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Barbé L, Lam S, Holub A, Faghihmonzavi Z, Deng M, Iyer R, Finkbeiner S. AutoComet: A fully automated algorithm to quickly and accurately analyze comet assays. Redox Biol 2023; 62:102680. [PMID: 37001328 PMCID: PMC10090439 DOI: 10.1016/j.redox.2023.102680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/08/2023] [Accepted: 03/15/2023] [Indexed: 04/16/2023] Open
Abstract
DNA damage is a common cellular feature seen in cancer and neurodegenerative disease, but fast and accurate methods for quantifying DNA damage are lacking. Comet assays are a biochemical tool to measure DNA damage based on the migration of broken DNA strands towards a positive electrode, which creates a quantifiable 'tail' behind the cell. However, a major limitation of this approach is the time needed for analysis of comets in the images with available open-source algorithms. The requirement for manual curation and the laborious pre- and post-processing steps can take hours to days. To overcome these limitations, we developed AutoComet, a new open-source algorithm for comet analysis that utilizes automated comet segmentation and quantification of tail parameters. AutoComet first segments and filters comets based on size and intensity and then filters out comets without a well-connected head and tail, which significantly increases segmentation accuracy. Because AutoComet is fully automated, it minimizes curator bias and is scalable, decreasing analysis time over ten-fold, to less than 3 s per comet. AutoComet successfully detected statistically significant differences in tail parameters between cells with and without induced DNA damage, and was more comparable to the results of manual curation than other open-source software analysis programs. We conclude that the AutoComet algorithm provides a fast, unbiased and accurate method to quantify DNA damage that avoids the inherent limitations of manual curation and will significantly improve the ability to detect DNA damage.
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Affiliation(s)
- Lise Barbé
- Center for Systems and Therapeutics, Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA
| | - Stephanie Lam
- Center for Systems and Therapeutics, Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA
| | - Austin Holub
- Center for Systems and Therapeutics, Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA
| | - Zohreh Faghihmonzavi
- Center for Systems and Therapeutics, Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA
| | - Minnie Deng
- Center for Systems and Therapeutics, Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA
| | - Rajshri Iyer
- Center for Systems and Therapeutics, Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA
| | - Steven Finkbeiner
- Center for Systems and Therapeutics, Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA; Departments of Neurology and Physiology, University of California, San Francisco, San Francisco, CA, 94158, USA.
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Convolution-layer parameters optimization in Convolutional Neural Networks. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Abstract
Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared to in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists.
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Affiliation(s)
- Zhoumeng Lin
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA.,Center for Environmental and Human Toxicology, University of Florida, FL, 32608, USA
| | - Wei-Chun Chou
- Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, 32610, USA.,Center for Environmental and Human Toxicology, University of Florida, FL, 32608, USA
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Paul T, Vainio S, Roning J. Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network. EXPERT SYSTEMS WITH APPLICATIONS 2022; 194:116559. [PMID: 35095217 PMCID: PMC8779865 DOI: 10.1016/j.eswa.2022.116559] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 12/29/2021] [Accepted: 01/16/2022] [Indexed: 05/06/2023]
Abstract
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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Affiliation(s)
- Tirthankar Paul
- InfoTech Oulu, Faculty of Information Technology and Electrical Engineering, Biomimetics and Intelligent Systems Group (BISG), University of Oulu, Oulu, Finland
| | - Seppo Vainio
- Infotech Oulu and Kvantum Institute, Faculty of Biochemistry and Molecular Medicine, Disease Networks, University of Oulu, Oulu, Finland
| | - Juha Roning
- InfoTech Oulu, Faculty of Information Technology and Electrical Engineering, Biomimetics and Intelligent Systems Group (BISG), University of Oulu, Oulu, Finland
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Davidovic LM, Cumic J, Dugalic S, Vicentic S, Sevarac Z, Petroianu G, Corridon P, Pantic I. Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2022; 28:265-271. [PMID: 34937605 DOI: 10.1017/s1431927621013878] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
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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Affiliation(s)
| | - Jelena Cumic
- University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Dr. Koste Todorovica 8, RS-11129 Belgrade, Serbia
| | - Stefan Dugalic
- University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Dr. Koste Todorovica 8, RS-11129 Belgrade, Serbia
| | - Sreten Vicentic
- University of Belgrade, Faculty of Medicine, University Clinical Center of Serbia, Clinic of Psychiatry, Pasterova 2, RS-11000 Belgrade, Serbia
| | - Zoran Sevarac
- University of Belgrade, Faculty of Organizational Sciences, Jove Ilica 154, RS-11000 Belgrade, Serbia
| | - Georg Petroianu
- Department of Pharmacology & Therapeutics, Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE
| | - Peter Corridon
- Department of Immunology and Physiology, College of Medicine and Health Sciences; Biomedical Engineering, Healthcare Engineering Innovation Center; Center for Biotechnology; Khalifa University of Science and Technology, PO Box 127788, Abu Dhabi, UAE
| | - Igor Pantic
- University of Belgrade, Faculty of Medicine, Department of Medical Physiology, Laboratory for Cellular Physiology, Visegradska 26/II, RS-11129 Belgrade, Serbia
- University of Haifa, 199 Abba Hushi Blvd. Mount Carmel, HaifaIL-3498838, Israel
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Application of artificial intelligence for detection of chemico-biological interactions associated with oxidative stress and DNA damage. Chem Biol Interact 2021; 345:109533. [PMID: 34051207 DOI: 10.1016/j.cbi.2021.109533] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 05/17/2021] [Accepted: 05/24/2021] [Indexed: 12/16/2022]
Abstract
In recent years, various AI-based methods have been developed in order to uncover chemico-biological interactions associated with DNA damage and oxidative stress. Various decision trees, bayesian networks, random forests, logistic regression models, support vector machines as well as deep learning tools, have great potential in the area of molecular biology and toxicology, and it is estimated that in the future, they will greatly contribute to our understanding of molecular and cellular mechanisms associated with DNA damage and repair. In this concise review, we discuss recent attempts to build machine learning tools for assessment of radiation - induced DNA damage as well as algorithms that can analyze the data from the most frequently used DNA damage assays in molecular biology. We also review recent works on the detection of antioxidant proteins with machine learning, and the use of AI-related methods for prediction and evaluation of noncoding DNA sequences. Finally, we discuss previously published research on the potential application of machine learning tools in aging research.
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Hong Y, Han HJ, Lee H, Lee D, Ko J, Hong ZY, Lee JY, Seok JH, Lim HS, Son WC, Sohn I. Deep learning method for comet segmentation and comet assay image analysis. Sci Rep 2020; 10:18915. [PMID: 33144610 PMCID: PMC7609680 DOI: 10.1038/s41598-020-75592-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 10/12/2020] [Indexed: 12/27/2022] Open
Abstract
Comet assay is a widely used method, especially in the field of genotoxicity, to quantify and measure DNA damage visually at the level of individual cells with high sensitivity and efficiency. Generally, computer programs are used to analyze comet assay output images following two main steps. First, each comet region must be located and segmented, and next, it is scored using common metrics (e.g., tail length and tail moment). Currently, most studies on comet assay image analysis have adopted hand-crafted features rather than the recent and effective deep learning (DL) methods. In this paper, however, we propose a DL-based baseline method, called DeepComet, for comet segmentation. Furthermore, we created a trainable and testable comet assay image dataset that contains 1037 comet assay images with 8271 manually annotated comet objects. From the comet segmentation test results with the proposed dataset, the DeepComet achieves high average precision (AP), which is an essential metric in image segmentation and detection tasks. A comparative analysis was performed between the DeepComet and the state-of-the-arts automatic comet segmentation programs on the dataset. Besides, we found that the DeepComet records high correlations with a commercial comet analysis tool, which suggests that the DeepComet is suitable for practical application.
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Affiliation(s)
- Yiyu Hong
- Department of R&D Center, Arontier Co., Ltd, Seoul, Republic of Korea
| | - Hyo-Jeong Han
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, Republic of Korea
| | - Hannah Lee
- Asan Institute of Life Sciences, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, Republic of Korea
| | - Donghwan Lee
- Department of R&D Center, Arontier Co., Ltd, Seoul, Republic of Korea
| | - Junsu Ko
- Department of R&D Center, Arontier Co., Ltd, Seoul, Republic of Korea
| | - Zhen-Yu Hong
- Department of R&D Center, Arontier Co., Ltd, Seoul, Republic of Korea
| | - Ji-Young Lee
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, Republic of Korea
| | - Ju-Hyung Seok
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, Republic of Korea
| | - Hee Seon Lim
- Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, Republic of Korea
| | - Woo-Chan Son
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
| | - Insuk Sohn
- Department of R&D Center, Arontier Co., Ltd, Seoul, Republic of Korea.
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Rosati R, Romeo L, Silvestri S, Marcheggiani F, Tiano L, Frontoni E. Faster R-CNN approach for detection and quantification of DNA damage in comet assay images. Comput Biol Med 2020; 123:103912. [PMID: 32658777 DOI: 10.1016/j.compbiomed.2020.103912] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/23/2020] [Accepted: 07/07/2020] [Indexed: 12/30/2022]
Abstract
BACKGROUND AND OBJECTIVE DNA damage analysis can provide valuable information in several areas ranging from the diagnosis/treatment of a disease to the monitoring of the effects of genetic and environmental influences. The evaluation of the damage is determined by comet scoring, which can be performed by a skilled operator with a manual procedure. However, this approach becomes very time-consuming and the operator dependency results in the subjectivity of the damage quantification and thus in a high inter/intra-operator variability. METHODS In this paper, we aim to overcome this issue by introducing a Deep Learning methodology based on Faster R-CNN to completely automatize the overall approach while discovering unseen discriminative patterns in comets. RESULTS The experimental results performed on two real use-case datasets reveal the higher performance (up to mean absolute precision of 0.74) of the proposed methodology against other state-of-the-art approaches. Additionally, the validation procedure performed by expert biologists highlights how the proposed approach is able to unveil true comets, often unseen from the human eye and standard computer vision methodology. CONCLUSIONS This work contributes to the biomedical informatics field by the introduction of a novel approach based on established object detection Deep Learning technique for evaluating the DNA damage. The main contribution is the application of Faster R-CNN for the detection and quantification of DNA damage in comet assay images, by fully automatizing the detection/classification DNA damage task. The experimental results extracted in two real use-case datasets demonstrated (i) the higher robustness of the proposed methodology against other state-of-the-art Deep Learning competitors, (ii) the speeding up of the comet analysis procedure and (iii) the minimization of the intra/inter-operator variability.
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Affiliation(s)
- Riccardo Rosati
- Department of Information Engineering, Polytechnic University of Marche, Via Brecce Bianche 12, 60131 Ancona, Italy.
| | - Luca Romeo
- Department of Information Engineering, Polytechnic University of Marche, Via Brecce Bianche 12, 60131 Ancona, Italy; Computational Statistics and Machine Learning and Cognition, Motion and Neuroscience, Istituto Italiano di Tecnologia, Genova, Italy
| | - Sonia Silvestri
- Biochemistry Department of Life and Environmental Sciences, Polytechnic University of Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
| | - Fabio Marcheggiani
- Biochemistry Department of Life and Environmental Sciences, Polytechnic University of Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
| | - Luca Tiano
- Biochemistry Department of Life and Environmental Sciences, Polytechnic University of Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
| | - Emanuele Frontoni
- Department of Information Engineering, Polytechnic University of Marche, Via Brecce Bianche 12, 60131 Ancona, Italy
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