-S16-S12 · 2.67 Impact Factor
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    ABSTRACT: In biology, identifying the tertiary structure of a protein helps determine its functions. A step towards tertiary structure identification is predicting a protein's fold. Computational methods have been applied to determine a protein's fold by assembling information from its structural, physicochemical and/or evolutionary properties. It has been shown that evolutionary data helps improve prediction accuracy. In this study, a scheme is proposed that uses the genetic algorithm (GA) to optimize a weighted voting system to improve protein fold recognition. This scheme incorporates k-separated bigram transition probabilities for feature extraction, which are based on the Position Specific Scoring Matrix (PSSM). A set of SVM classifiers are used for initial classification, whereupon their predictions are consolidated using the optimized weighted voting system. This scheme has been demonstrated on the Ding and Dubchak (DD) benchmarked data set.
    Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), 2014, Nadi, Fiji; 11/2014
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    Belinda Schwerin · Kuldip Paliwal
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    ABSTRACT: The speech transmission index (STI) is a well known measure of intelligibility, most suited to the evaluation of speech intelligibility in rooms, with stimuli subjected to additive noise and reverberance. However, STI and its many variations do not effectively represent the intelligibility of stimuli containing non-linear distortions such as those resulting from processing by enhancement algorithms. In this paper, we revisit the STI approach and propose a variation which processes the modulation envelope in short-time segments, requiring only an assumption of quasi-stationarity (rather than the stationarity assumption of STI) of the modulation signal. Results presented in this work show that the proposed approach improves the measures correlation to subjective intelligibility scores compared to traditional STI for a range of noise types and subjected to different enhancement approaches. The approach is also shown to have higher correlation than other coherence, correlation and distance measures tested, but is unsuited to the evaluation of stimuli heavily distorted with (for example) masking based processing, where an alternative approach such as STOI is recommended.
    Speech Communication 11/2014; 65. DOI:10.1016/j.specom.2014.05.003 · 1.55 Impact Factor
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    ABSTRACT: Because a nearly constant distance between two neighbouring Cα atoms, local backbone structure of proteins can be represented accurately by the angle between Cαi−1CαiCαi+1 (θ) and a dihedral angle rotated about the CαiCαi+1 bond (τ). θ and τ angles, as the representative of structural properties of three to four amino-acid residues, offer a description of backbone conformations that is complementary to φ and ψ angles (single residue) and secondary structures (>3 residues). Here, we report the first machine-learning technique for sequence-based prediction of θ and τ angles. Predicted angles based on an independent test have a mean absolute error of 9° for θ and 34° for τ with a distribution on the θ-τ plane close to that of native values. The average root-mean-square distance of 10-residue fragment structures constructed from predicted θ and τ angles is only 1.9Å from their corresponding native structures. Predicted θ and τ angles are expected to be complementary to predicted ϕ and ψ angles and secondary structures for using in model validation and template-based as well as template-free structure prediction. The deep neural network learning technique is available as an on-line server called Structural Property prediction with Integrated DEep neuRal network (SPIDER) at http://sparks-lab.org. © 2014 Wiley Periodicals, Inc.
    Journal of Computational Chemistry 10/2014; 35(28). DOI:10.1002/jcc.23718 · 3.60 Impact Factor
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    ABSTRACT: Protein subcellular localization is defined as predicting the functioning location of a given protein in the cell. It is considered an important step towards protein function prediction and drug design. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve protein subcellular localization prediction performance. However, relying solely on GO, this problem remains unsolved. At the same time, the impact of other sources of features especially evolutionary-based features has not been explored adequately for this task. In this study, we aim to extract discriminative evolutionary features to tackle this problem. To do this, we propose two segmentation based feature extraction methods to explore potential local evolutionary-based information for Gram-positive and Gram-negative subcellular localizations. We will show that by applying a Support Vector Machine (SVM) classifier to our extracted features, we are able to enhance Gram-positive and Gram-negative subcellular localization prediction accuracies by up to 6.4% better than previous studies including the studies that used GO for feature extraction.
    Journal of Theoretical Biology 09/2014; 364. DOI:10.1016/j.jtbi.2014.09.029 · 2.30 Impact Factor
  • Alok Sharma · Kuldip K. Paliwal
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    ABSTRACT: Dimensionality reduction is an important aspect in the pattern classification literature, and linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction technique. The application of variants of LDA technique for solving small sample size (SSS) problem can be found in many research areas e.g. face recognition, bioinformatics, text recognition, etc. The improvement of the performance of variants of LDA technique has great potential in various fields of research. In this paper, we present an overview of these methods. We covered the type, characteristics and taxonomy of these methods which can overcome SSS problem. We have also highlighted some important datasets and software/packages.
    International Journal of Machine Learning and Cybernetics 06/2014; 6(3). DOI:10.1007/s13042-013-0226-9
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    ABSTRACT: Protein fold recognition (PFR) is considered as an important step towards the protein structure prediction problem. Despite all the efforts that have been made so far, finding an accurate and fast computational approach to solve the PFR still remains a challenging problem for bioinformatics and computational biology. In this study, we propose the concept of segmented-based feature extraction technique to provide local evolutionary information embedded in position specific scoring matrix (PSSM) and structural information embedded in the predicted secondary structure of proteins using SPINE-X. We also employ the concept of occurrence feature to extract global discriminatory information from PSSM and SPINE-X. By applying a support vector machine (SVM) to our extracted features, we enhance the protein fold prediction accuracy for 7.4 percent over the best results reported in the literature. We also report 73.8 percent prediction accuracy for a data set consisting of proteins with less than 25 percent sequence similarity rates and 80.7 percent prediction accuracy for a data set with proteins belonging to 110 folds with less than 40 percent sequence similarity rates. We also investigate the relation between the number of folds and the number of features being used and show that the number of features should be increased to get better protein fold prediction results when the number of folds is relatively large.
    IEEE/ACM Transactions on Computational Biology and Bioinformatics 05/2014; 11(3):510-519. DOI:10.1109/TCBB.2013.2296317 · 1.54 Impact Factor
  • Alok Sharma · Kuldip K. Paliwal · Seiya Imoto · Satoru Miyano
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    ABSTRACT: Investigation of genes, using data analysis and computer-based methods, has gained widespread attention in solving human cancer classification problem. DNA microarray gene expression datasets are readily utilized for this purpose. In this paper, we propose a feature selection method using improved regularized linear discriminant analysis technique to select important genes, crucial for human cancer classification problem. The experiment is conducted on several DNA microarray gene expression datasets and promising results are obtained when compared with several other existing feature selection methods.
    Machine Vision and Applications 04/2014; 25(3). DOI:10.1007/s00138-013-0577-y · 1.44 Impact Factor
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    James Lyons · Alok Sharma · Abdollah Dehzangi · Kuldip K Paliwal
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    ABSTRACT: In protein fold recognition, a protein is classified into one of its folds. The recognition of a protein fold can be done by employing feature extraction methods to extract relevant information from protein sequences and then by using a classifier to accurately recognize novel protein sequences. In the past, several feature extraction methods have been developed but with limited recognition accuracy only. Protein sequences of varying lengths share the same fold and therefore they are very similar (in a fold) if aligned properly. To this, we develop an amino acid alignment method to extract important features from protein sequences by computing dissimilarity distances between proteins. This is done by measuring distance between two respective position specific scoring matrices of protein sequences which is used in a support vector machine framework. We demonstrated the effectiveness of the proposed method on several benchmark datasets. The method shows significant improvement in the fold recognition performance which is in the range of 4.3% to 7.6% compared to several other existing feature extraction methods.
    Journal of Theoretical Biology 03/2014; 354. DOI:10.1016/j.jtbi.2014.03.033 · 2.30 Impact Factor
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    Belinda Schwerin · Kuldip Paliwal
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    ABSTRACT: In this paper we investigate an alternate, RI-modulation (R = real, I = imaginary) AMS framework for speech enhancement, in which the real and imaginary parts of the modulation signal are processed in secondary AMS procedures. This framework offers theoretical advantages over the previously proposed modulation AMS frameworks in that noise is additive in the modulation signal and noisy acoustic phase is not used to reconstruct speech. Using the MMSE magnitude estimation to modify modulation magnitude spectra, initial experiments presented in this work evaluate if these advantages translate into improvements in processed speech quality. The effect of speech presence uncertainty and log-domain processing on MMSE magnitude estimation in the RI-modulation framework is also investigated. Finally, a comparison of different enhancement approaches applied in the RI-modulation framework is presented. Using subjective and objective experiments as well as spectrogram analysis, we show that RI-modulation MMSE magnitude estimation with speech presence uncertainty produces stimuli which has a higher preference by listeners than the other RI-modulation types. In comparisons to similar approaches in the modulation AMS framework, results showed that the theoretical advantages of the RI-modulation framework did not translate to an improvement in overall quality, with both frameworks yielding very similar sounding stimuli, but a clear improvement (compared to the corresponding modulation AMS based approach) in speech intelligibility was found.
    Speech Communication 03/2014; 58:49–68. DOI:10.1016/j.specom.2013.11.001 · 1.55 Impact Factor
  • Roger Chappel · Kuldip Paliwal
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    ABSTRACT: This work highlights the need to adapt teaching methods in digital signal processing (DSP) on speech to suit shifts in generational learning behavior, furthermore it suggests the use of integrating theory into a practical smart phone or tablet application as a means to bridge the gap between traditional teaching styles and current learning styles. The application presented here is called “Speech Enhancement for Android (SEA)” and aims at assisting in the development of an intuitive understanding of course content by allowing students to interact with theoretical concepts through their personal device. SEA not only allows the student to interact with speech processing methods, but also enables the student to interact with their surrounding environment by recording and processing their own voice. A case study on students studying DSP for speech processing found that by using SEA as an additional learning tool enhanced their understanding and helped to motivate students to engage in course work by way of having ready access to interactive content on a hand held device. This paper describes the platform in detail acting as a road-map for education institutions, and how it can be integrated into a DSP based speech processing education framework.
    Speech Communication 02/2014; 57:13–38. DOI:10.1016/j.specom.2013.08.002 · 1.55 Impact Factor
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    ABSTRACT: Prediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology. In this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins. By applying SVM to our extracted features, for the first time we enhance the protein structural class prediction accuracy to over 90% and 85% for two popular low-homology benchmarks that have been widely used in the literature. We report 92.2% and 86.3% prediction accuracies for 25PDB and 1189 benchmarks which are respectively up to 7.9% and 2.8% better than previously reported results for these two benchmarks. By proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.
    BMC Genomics 01/2014; 15 Suppl 1(Suppl 1):S2. DOI:10.1186/1471-2164-15-S1-S2 · 4.04 Impact Factor
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    Stephen So · Kuldip K. Paliwal · Zheng-Hua Tan · Børge Lindberg
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    James Lyons · Neela Biswas · Alok Sharma · Abdollah Dehzangi · Kuldip K. Paliwal
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    ABSTRACT: In protein fold recognition, a protein is classified into one of its folds. The recognition of a protein fold can be done by employing feature extraction methods to extract relevant information from protein sequences and then by using a classifier to accurately recognize novel protein sequences. In the past, several feature extraction methods have been developed but with limited recognition accuracy only. Protein sequences of varying lengths share the same fold and therefore they are very similar (in a fold) if aligned properly. To this, we develop an amino acid alignment method to extract important features from protein sequences by computing dissimilarity distances between proteins. This is done by measuring distance between two respective position specific scoring matrices of protein sequences which is used in a support vector machine framework. We demonstrated the effectiveness of the proposed method on several benchmark datasets. The method shows significant improvement in the fold recognition performance which is in the range of 4.3–7.6% compared to several other existing feature extraction methods.
    Journal of Theoretical Biology 01/2014; 354:137–145. · 2.30 Impact Factor

Publication Stats

5k Citations
336.90 Total Impact Points

Institutions

  • 1995–2015
    • Griffith University
      • School of Engineering
      Southport, Queensland, Australia
  • 2003
    • Idiap Research Institute
      Martigny, Valais, Switzerland
    • Norwegian University of Science and Technology
      Nidaros, Sør-Trøndelag, Norway
  • 1981–1999
    • Tata Institute of Fundamental Research
      Mumbai, Mahārāshtra, India
  • 1996
    • Carnegie Mellon University
      • Computer Science Department
      Pittsburgh, Pennsylvania, United States
  • 1991
    • AT&T Labs
      Austin, Texas, United States