PATTERNS PREDICTION OF CHEMOTHERAPY SENSITIVITY IN CANCER CELL LINES USING FTIR SPECTRUM, NEURAL NETWORK AND PRINCIPAL COMPONENTS ANALYSIS

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ZENDEHDEL REZVAN; MASOUDI NEJAD ALI; SHIRAZI FARSHAD H.

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Journal: Iranian Journal of Pharmaceutical Research Year:2012 | Volume:11 | Issue:2 Page(s): 401-410

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Title

PATTERNS PREDICTION OF CHEMOTHERAPY SENSITIVITY IN CANCER CELL LINES USING FTIR SPECTRUM, NEURAL NETWORK AND PRINCIPAL COMPONENTS ANALYSIS

Author(s)

ZENDEHDEL REZVAN | MASOUDI NEJAD ALI | SHIRAZI FARSHAD H. | Issue Writer Certificate

Keywords

DRUG RESISTANT

FOURIER TRANSFORM INFRARED

PRINCIPLE COMPONENT ANALYSIS

ARTIFICIAL NEURONAL NETWORK

LINEAR DISCRIMINATE ANALYSIS

PATTERN RECOGNITION

Abstract

Drug resistance enables cancer cells to break away from cytotoxic effect of anticancer drugs. Identification of resistant phenotype is very important because it can lead to effective treatment plan. There is an interest in developing classifying models of resistance phenotype based on the multivariate data. We have investigated a vibrational spectroscopic approach in order to characterize a sensitive human ovarian cell line, A2780, and its cisplatin-resistant derivative, A2780-cp. In this study FTIR method have been evaluated via the use of principal components analysis (PCA), ANN (artificial neuronal network) and LDA (linear discriminate analysis). FTIR spectroscopy on these cells in the range of 400-4000 cm-1 showed alteration in the secondary structure of proteins and a CH stretching vibration. We have found that the ANN models correctly classified more than 95% of the cell lines, while the LDA models with the same data sets could classify 85% of cases. In the process of different ranges of spectra, the best classification of data set in the range of 1000-2000 cm-1 was done using ANN model, while the data set between 2500-3000 cm-1 was more correctly classified with the LDA model. PCA of the spectral data also provide a good separation for representing the variety of cell line spectra. Our work supports the promise of ANN analysis of FTIR spectrum as a supervised powerful approach and PCA as unsupervised modeling for the development of automated methods to determine the resistant phenotype of cancer classification.

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APA: Copy

ZENDEHDEL, REZVAN, MASOUDI NEJAD, ALI, & SHIRAZI, FARSHAD H.. (2012). PATTERNS PREDICTION OF CHEMOTHERAPY SENSITIVITY IN CANCER CELL LINES USING FTIR SPECTRUM, NEURAL NETWORK AND PRINCIPAL COMPONENTS ANALYSIS. IRANIAN JOURNAL OF PHARMACEUTICAL RESEARCH (IJPR), 11(2), 401-410. SID. https://sid.ir/paper/287956/en

Vancouver: Copy

ZENDEHDEL REZVAN, MASOUDI NEJAD ALI, SHIRAZI FARSHAD H.. PATTERNS PREDICTION OF CHEMOTHERAPY SENSITIVITY IN CANCER CELL LINES USING FTIR SPECTRUM, NEURAL NETWORK AND PRINCIPAL COMPONENTS ANALYSIS. IRANIAN JOURNAL OF PHARMACEUTICAL RESEARCH (IJPR)[Internet]. 2012;11(2):401-410. Available from: https://sid.ir/paper/287956/en

IEEE: Copy

REZVAN ZENDEHDEL, ALI MASOUDI NEJAD, and FARSHAD H. SHIRAZI, “PATTERNS PREDICTION OF CHEMOTHERAPY SENSITIVITY IN CANCER CELL LINES USING FTIR SPECTRUM, NEURAL NETWORK AND PRINCIPAL COMPONENTS ANALYSIS,” IRANIAN JOURNAL OF PHARMACEUTICAL RESEARCH (IJPR), vol. 11, no. 2, pp. 401–410, 2012, [Online]. Available: https://sid.ir/paper/287956/en

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