Abstract
In this paper a CAD system was designed for the classification of Protein Kinase B (PKB) using ten different discrete wavelet transforms and SSVM and SVM classifier. A set of different images has been collected from which data is divided into training and testing data set. The PKB is categorized into two classes called absent or present. The highest overall classification accuracy of 80% was obtained with biorthogonal: bior 4.4 wavelet transforms and daubechies: db6 wavelet transforms using SSVM classifier.
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Jain S, Chauhan DS (2015) Linear and non linear modeling of Protein Kinase B/AkT. In: Proceeding of the international conference on information and communication technology for sustainable development, Ahmedabad, India. pp 81–88
Jain S (2012) Communication of signals and responses leading to cell survival/cell death using Engineered Regulatory Networks. PhD Dissertation, Jaypee University of Information Technology, Solan, Himachal Pradesh, India
Weiss R (2001) Cellular computation and communications using engineered genetic regulatory networks. PhD Dissertation, MIT
Libermann TA, Razon TA, Bartal AD et al (1984) Expression of epidermal growth factor receptors in human brain tumors. Cancer Res 44:753–760
Normanno N, De Luca A, Bianco C et al (2006) Epidermal growth factor receptor (EGFR) signaling. Cancer Gene 366:2–16
Ullrich A, Schlessinger J (1990) Signal transduction by receptors with tyrosine kinase activity. Cell 61:203–211
Jain S, Chauhan DS (2015) mathematical analysis of receptors for survival proteins. Int J Pharma Bio Sci 6(3):164–176
Lizcano JM, Alessi DR (2002) The insulin signalling pathway. Curr Biol 12:236–238
White MF (2003) Insulin signaling in health and disease. Science 302:1710–1711
Jain S, Naik PK, Bhooshan SV (2011) Mathematical modeling deciphering balance between cell survival and cell death using insulin. Netw Biol 1(1):46–58
Jain S, Naik PK, Bhooshan SV (2010) A system model for cell death/survival using SPICE and ladder logic. Digest J Nanomater Biostruct 5(1):57–66
Jain S, Naik PK (2012) System modeling of cell survival and cell death: a deterministic model using Fuzzy System. Int J Pharma BioSci 3(4):358–373
Jain S (2015) Mathematical analysis and probability density function of FKHR pathway for cell survival/death. In: Proceedings of the control system and power electronics—CSPE, Bangalore. pp 84–93
Gaudet S, Kevin JA, John AG et al (2005) A compendium of signals and responses triggered by prodeath and prosurvival cytokines. Manuscript M500158-MCP200, 2005
Kevin JA, John AG, Suzanne G et al (2005) A systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310:1646–1653
Thoma B, Grell M, Pfizenmaier K, Scheurich P (1990) Identification of a 60-kD tumor necrosis factor (TNF) receptor as the major signal transducing component in TNF responses. J Exp Med 172:1019–1023
Jain S, Naik PK, Bhooshan SV (2011) Mathematical modeling deciphering balance between cell survival and cell death using tumor necrosis factor α. Res J Pharm Biol Chem Sci 2(3):574–583
Raja BK, Madheswaran M, Thyagarajah K (2010) Texture pattern analysis of kidney tissues for disorder identification and classification using dominant Gabor wavelet. Mach Vision Appl 21(3):287–300
Rana S, Jain S, Virmani J (2016) Classification of kidney Lesions using gabor wavelet texture features. In: Proceeding of the 10th INDIACom 3rd 2016 international conference on computing for sustainable global development. pp 2528–2532
Bhusri S, Jain S, Virmani J (2016) Classification of breast Lesions based on Laws’ feature extraction techniques. In: Proceeding of the 10th INDIACom 3rd 2016 international conference on computing for sustainable global development. pp 2523–2527
Subramanya MB, Kumar V, Mukherjee S, Saini M (2014) SVM-Based CAC system for B-mode kidney ultrasound images. J Digit Imaging Soc Imaging Inf Med 28(4):448–458
Bhusri S, Jain S, Virmani J (2016) Breast Lesions classification using the amalagation of morphological and texture features. Int J Pharma BioSci 7(2):617–624
Rana S, Jain S, Virmani J (2016) SVM-based characterization of focal kidney Lesions from B-mode ultrasound images. Res J Pharm Biol Chem Sci 7(4):837–846
Bhusri S, Jain S, Virmani J (2016) Classification of breast lesions using the difference of statistical features. Res J Pharm Biol Chem Sci 7(4):1365–1372
Jain S, Naik PK, Bhooshan SV (2011) Nonlinear modeling of cell survival/death using artificial neural network. 2011. In: The proceedings of international conference on computational intelligence and communication networks, Gwalior, India. pp 565–568
Jain S, Naik PK, Bhooshan SV (2010) Petri net implementation of cell signaling for cell death. Int J Pharma Bio Sci 1(2):1–18
Virmani J, Kumar V, Kalra N, Khandelwal N (2011) Prediction of cirrhosis from liver ultrasound B-mode images based on Laws’ masks analysis. In: The proceedings of IEEE international conference on image information processing, ICIIP-2011, Waknaghat, HP, India. pp 1–5
Virmani J, Kumar V, Kalra N, Khandelwal N (2013) SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors. J Digit Imaging 26:530–543
LIBSVM (2016) http://www.csie.ntu.edu.tw/~cjlin/libsvm. Accessed 15 Jan 2016
Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2:1–43
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Jain, S. Classification of Protein Kinase B using discrete wavelet transform. Int. j. inf. tecnol. 10, 211–216 (2018). https://doi.org/10.1007/s41870-018-0090-7
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DOI: https://doi.org/10.1007/s41870-018-0090-7