Publications

2021

[1] CAID predictors, Critical assessment of intrinsic disorder prediction, Nature Methods 2021 https://doi.org/10.1038/s41592-021-01117-3. {Sharma R and Sharma A}

[2] Shiu K, Sharma R, Sharma A, OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals, PeerJ, 2021 10.7717/peerj-cs.375

[3] Shiu K, Sharma A, Tsunoda T, Kumarevel T, Sharma A, Forecasting the spread of COVID-19 using LSTM network, BMC Bioinformatics, 2021 (accepted)

[4] Shiu K, Tsunoda T, Sharma A, SPECTRA - a tool for enhanced brain wave signal recognition, BMC Bioinfomtics, 2021 (accepted)

[5] Preterm Birth Prediction Challenge Consortium, Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth, Cell Reports Medicine, Cell Press, 2021 (accepted) {Shiu, Ronesh, Edwin, Alok}

[6] Sharma R, Kumar S, Tsunoda T, Kumarevel T, Sharma A, Single-stranded and double-stranded DNA-binding protein prediction using HMM profiles, Analytical Biochemistry, vol. 612, no. 113954, 2021.

[7] Renanse A, Chandra R, Sharma A, Memory capacity of neural turing machines with matrix representation, arXiv, 1-44, 2021.

[8] Sharma A*, Lysenko A*, Boroevich KA, Vans E, Tusnoda T*, DeepFeature: feature selection in non-image data using convolutional neural network, Briefings in Bioinformatics, doi: 10.1093/bib/bbab297, 2021. Package: https://alok-ai-lab.github.io/deepfeature/

[9] Ahmed S, Muhammod R, Khan ZH, Sharma A, Shatabda S, Dehzangi A, ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides, Scientific Reports 11(1), 1-15, 2021

[10] Miah Md O, Muhammod R, Mamun KAA, Farid D Md, Kumar S, Sharma A, Dehzangi A, CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data, Journal of Neuroscience Methods 364, 109373, 2021.

2020

[1] Ahmad Md W, Arafat Md E, Taherzadeh G, Sharma A, Dipta SR, Dehzangi A, Shatabda S, Mal-light: Enhancing lysine Malonylation sites prediction problem using evolutionary-based features, IEEE Access, vol 8, pp. 77888-77902, 2020.

[2] Wardah W, Dehzangi A, Therzadeh G, Rashid MA, Khan MGM, Tsunoda T, Sharma A, Predicting protein-peptide binding sites with deep convolutional neural network, Journal of Theoretical Biology, vol 496, 110278:1-8, 2020.

[3] Lopez Y, Dehzangi A, Reddy HM, Sharma A, C-iSumo: A sumoylation site predictor that incorporates intrinsic characteristics of amino acid sequences, Computational Biology and Chemistry, 2020 (in press).

[4] Sharma A*, Lysenko A*, Boroevich K, Vans E, Tsunoda T, DeepInsight-FS: Selecting features for non-image data using convolutional neural network, bioRxiv https://doi.org/10.1101/2020.09.17.301515

[5] Sharma R, Kumar S, Tsunoda T, Kumarevel T, Sharma A, Single-stranded and double-stranded DNA-binding protein prediction using HMM profiles, Analytical Biochemistry, vol 612, 113954, 2020

[6] Arafat ME, Ahmad MW, Shovan SM, Dehzangi A, Shubashis RD, Hasan Md. AM, Taherzadeh G, Shatabda S, Sharma A, Accurately predicting glutarylation sites using sequential bi-peptide-based evolutionary features, Genes, vol 11, issue 9, pp 1-16, 2020.

[7] Vans E, Patil A, Sharma A, FEATS: Feature selection based clustering of single-cell RNA-seq data, bioRxiv https://doi.org/10.1101/2020.07.13.200485

[8] Vans E, Patil A, Sharma A, FEATS: Feature selection based clustering of single-cell RNA-seq data, Briefings in Bioinformatics, bbaa306, 1-9, 2020

[9] Shigemizu D, Akiyama S, Higaki S, Sugimoto T, Sakurai T, Boroevich KA, Sharma A, Tsunoda T, Ochiya T, Niida S, Ozaki K, Prognosis prediction model for conversion from mild cognitive impairment to Alzheimer’s disease created by integrative analysis of multi-omics data, Alzheimer's Research & Therapy, vol. 12, issue 1, pp 1-12, 2020

[10] Tanevski J et al., Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data, Life Science Alliance, vol 3, issue 11, 2020 (DREAM SCTC Consortium)

[11] Chandra AA*, Sharma A*, Dehzangi A, Tsunoda T, RAM-PGK: Prediction of Lysine Phosphoglycerylation Based on Residue Adjacency Matrix, Genes, 11(12), pp 1-12, 2020

[12] Singh V*, Sharma A*, Dehzangi A, Tsunoda T, PupStruct: Prediction of Pupylated Lysine Residues Using Structural Properties of Amino Acids , Genes, 11(12), pp 1-13, 2020.

[13] Ahmed S, Hossain Z, Uddin M, Taherzadeh G, Sharma A, Shatabda S, Dehzangi A, Accurate prediction of RNA 5-hydroxymethylcytosine modification by utilizing novel position-specific gapped k-mer descriptors, Computational and Structural Biotechnology Journal, 18, pp 3528-3538, 2020.

[14] Sharma A,*, Lysenko A*, Boroevich K, Vans E, Tsunoda T, DeepInsight-FS: Selecting features for non-image data using convolutional neural network, bioRxiv, doi.org/10.1101/2020.09.17.301515, 2020

[15] Necci M et al., Critical assessment of protein intrinsic disorder prediction, bioRxiv, 10.1101/2020.08.11.245852, 2020 (CAID Predictors)

2019

[1] AstraZeneca-Sanger Drug Combination DREAM Consortium, Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen, Nature Communications, 10:2674, pp. 1-17, 2019. Paper.pdf

[2] Shigemizu, D., Akiyama, S., Asanomi, Y., Boroevich, K.A., Sharma, A., Tsunoda, T., Matsukuma, K., Ichikawa, M., Takizawa, S., Sakurai, T., Ozaki, K., Niida, S.,Risk prediction models for dementia constructed by supervised principal compenent analysis using miRNA expression data, Communications Biology, 2(77), 1-8, 2019 Paper.pdf

[3] Sharma, R.*, Sharma, A.*, Patil, A., Tsunoda, T., Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions, 19(Suppl 13):378, pp. 155-163, BMC Bioinformatics, 2019. Paper.pdf

[4] Reddy, H.M.*, Sharma, A.*, Dehzangi, A., Shigemizu, D., Chandra, A.A., Tatsuhiko, T., Glystruct: glycation prediction using structural properties of amino acid residues, 19(Suppl 13):547, pp. 55-64, BMC Bioinformatics, 2019. Paper.pdf

[5] Sharma, R*, Sharma, A*, Raicar, G, Tsunoda, T, Patil, A, Opal+: length-specific MoRF prediction in intrinsically disordered protein sequences, Proteomics, 19(1800058),1-4, 2019. Paper.pdf Supplement.pdf

[6] Muhammod R, Ahmed S, Farid DM, Shatabda S, Sharma A*, Dehzangi A, PyDeat: a python-based effective feature generation tool for DNA, RNA, and protein sequences, Bioinformatics, 165, 1-2, 2019. Paper.pdf Supplement.pdf

[7] Sharma, A*, Lysenko, A*, Lopez, Y., Dehzangi, A, Reddy, H, Sattar, A, Tsunoda, T, HseSUMO: Sumoylation site prediction using half-sphere exposures of amino acids residues, BMC Genomics, 19(Suppl 9):982, 1-7, 2019 Paper.pdf

[8] Chandra A*, Sharma A*, Dehzangi A, Tsunoda T, EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction, BMC Genomics, 19(Suppl 9):984, 1-9, 2019. Paper.pdf

[9] Kumar S*, Sharma A*, Tsunoda T, Brain wave classification using long short-term memory based OPTICAL predictor, Scientific Reports, 9:9153, 1-13, 2019. Paper.pdf

[10] Kumar S, Sharma A, Tsunoda T, Subject-specific-frequency-bank for motor imagery EEG signal recognition based on common spectral pattern, PRICAI-2019, Eds. A Nayak and A Sharma, Springer's Lecture Notes in Artificial Intelligence, Part II, pp. 712-722, 2019.

[11] Singh V, Sharma A, Chandra A, Dehzangi A, Shigemizu D, Tsunoda T, Computational prediction of lysine pupylation sites in prokaryotic proteins using position specific scoring matrix into bigram for feature extraction, PRICAI-2019, Eds. A Nayak and A Sharma, Springer's Lecture Notes in Artificial Intelligence, Part III, pp. 488-500, 2019.

[12] Vans E, Sharma A, Patil A, Shigemizu D, Tsunoda T, Clustering of small-sample-cell RNA-seq data via feature clustering and selection, PRICAI-2019, Eds. A Nayak and A Sharma, Springer's Lecture Notes in Artificial Intelligence, Part III, pp. 445-456, 2019.

[13] Sharma A*, Vans E, Shigemizu D, Boroevich KA, Tsunoda T*, DeepInsight: a methodology to transform a non-image data to an image for convolution neural network architecture, Scientific Reports, 9:11399, pp. 1-7, 2019. Paper.pdf Supplement.pdf Download DeepInsight Matlab Code: DeepInsight_Pkg.tar.gz Tested on Ubuntu 18.10.

Video Explanation on DeepInsight::
https://www.youtube.com/watch?v=411iwaptk24&feature=youtu.be

[14] Wardah W, Khan MGM, Sharma A, Rashid MA, Protein secondary structure prediction using neural networks and deep learning: a review, Computational Biology and Chemistry 81:1-8, 2019. Paper.pdf

[15] Shigemizu D, Akiyama S, Asanomi Y, Boroevich KA, Sharma A, Tsunoda T, Sakurai T, Ozaki K, Ochiya T, Niida S, A comparison of machine learning classifiers for dimentia with Lewy bodies using miRNA expression data, BMC Medical Genomics 12(1):1-10, 2019. Paper.pdf

[16] Vasighizaker A, Sharma A, Dehzangi A, A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer, PLoS ONE, 14(12):e0226115, 2019.

[17] Chandra A*, Sharma A*, Dehzangi A, Shigemizu D, Tsunoda T, Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix, BMC Molecular and Cell Biology, 2019 (accepted).

[18] Nayak AC, Sharma A (Eds.), PRICAI 2019: Trends in Artificial Intelligence, 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, Aug 26-30, 2019, Proceedings, Part I, pp. 767 (LNCS volume 11670, LNAI volumne 11670)

[19] Nayak AC, Sharma A (Eds.), PRICAI 2019: Trends in Artificial Intelligence, 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, Aug 26-30, 2019, Proceedings, Part II, pp. 729 (LNCS volume 11671, LNAI volumne 11671)

[20] Nayak AC, Sharma A (Eds.), PRICAI 2019: Trends in Artificial Intelligence, 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, Aug 26-30, 2019, Proceedings, Part III, pp. 761 (LNCS volume 11672, LNAI volumne 11672)

2018

[1] Viral Dream Consortium, A crowdsource analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection, Nature Communication, 9:4418, 1-11, 2018. Paper.pdf (Team: Ronesh Sharma, Harsh Saini, and, Alok Sharma)

[2] Uddin, R., Sharma, A., Farid, D., Rahman, M., Dehzangi, A., Shatabda, S., EvoStruct-Sub: an accurate gram-positive protein subcellular localization predictor using evolutionary and structural features, Journal of Theoretical Biology, vol. 443, pp. 138-146, 2018 Paper.pdf.

[3] Sharma, R., Bayarjargal, M., Tsunoda, T., Patil, A. and Sharma, A., MoRFPred-plus: Computational Identification of MoRFs in Protein Sequence using physicochemical properties and HMM profiles, Journal of Theoretical Biology, vol. 437, pp. 9-16, 2018. Paper.pdf Download package https://github.com/roneshsharma/MoRFpred-plus/wiki/MoRFpred-plus:-Download

[4] Sharma, R., Raicar, G., Tsunoda, T., Patil, A., and Sharma, A., OPAL: prediction of MoRF regions in intrinsically disordered protein sequence, Bioinformatics, vol. 34, no. 11, pp. 1850-1858, 2018.Paper.pdf Suppl.zip, Webserver http://www.alok-ai-lab.com/tools/opal/, Download packages: Matlab, Octave

[5] Dehzangi, A, Lopez, Y., Lal, S.P., Taherzadeh, G., Sattar, A., Tsunoda, T., Sharma, A., Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams, PLoS ONE, 13(2):e0191900, 2018 Paper.pdf

[6] López, Y.*, Sharma, A*, Dehzangi, A., Lal, S.P., Taherzadeh, G., Sattar, A., Tsunoda, T., Success: evolutionary and structural properties of amino acids prove effective for succinylation site prediction, BMC Genomics, 19(Suppl 1):923, 2018. Paper.pdf Download https://github.com/YosvanyLopez/Success

[7] Kumar,S., Sharma, A., A new parameter tuning approach for enhanced motor imagery EEG signal classification, Medical & Biological Engineering & Computing, 2018. Paper.pdf Download https://github.com/ShiuKumar/TFPO-CSP

[8] Lopez, Y, Kamola PJ, Sharma R, Shigemizu D, Tsunoda T, Sharma A, Computational Pipelines and Workflows in Bioinformatics, Enclyclopedia of Bioinformatics and Computational Biology, ISBN 9780128114148, Elsevier, 2018, Download/View BookChapter.pdf

[10] Chandra A*, Sharma A*, Dehzangi A, Ranganathan S, Jokhan A, Chou K-C, Tsunoda T, PhoglyStruct: prediction of phosphoglycerylated lysine residues using structural properties of amino acids, Scientific Reports, 8:17923, pp. 1-11, 2018. Paper.pdf

[11] Dehzangi A*, Lopez Y*, Taherzadeh G, Sharma A",Tsunoda T", SumSec: accurate prediction of sumoylation sites using predicted secondary structure, Molecules, 23(12), 1-13, 2018. Paper.pdf

[12] Lysenko A, Sharma A, Boroevich KA, Tsunoda T, An integrative machine learning approach for prediction of toxicity-related drug safety, Life Science Alliance, 1(6) e201800098, 2018. Paper.pdf

2017

[1] Shatabda, S., Saha, S., Sharma, A., Dehzangi, A., iPHLoc-ES: Identification of Bacteriophage Protein Locations using Evolutionary and Structural Features, Journal of Theoretical Biology, vol. 435, pp. 229-237, 2017

[2] Kumar, S., Mamun, K., Sharma, A. CSP-TSM: optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI, Computers in Biology and Medicine, vol. 91, pp. 231-242, 2017

[3] Sharma, A., Boroevich, K.A., Shigemizu, D., Kamatani, Y., Kubo, M., Tsunoda, T., Hierarchical maximum likelihood clustering approach, IEEE Transactions on Bio-Medical Engineering, vol. 64, issue 1, pp. 112-122, 2017. Download package http://emu.src.riken.jp/HML/

[4] López, Y., Dehzangi, A., Lal, S.P., Taherzadeh, G., Michaelson, J., Sattar, A., Tsunoda, T., Sharma, A., SucStruct: prediction of succinylated lysine residues by using structural properties of amino acids, Analytical Biochemistry, vol. 527, pp. 24-32, 2017. Download https://github.com/YosvanyLopez/SucStruct

[5] Dehzangi, A., López, Y., Lal, S.P., Taherzadeh, G., Mchaelson, J., Sattar, A., Tsunoda, T., Sharma, A. PSSM-Suc: accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction, Journal of Theoretical Biology, 425, pp. 97-102, 2017. Download https://github.com/YosvanyLopez/SucEvol

[6] Sharma, A., Kamola, P.J., Tsunoda, T., 2D-EM clustering approach for high-dimensional data through folding feature vectors, BMC Bioinformatics, vol. 18, Suppl 16, pp. 547, 2017. Download package http://emu.src.riken.jp/2D-EM/

[7] Sharma, A., López, Y., Tsunoda, T., Divisive hierarchical maximum likelihood clustering, BMC Bioinformatics, vol. 18, Suppl 16, pp. 546, 2017. Download: http://emu.src.riken.jp/DRAGON/

[8] Kumar, S., Sharma, A., Tsunoda, T., An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information, BMC Bioinformatics, vol. 18, Suppl 16, pp. 545, 2017.

[9] Zaman, R., Chowdhury, S., Rashid, M., Sharma, A., Dehzangi, A., Shatabda, S., HMMBinder: DNA-binding protein prediction using HMM profile based features, BioMed Research International, vol. 2017, Article ID 4590609, 2017.

[10] Y. Yang, R. Heffernan, K. Paliwal, J. Lyons, A. Dehzangi, A. Sharma, J. Wang, A. Sattar, Y. Zhou, SPIDER2: A Package to Predict Secondary S tructure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks, Book Title: Prediction of Protein Secondary Structure, Series Title: Methods Molecular Biology, vol. 1484, ISSN:978-1-4939-6404-8., 2017.

2016

[1] Lyons, J., Paliwal, K.K., Dehzangi, A., Heffernan, R., Tsunoda, T., Sharma, A., Protein fold recognition using HMM-HMM alignment and dynamic programming, Journal of Theoretical Biology, vol. 393, pp. 67-74, 2016.

[2] Raicar, G., Saini, H., Dehzangi, A., Lal, S., Sharma, A., Improving protein fold recognition and structural class prediction accuracies using physicochemical properties of amino acids, Journal of Theoretical Biology, vol. 402, pp. 117-128, 2016.

[3] Saini, H., Raicar, G., Lal, S., Dehzangi, A., Imoto, S., Sharma, A., Protein fold recognition using genetic algorithm optimized voting scheme and profile bigram, Journal of Software, vol. 11, issue 8, pp. 756-767, 2016.

[4] Mamun, K., Sharma, A., Hoque, A.S.M., Szecsi, T., Patient condition monitoring modular hospital robot, Journal of Software, vol. 11, no. 8, pp. 768-786, 2016.

[5] Heffernan, R, Dehzangi, A., Lyons, J., Paliwal, K.K., Sharma, A., Wang, J., Sattar, A., Zhou, Y., Yang, Y., Highly Accurate Sequence-based Prediction of Half-Sphere Exposures of Amino Acid Residues in Proteins, Bioinformatics, vol. 32, no. 6, pp. 843-849, 2016. Download http://sparks-lab.org/

[6] Sharma, A., Shigemizu, D., Boroevich, K.A., López, Y., Kamatani, Y., Kubo, M., Tsunoda, T., Stepwise iterative maximum likelihood clustering approach, BMC Bioinformatics, 17(319),1-14, 2016. Download package http://emu.src.riken.jp/SIML/

[7] Sharma, R., Kumar, S., Tsunoda, T., Patil, A., Sharma, A., Predicting MoRFs in protein sequences using HMM profiles, INCOB 2016, BMC Bioinformatics, Suppl. 15, 2016.

[8] Saini, H., Lal, S., Naidu, V.V., Pickering, V.W., Singh, G., Tsunoda, T., Sharma, A., Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data, INCOB 2016, BMC Medical Genomics, Suppl. 6, 2016.

[9] Kumar, S., Sharma, R., Sharma, A., Tsunoda, T., Decimation Filter with Common Spatial Pattern and Fishers Discriminant Analysis for Motor Imagery Classification, IEEE World Congress on Computational Intelligence, IJCNN, 24-29 July, Vancouver, Canada, pp. 2090-2095, 2016.

[10] Kumar, S., Sharma, A., Mamun, K., Tsunoda, T., A deep learning approach for motor imagery EEG signal classification, IEEE APWC, 2016.

[11] López, Y., Sharma, A., Tsunoda, T., Survey of highly mutated transcription factor binding sites in human cancers, INCOB 2016, Sep 21-23, Singapore (Poster).

[12] Kumar, R., Lal, S.P., Sharma, A., Detecting denial of service attacks in the cloud, 14th Int. Conf. on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), 2016 IEEE 14th Intl C, 309-316, 2016.

2015

[1] Sharma, A. Paliwal, K.K., A Deterministic Approach to Regularized Linear Discriminant Analysis, Neurocomputing, vol. 151, pp. 207-214, 2015.

[2] Dehzangi, A., Sharma, A., Lyons, J., Paliwal, K.K., Sattar, A., A mixture of physicochemical and evolutionary-based feature extraction approached for protein fold recognition, International Journal of Data Mining and Bioinformatics, vol. 11, no. 1, pp. 115-138, 2015.

[3] Heffernan, R. Paliwal, K.K., Lyons, J., Dehzangi, A., Sharma, A., Wang, J. Sattar, A., Yang, Y., Zhou, Y., Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning, Scientific Reports, Nature Publishing Group, vol. 5, article no. 11476, pp. 1-11, 2015. Online Server http://sparks-lab.org/

[4] Saini, H., Raicar, G., Sharma, A., Lal, S., Dehzangi, A., Lyons, J., Paliwal, K.K., Imoto, S., Miyano, S., Probabilistic expression of spatially varied amino acid dimers into general form of Chou’s pseudo amino acid composition for protein fold recognition, Journal of Theoretical Biology, vol. 380, no. 7, pp. 291-298, 2015.

[5] Dehzangi, A., Sohrabi, S., Lyons, J., Sharma, A., Paliwal, K.K., Sattar, A., Gram-positive and gram-negative subcellular localization using rotation forest and physicochemical-based features, BMC Bioinformatics, vol. 16, issue Suppl 4:S1, pp. 1-8, 2015.

[6] Dehzangi, A., Heffernan, R., Sharma, A., Lyons, J., Paliwal, K.K., Sattar, A., Gram-positive and Gram-negative Protein Subcellular Localization by Incorporating Evolutionary-based Descriptors into Chou's General PseAAC, Journal of Theoretical Biology, vol. 364, pp. 284-294, 2015.

[7] Lyons, J., Dehzangi, A., Heffernan, R., Yang, Y., Zhou, Y., Sharma, A., Paliwal, K.K., Advancing the accuracy of protein fold recognition by utilizing profile of hidden Markov models, IEEE Transactions on NanoBioscience, vol 14, issue 7, pp. 761-772, 2015.

[8] Saini, H., Raicar, G., Dehzangi, A., Lal, S., Sharma, A., Subcellular localization for Gram Positive and Gram Negative Bacterial Proteins using Linear Interpolation Smoothing Model, Journal of Theoretical Biology, vol. 386, pp 25-33, 2015.

[9] Sharma, A., Paliwal, K.K., Linear discriminant analysis for the small sample size problem: an overview, International Journal of Machine Learning and Cybernetics, vol. 6, issue 3, pp. 443-454, 2015.

[10] Sharma, R, Dehzangi, A, Lyons, J., Paliwal, KK, Tsunoda, T, Sharma, A., Predict Gram-positive and Gram-negative subcellular localization via incorporating evolutionary information and physicochemical features into Chou’s general PseAAC, IEEE Transactions on NanoBioscience, vol. 14, issue 8, pp. 915-926, 2015.

[11] Sharma, A., Sharma, R., Paliwal, K.K., Dehzangi, A., Lyons, J., Tsunoda, T., Importance of Dimensionality Reduction in Protein Fold Recognition, IEEE Asia-Pacific World Congress on Computer Science and Engineering, pp. 1-6, 2-4 Dec, 2015.

[12] Kumar, S., Sharma, A., Mamun, KA, Tsunoda, T, Application of Cepstrum Analysis and Linear Predictive Coding for Motor Imaginary Task Classification, IEEE Asia-Pacific World Congress on Computer Science and Engineering, pp. 1-6, 2-4 Dec, 2015.