Adaptive diagnostic machine learning technique for classification of cell decisions for AKT protein

Abstract

Artificial intelligence techniques can unravel clinically consistent information in clinical data which in turn assist in decision-making. With the help of state-of-the-art techniques, diagnostic systems are used to identify various diseases by using different machine learning (ML) techniques. In this paper, a combination of ML techniques such as Radial Basis Function (RBF) and Multiple Layer Perceptron (MLP) was used to predict cell decisions (cell survival/death) of AKT protein. AKT signalling networks have various downstream consequences on cellular metabolism either directly through the regulation of nutrient transporters, metabolic enzymes or indirectly through the control of transcription factors that regulate the expression of metabolic pathways which determine cell survival, cell growth, and cell death. Experimental analysis was performed in this work to examine the signalling networks that determine cell survival/death decisions by using an amalgamation of three proteins for ten different combinations in 13 different slices for a period of 0–24 h. P-P plot, Q-Q plot, and histogram tests were used for data visualization to determine which distribution the data fits. In addition, goodness of fit test was also employed using distribution functions such as Weibull, Exponential, and Normal distribution to determine whether the data fits a distribution of a certain population. The results were validated by calculating the MTTF values. The results of the analysis performed show that the Weibull distribution yields remarkable results. Also the results obtained with the Multiple Layer Perceptron, MLP 10-8-1 was found to perform better than other techniques giving an accuracy of 99.33% when the exponential activation function was used. The results of the experimental study indicate that it is possible to create self-consistent cell-signalling compendia based on AKT protein data that have been computationally simulated to provide valuable insights for cell survival/death regulation.