The Use of ANFIS and RBF to Model and Predict the Inhibitory Concentration Values Determined by MTT Assay on Cancer Cell Lines

Rezaei, Alireza; Noori, Leila; Taghipour, Mostafa

doi:10.5815/ijitcs.2016.04.04

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…In the study of Rezai et al, 2016, the use of the arti cial neural network has also been introduced as a useful, reliable, cheap, and fast tool for predicting lethality in human cancer cells (34).…”

Section: Prediction Of Cellular Viability Using Annmentioning

confidence: 99%

WITHDRAWN: The effect of crystallization level of silica nanoparticles on cell proliferation of MRC-5 cell line and its prediction using artificial neural network

Abbasi

Hashemi

et al. 2020

Preprint

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Background: This study investigated the effect of the level of silica nanoparticles (SiO2NPs) crystallization on the cell proliferation of MRC-5 cells and its prediction using an artificial neural network (ANN).Methods: Variables studied included temperature (70-1000°C), calcination time (2, 12 and 24 hours), and catalyst feed rate (0.01, 0.05 and 0.1mL/min). Cell proliferation was determined by the MTT test after 24 hours of exposure, and results were analyzed using the t-test in MATLAB.Results: the synthesized particles size was less than 50nm, and the XRD peak varied from 30 to 21° during the increase in calcination temperature. The maximum level of crystallization was at 800°C (58% relative to amorphous) with the lowest cell viability. Cell proliferation decreased with increasing concentration of nanoparticles (p<0.05) and increasing feed rate. There was also a positive relationship between increased crystallization and decreased cell proliferation (R2=0.78), but no such association was observed for calcination time. Cell proliferation of MRC-5 was slightly correlated with the linear regression model (MSE>0.12), while ANN was well predicted by the Levenberg–Marquardt algorithm. The suggested structure in this study was 4:10:1 with R2all=0.97, R2test=0.97, RMSE=0.25 and MSE=0.003. The correlation between laboratory results and ANN prediction was 0.94, and the minimum and maximum OD level in the laboratory data and predicted ANN were attributed to 20 and 13 runs.Conclusion: changes in the degree of crystallization of SiO2NPs, an increase in concentration, and the rate of catalyst feed during crystallization of SiO2NPs were practical factors in increasing cytotoxicity.

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Section: Prediction Of Cellular Viability Using Annmentioning

confidence: 99%

WITHDRAWN: The effect of crystallization level of silica nanoparticles on cell proliferation of MRC-5 cell line and its prediction using artificial neural network

Abbasi

Hashemi

et al. 2020

Preprint

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“…AI-based classifiers such as ANN are used the last phase of Computer-Aided Diagnosis (CAD) system [3]. In the literature, these applications are included in many disease and cancer studies in estimating cancer staging [4], and type [5], classification [6], [7], risk assessment [8] and diagnosis of disease [9], [10], decision making [11] and improving the accuracy of cancer survival prediction [12].…”

Section: Introductionmentioning

confidence: 99%

Estimating Breast Cancer Class Using Artificial Neural Network and Logistic Regression Methods

ÖZMEN-AKYOL

2022

Eskişehir Türk Dünyası Uygulama Ve Araştırma Merkezi Bilişim Dergisi

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In this study, artificial neural networks (ANN) and logistic regression (LR) prediction models for breast cancer type has been developed. The proposed models are implemented with real clinical data for breast cancer type prediction. For purpose of constructing the prediction model, there are 699 instances and 10 attributes used in training and testing for the model. The data used in the ANN and LR models are arranged in a format of 9 input parameters and an output parameter which is the class of breast cancer (benign or malignant). The evaluation was made by comparing the data obtained by the two methods. ANN and LR models have accuracy performance 94,78% and 96,18%, respectively. The LR method's accuracy rate is better than the ANN method. This is because the LR method analyzes by taking into account the form of categorical data.

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“…The neural networks have inherent non‐linear characteristics and self‐study ability. Feedforward neural networks (FNNs) have been used in various fields including signal processing [15] and pattern classification [16] etc. However, most of the FNNs are based on the back propagation algorithm which is a non‐linear optimisation technology with a slow convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Significant wave height extraction using a low‐frequency HFSWR system under low sea state

et al. 2018

IET Radar, Sonar & Navigation

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The Use of ANFIS and RBF to Model and Predict the Inhibitory Concentration Values Determined by MTT Assay on Cancer Cell Lines

Cited by 7 publications

References 21 publications

WITHDRAWN: The effect of crystallization level of silica nanoparticles on cell proliferation of MRC-5 cell line and its prediction using artificial neural network

WITHDRAWN: The effect of crystallization level of silica nanoparticles on cell proliferation of MRC-5 cell line and its prediction using artificial neural network

Estimating Breast Cancer Class Using Artificial Neural Network and Logistic Regression Methods

Significant wave height extraction using a low‐frequency HFSWR system under low sea state

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