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Shanley D, Hogenboom J, Lysen F, Wee L, Lobo Gomes A, Dekker A, Meacham D. Getting real about synthetic data ethics : Are AI ethics principles a good starting point for synthetic data ethics? EMBO Rep 2024; 25:2152-2155. [PMID: 38388694 PMCID: PMC11094102 DOI: 10.1038/s44319-024-00101-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 02/13/2024] [Indexed: 02/24/2024] Open
Abstract
Synthetic data promises to be a viable alternative when data collection and data sharing may not be feasible or cost effective, but it raises distinct ethical issue that merit serious consideration.
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Affiliation(s)
| | | | - Flora Lysen
- Maastricht University, Maastricht, The Netherlands
| | - Leonard Wee
- Maastricht University, Maastricht, The Netherlands
| | | | - Andre Dekker
- Maastricht University, Maastricht, The Netherlands
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2
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Fang K, Wang J, Chen Q, Feng X, Qu Y, Shi J, Xu Z. Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method. PLoS One 2024; 19:e0298287. [PMID: 38593135 PMCID: PMC11003668 DOI: 10.1371/journal.pone.0298287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 01/18/2024] [Indexed: 04/11/2024] Open
Abstract
Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular shapes, blurred edges, and similar color to the background. How to demonstrate good adaptability in the field of image vision by picking up single particles from multiple types of cryo-electron micrographs is currently a challenge in the field of cryo-electron micrographs. This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network's tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM algorithm can better solve the problem of good adaptability in picking single particles of many types of cryo-electron micrographs, improve the accuracy and generalization ability of the single particle picking method, and provide high-quality data support for the three-dimensional reconstruction of a single particle. In this paper, ablation experiments and comparison experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and comprehensively on multiple datasets. The Average Precision is an important evaluation index of the evaluation model, and the optimal Average Precision reached 95.5% in the training stage Swin-cryoEM, and the single particle picking performance was also superior in the prediction stage. This model inherits the advantages of the Swin Transformer detection model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in terms of the single particle detection capability of cryo-electron micrographs.
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Affiliation(s)
- Kun Fang
- Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, China
- Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Hunan Meteorological Bureau, Changsha, Hunan, China
| | - JinLing Wang
- Xiangtan University& China Unicom (Hunan) Industrial Internet Co., Ltd, China Unicom (Hunan), Changsha, Hunan, China
| | - QingFeng Chen
- Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, China
- Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Hunan Meteorological Bureau, Changsha, Hunan, China
| | - Xian Feng
- Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, China
- Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Hunan Meteorological Bureau, Changsha, Hunan, China
| | - YouMing Qu
- Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, China
- Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Hunan Meteorological Bureau, Changsha, Hunan, China
| | - Jiachi Shi
- Hunan Meteorological Information Center, Hunan Meteorological Bureau, Changsha, Hunan, China
- Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Hunan Meteorological Bureau, Changsha, Hunan, China
| | - Zhuomin Xu
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, Hubei, China
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3
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Qin Q, Jiang X, Huo L, Qian J, Yu H, Zhu H, Du W, Cao Y, Zhang X, Huang Q. Computational design and engineering of self-assembling multivalent microproteins with therapeutic potential against SARS-CoV-2. J Nanobiotechnology 2024; 22:58. [PMID: 38341574 PMCID: PMC10858482 DOI: 10.1186/s12951-024-02329-3] [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: 10/10/2023] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
Multivalent drugs targeting homo-oligomeric viral surface proteins, such as the SARS-CoV-2 trimeric spike (S) protein, have the potential to elicit more potent and broad-spectrum therapeutic responses than monovalent drugs by synergistically engaging multiple binding sites on viral targets. However, rational design and engineering of nanoscale multivalent protein drugs are still lacking. Here, we developed a computational approach to engineer self-assembling trivalent microproteins that simultaneously bind to the three receptor binding domains (RBDs) of the S protein. This approach involves four steps: structure-guided linker design, molecular simulation evaluation of self-assembly, experimental validation of self-assembly state, and functional testing. Using this approach, we first designed trivalent constructs of the microprotein miniACE2 (MP) with different trimerization scaffolds and linkers, and found that one of the constructs (MP-5ff) showed high trimerization efficiency, good conformational homogeneity, and strong antiviral neutralizing activity. With its trimerization unit (5ff), we then engineered a trivalent nanobody (Tr67) that exhibited potent and broad neutralizing activity against the dominant Omicron variants, including XBB.1 and XBB.1.5. Cryo-EM complex structure confirmed that Tr67 stably binds to all three RBDs of the Omicron S protein in a synergistic form, locking them in the "3-RBD-up" conformation that could block human receptor (ACE2) binding and potentially facilitate immune clearance. Therefore, our approach provides an effective strategy for engineering potent protein drugs against SARS-CoV-2 and other deadly coronaviruses.
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Affiliation(s)
- Qin Qin
- State Key Laboratory of Genetic Engineering, Shanghai Engineering Research Center of Industrial Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Xinyi Jiang
- State Key Laboratory of Genetic Engineering, Shanghai Engineering Research Center of Industrial Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Liyun Huo
- State Key Laboratory of Genetic Engineering, Shanghai Engineering Research Center of Industrial Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Jiaqiang Qian
- State Key Laboratory of Genetic Engineering, Shanghai Engineering Research Center of Industrial Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | | | - Haixia Zhu
- State Key Laboratory of Genetic Engineering, Shanghai Engineering Research Center of Industrial Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Wenhao Du
- State Key Laboratory of Genetic Engineering, Shanghai Engineering Research Center of Industrial Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Yuhui Cao
- ACROBiosystems Inc, Beijing, 100176, China
| | - Xing Zhang
- ACROBiosystems Inc, Beijing, 100176, China
| | - Qiang Huang
- State Key Laboratory of Genetic Engineering, Shanghai Engineering Research Center of Industrial Microorganisms, MOE Engineering Research Center of Gene Technology, School of Life Sciences, Fudan University, Shanghai, 200438, China.
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, 201203, China.
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Dhakal A, Gyawali R, Wang L, Cheng J. A large expert-curated cryo-EM image dataset for machine learning protein particle picking. Sci Data 2023; 10:392. [PMID: 37349345 PMCID: PMC10287764 DOI: 10.1038/s41597-023-02280-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/30/2023] [Indexed: 06/24/2023] Open
Abstract
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.
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Affiliation(s)
- Ashwin Dhakal
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO, 65211, USA
| | - Rajan Gyawali
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO, 65211, USA
| | - Liguo Wang
- Laboratory for BioMolecular Structure (LBMS), Brookhaven National Laboratory, Upton, NY, 11973, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO, 65211, USA.
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Dhakal A, Gyawali R, Wang L, Cheng J. CryoPPP: A Large Expert-Labelled Cryo-EM Image Dataset for Machine Learning Protein Particle Picking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.21.529443. [PMID: 36865277 PMCID: PMC9980126 DOI: 10.1101/2023.02.21.529443] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Cryo-electron microscopy (cryo-EM) is currently the most powerful technique for determining the structures of large protein complexes and assemblies. Picking single-protein particles from cryo-EM micrographs (images) is a key step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though the emerging machine learning-based particle picking can potentially automate the process, its development is severely hindered by lack of large, high-quality, manually labelled training data. Here, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for single protein particle picking and analysis to address this bottleneck. It consists of manually labelled cryo-EM micrographs of 32 non-redundant, representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). It includes 9,089 diverse, high-resolution micrographs (∼300 cryo-EM images per EMPIAR dataset) in which the coordinates of protein particles were labelled by human experts. The protein particle labelling process was rigorously validated by both 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of machine learning and artificial intelligence methods for automated cryo-EM protein particle picking. The dataset and data processing scripts are available at https://github.com/BioinfoMachineLearning/cryoppp.
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Affiliation(s)
- Ashwin Dhakal
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA. Fax: 573-882-8318
| | - Rajan Gyawali
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA. Fax: 573-882-8318
| | - Liguo Wang
- Laboratory for BioMolecular Structure (LBMS), Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, NextGen Precision Health, University of Missouri, Columbia, MO 65211, USA. Fax: 573-882-8318
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Abstract
Cryo-electron microscopy (CryoEM) has become a vital technique in structural biology. It is an interdisciplinary field that takes advantage of advances in biochemistry, physics, and image processing, among other disciplines. Innovations in these three basic pillars have contributed to the boosting of CryoEM in the past decade. This work reviews the main contributions in image processing to the current reconstruction workflow of single particle analysis (SPA) by CryoEM. Our review emphasizes the time evolution of the algorithms across the different steps of the workflow differentiating between two groups of approaches: analytical methods and deep learning algorithms. We present an analysis of the current state of the art. Finally, we discuss the emerging problems and challenges still to be addressed in the evolution of CryoEM image processing methods in SPA.
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Affiliation(s)
- Jose Luis Vilas
- Biocomputing Unit, Centro
Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - Jose Maria Carazo
- Biocomputing Unit, Centro
Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
| | - Carlos Oscar S. Sorzano
- Biocomputing Unit, Centro
Nacional de Biotecnologia (CNB-CSIC), Darwin, 3, Campus Universidad Autonoma, 28049 Cantoblanco, Madrid, Spain
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Wu JG, Yan Y, Zhang DX, Liu BW, Zheng QB, Xie XL, Liu SQ, Ge SX, Hou ZG, Xia NS. Machine Learning for Structure Determination in Single-Particle Cryo-Electron Microscopy: A Systematic Review. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:452-472. [PMID: 34932487 DOI: 10.1109/tnnls.2021.3131325] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Recently, single-particle cryo-electron microscopy (cryo-EM) has become an indispensable method for determining macromolecular structures at high resolution to deeply explore the relevant molecular mechanism. Its recent breakthrough is mainly because of the rapid advances in hardware and image processing algorithms, especially machine learning. As an essential support of single-particle cryo-EM, machine learning has powered many aspects of structure determination and greatly promoted its development. In this article, we provide a systematic review of the applications of machine learning in this field. Our review begins with a brief introduction of single-particle cryo-EM, followed by the specific tasks and challenges of its image processing. Then, focusing on the workflow of structure determination, we describe relevant machine learning algorithms and applications at different steps, including particle picking, 2-D clustering, 3-D reconstruction, and other steps. As different tasks exhibit distinct characteristics, we introduce the evaluation metrics for each task and summarize their dynamics of technology development. Finally, we discuss the open issues and potential trends in this promising field.
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Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol 2022; 23:40-55. [PMID: 34518686 DOI: 10.1038/s41580-021-00407-0] [Citation(s) in RCA: 564] [Impact Index Per Article: 282.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/23/2021] [Indexed: 02/08/2023]
Abstract
The expanding scale and inherent complexity of biological data have encouraged a growing use of machine learning in biology to build informative and predictive models of the underlying biological processes. All machine learning techniques fit models to data; however, the specific methods are quite varied and can at first glance seem bewildering. In this Review, we aim to provide readers with a gentle introduction to a few key machine learning techniques, including the most recently developed and widely used techniques involving deep neural networks. We describe how different techniques may be suited to specific types of biological data, and also discuss some best practices and points to consider when one is embarking on experiments involving machine learning. Some emerging directions in machine learning methodology are also discussed.
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Affiliation(s)
- Joe G Greener
- Department of Computer Science, University College London, London, UK
| | - Shaun M Kandathil
- Department of Computer Science, University College London, London, UK
| | - Lewis Moffat
- Department of Computer Science, University College London, London, UK
| | - David T Jones
- Department of Computer Science, University College London, London, UK.
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George B, Assaiya A, Roy RJ, Kembhavi A, Chauhan R, Paul G, Kumar J, Philip NS. CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy. Commun Biol 2021; 4:200. [PMID: 33589717 PMCID: PMC7884729 DOI: 10.1038/s42003-021-01721-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 01/19/2021] [Indexed: 11/27/2022] Open
Abstract
Particle identification and selection, which is a prerequisite for high-resolution structure determination of biological macromolecules via single-particle cryo-electron microscopy poses a major bottleneck for automating the steps of structure determination. Here, we present a generalized deep learning tool, CASSPER, for the automated detection and isolation of protein particles in transmission microscope images. This deep learning tool uses Semantic Segmentation and a collection of visually prepared training samples to capture the differences in the transmission intensities of protein, ice, carbon, and other impurities found in the micrograph. CASSPER is a semantic segmentation based method that does pixel-level classification and completely eliminates the need for manual particle picking. Integration of Contrast Limited Adaptive Histogram Equalization (CLAHE) in CASSPER enables high-fidelity particle detection in micrographs with variable ice thickness and contrast. A generalized CASSPER model works with high efficiency on unseen datasets and can potentially pick particles on-the-fly, enabling data processing automation.
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Affiliation(s)
- Blesson George
- Artificial Intelligence Research and Intelligent Systems (airis4D), Thelliyoor, Kerala, India
- Department of Physics, CMS College, Kottayam, Kerala, India
| | - Anshul Assaiya
- Laboratory of Membrane Protein Biology, National Centre for Cell Science, S. P. Pune University Campus, Pune, India
| | - Robin J Roy
- Artificial Intelligence Research and Intelligent Systems (airis4D), Thelliyoor, Kerala, India
| | - Ajit Kembhavi
- Inter-University Centre for Astronomy and Astrophysics (IUCAA), S. P. Pune University Campus, Pune, India
| | - Radha Chauhan
- Laboratory of Structural Biology, National Centre for Cell Science, S. P. Pune University Campus, Pune, India
| | - Geetha Paul
- Artificial Intelligence Research and Intelligent Systems (airis4D), Thelliyoor, Kerala, India
| | - Janesh Kumar
- Laboratory of Membrane Protein Biology, National Centre for Cell Science, S. P. Pune University Campus, Pune, India.
| | - Ninan S Philip
- Artificial Intelligence Research and Intelligent Systems (airis4D), Thelliyoor, Kerala, India.
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Li B, Cao Y, Westhof E, Miao Z. Advances in RNA 3D Structure Modeling Using Experimental Data. Front Genet 2020; 11:574485. [PMID: 33193680 PMCID: PMC7649352 DOI: 10.3389/fgene.2020.574485] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 09/02/2020] [Indexed: 12/26/2022] Open
Abstract
RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.
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Affiliation(s)
- Bing Li
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, Strasbourg, France
| | - Zhichao Miao
- Translational Research Institute of Brain and Brain-Like Intelligence, Department of Anesthesiology, Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine, Shanghai, China
- Newcastle Fibrosis Research Group, Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom
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Meijering E. A bird's-eye view of deep learning in bioimage analysis. Comput Struct Biotechnol J 2020; 18:2312-2325. [PMID: 32994890 PMCID: PMC7494605 DOI: 10.1016/j.csbj.2020.08.003] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/26/2020] [Accepted: 08/01/2020] [Indexed: 02/07/2023] Open
Abstract
Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with potentially far-reaching consequences for healthcare. In this mini-review, we take a bird's-eye view at the past, present, and future developments of deep learning, starting from science at large, to biomedical imaging, and bioimage analysis in particular.
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Affiliation(s)
- Erik Meijering
- School of Computer Science and Engineering & Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia
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