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All Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Global Pharma Technology
Shruti Jain
Jaypee University of Information Technology
Medicine & Pharmacology

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Journal : Journal of Global Pharma Technology

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IMPLEMENTATION OF MARKER PROTEINS USING STANDARDISED EFFECT Shruti Jain
Journal of Global Pharma Technology Volume 09 Issue 05
Publisher : Journal of Global Pharma Technology

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Abstract

In this paper we have discussed the null hypothesized mean,  standardized effect, true population mean, group sample size,  population S.D., type I error rate (Alpha),  critical value ,and power of different marker proteins (AkT, EGFR, ERK, JNK, MK2, IRS and FKHR) theoretically and validate it with Minitab and Statistica Software. Different plots have been plotted between power and sample size, power and standardized effect, power and type 1 error rate. Later we have plotted a correlation graph for different marker proteins which shows mean, SD, max, min, histogram plot, and scatter plot for each marker protein.
Computational Analysis of MK2 Protein for HT Carcinoma Cells using Pre-Processing and Characterization Techniques for Cell Death/ Survival Shruti Jain
Journal of Global Pharma Technology Volume 10 Issue 11 (2018) November 2018
Publisher : Journal of Global Pharma Technology

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Abstract

This paper presents the computational analysis using pre-processing and different characterization of MK2 proteins for cell death/ survival for HT carcinoma cells. Initially data is collected which than pre processed by completing the missing data, removing various outliers, checking whether the data is normal or non- normal. Later various characterisation steps were applied which includes: classification, regression, clustering, association rules etc. Different data variables use different approaches of pre processing and characterisation. In this paper we have collected the different data of MK2 proteins where we have cleaned the data by using various clustering approaches, than regression analysis, correlation matrix and covariance matrixes are calculated. In last we have classified the data by using different machine learning approaches (k-NN, SVM and Neural network). For k-NN we have compared the results of different techniques like chebyshev, city block and Euclidean. For SVM we have used linear, polynomial, RBF and sigmoid function for both types (Type 1 and Type 2) and for neural network we have calculated training, test and validation perfection using different hidden, and output activation function. This paper shows best 10 network using MLP and RBF approach. 
Co-Authors Ayodeji Olalekan Salau Joy Nnenna Eneh