Classification of DNA damages on segmented comet assay images using convolutional neural network

Atila, Ümit; Baydilli, Yusuf Yargı; Sehirli, Eftal; Turan, Muhammed Kamil

doi:10.1016/j.cmpb.2019.105192

Cited by 16 publications

(5 citation statements)

References 15 publications

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“…The assay is today commonly used for the DNA fragmentation measurement. The example would be the recent research of Atila et al (2019) where the authors used a convolutional neural network for classification purposes. However, the use of neural networks in toxicology has not yet reached its full potential, and it is estimated that in the future this topic will continue to be highly relevant in biomedicine and the focus of many research teams around the world.…”

Section: Discussionmentioning

confidence: 99%

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

et al. 2021

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Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

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Section: Discussionmentioning

confidence: 99%

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

et al. 2021

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“…The pretrained deep neural network (PTDNN) classifier uses the acquired FCGR images as input, which correlate to helitron sequences as features. These frequency matrices were used as normalized statistical features (NKmers) to categorize helitron families using two classifiers: the Support Vector Machine (SVM) and the Random Forest (RF).For quantifying DNA damage using comet assay images, researchers in [4] compared Convolutional Neural Network (CNN) to other approaches in the literature.…”

Section: Related Workmentioning

confidence: 99%

“…High-throughput or largescale experimental approaches distinguish it, as well as statistical and computer analysis of the outcomes [2] [3].It's used to track the expression of a large number of genes at the same time. Gene expression refers to the process of translating a gene's DNA sequence into RNA, which serves as a template for the production of proteins, and gene expression level refers to how active a gene is in a particular tissue at a certain time or in a particular experimental circumstance [4]. This technology entails a number of steps.…”

Section: Introductionmentioning

confidence: 99%

Using Genetic Algorithm for DNA Profile Matching

S. Jabir1,

A. Kahlaf

2023

Basrah Researches Sciences

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The DNA is used in forensic investigations to identify suspects and victims at crime scenes. However, manual matching of DNA profiles is difficult and error-prone, especially in large databases. In Iraq, technology for DNA matching is limited, making manual matching the only option. Regenerate. In this work, we propose a Genetic Algorithm (GA) for DNA dataset matching to provide simple and user-friendly software to be used by law enforcement agencies in Iraq. The genetic algorithm is a type of heuristic search method used in computing science and artificial intelligence. It is based on the theory of natural selection and evolutionary biology and is used to find the best solutions to search problems. Genetic algorithm is robust for searching through big, complicated datasets. Thus, in this paper, the GA is the algorithm of choice to achieve the goal of DNA matching search. The used dataset is actual data that have been collected from the Ministry of Interior at the Basra Investigation Center. Finally, the python simulation results show 100% accuracy where the proposed method managed to find the DNAs under consideration precisely.

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“…The technique is one of the deep learning methods applied to solve data analysis and computer vision. It was efficient to classify DNA damages based on comet assay images ( Atila et al, 2020 ). A neural network tool can be beneficial to organize the virus sequences into their family in this context.…”

Section: Introductionmentioning

confidence: 99%

Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network

Tobias

Vainio

Röning

2022

Expert Systems with Applications

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In this study, chaos game representation (CGR) is introduced for investigating the pattern of genome sequences. It is an image representation of the genome for the overall visualization of the sequence. The CGR representation is a mapping technique that assigns each sequence base into the respective position in the two-dimension plane to portray the DNA sequence. Importantly, CGR provides one to one mapping to nucleotides as well as sequence. A coordinate of the CGR plane can tell the corresponding base and its location in the original genome. Therefore, the whole nucleotide sequence (until the current nucleotide) can be restored from the one point of the CGR. In this study, CGR coupled with artificial neural network (ANN) is introduced as a new way to represent the genome and to classify intra-coronavirus sequences. A hierarchy clustering study is done to validate the approach and found to be more than 90% accurate while comparing the result with the phylogenetic tree of the corresponding genomes. Interestingly, the method makes the genome sequence significantly shorter (more than 99% compressed) saving the data space while preserving the genome features.

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Classification of DNA damages on segmented comet assay images using convolutional neural network

Cited by 16 publications

References 15 publications

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

Using Genetic Algorithm for DNA Profile Matching

Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network

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