Fractal dimension-based viability analysis of cancer cell lines in lens-free holographic microscopy via machine learning

2021 ◽  
Author(s):  
Muhammed Ali Pala ◽  
Murat Erhan Çimen ◽  
Akif Akgül ◽  
Mustafa Zahid Yıldız ◽  
Ali Fuat Boz
Keyword(s):  
Machine Learning ◽  
Fractal Dimension ◽  
Cell Lines ◽  
Cancer Cell ◽  
Cancer Cell Lines ◽  
Viability Analysis ◽  
Holographic Microscopy

scholarly journals Prediction of chemoresistance trait of cancer cell lines using machine learning algorithms and systems biology analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Atousa Ataei ◽  
Niloufar Seyed Majidi ◽  
Javad Zahiri ◽  
Mehrdad Rostami ◽  
S. Shahriar Arab ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systems Biology ◽  
Cell Lines ◽  
Cancer Cell ◽  
Learning Algorithms ◽  
Topological Analysis ◽  
Cancer Cell Lines ◽  
Machine Learning Algorithms ◽  
Cancer Drug ◽  
Fisher Score

AbstractMost of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

scholarly journals Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jianpeng Xue ◽  
Yang Pu ◽  
Jason Smith ◽  
Xin Gao ◽  
Chun Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Cells ◽  
Cell Lines ◽  
Cancer Cell ◽  
Prostate Cancer Cell ◽  
Cancer Cell Lines ◽  
Prostate Cancer Cells ◽  
Prostate Cancer Cell Lines ◽  
Native Fluorescence

AbstractMetastasis is the leading cause of mortalities in cancer patients due to the spreading of cancer cells to various organs. Detecting cancer and identifying its metastatic potential at the early stage is important. This may be achieved based on the quantification of the key biomolecular components within tissues and cells using recent optical spectroscopic techniques. The aim of this study was to develop a noninvasive label-free optical biopsy technique to retrieve the characteristic molecular information for detecting different metastatic potentials of prostate cancer cells. Herein we report using native fluorescence (NFL) spectroscopy along with machine learning (ML) to differentiate prostate cancer cells with different metastatic abilities. The ML algorithms including principal component analysis (PCA) and nonnegative matrix factorization (NMF) were used for dimension reduction and feature detection. The characteristic component spectra were used to identify the key biomolecules that are correlated with metastatic potentials. The relative concentrations of the molecular spectral components were retrieved and used to classify the cancer cells with different metastatic potentials. A multi-class classification was performed using support vector machines (SVMs). The NFL spectral data were collected from three prostate cancer cell lines with different levels of metastatic potentials. The key biomolecules in the prostate cancer cells were identified to be tryptophan, reduced nicotinamide adenine dinucleotide (NADH) and hypothetically lactate as well. The cancer cells with different metastatic potentials were classified with high accuracy using the relative concentrations of the key molecular components. The results suggest that the changes in the relative concentrations of these key fluorophores retrieved from NFL spectra may present potential criteria for detecting prostate cancer cells of different metastatic abilities.

scholarly journals Drug Sensitivity Prediction From Cell Line-Based Pharmacogenomics Data: Guidelines for Developing Machine Learning Models

2021 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Soheil Jahangiri-Tazehkand ◽  
Casey Hon ◽  
Petr Smirnov ◽  
Anthony Mammoliti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Drug Sensitivity ◽  
Cancer Cell Lines ◽  
Precision Oncology ◽  
Learning Models ◽  
Drug Sensitivity Prediction ◽  
Machine Learning Models

ABSTRACTThe goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training a predictor using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors, and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. Application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.

scholarly journals Prediction of Chemoresistance Trait of Cancer Cell Lines using Machine Learning Algorithms and Systems Biology Analysis

2021 ◽  
Author(s):  
Atousa Ataei ◽  
Niloufar Seyed Majidi ◽  
Javad Zahiri ◽  
Mehrdad Rostami ◽  
Seyed Shahriar Arab ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systems Biology ◽  
Cell Lines ◽  
Cancer Cell ◽  
Learning Algorithms ◽  
Topological Analysis ◽  
Cancer Cell Lines ◽  
Machine Learning Algorithms ◽  
Cancer Drug ◽  
Fisher Score

Abstract Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.

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