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Cheng Y, Xu SM, Santucci K, Lindner G, Janitz M. Machine learning and related approaches in transcriptomics. Biochem Biophys Res Commun 2024; 724:150225. [PMID: 38852503 DOI: 10.1016/j.bbrc.2024.150225] [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: 02/25/2024] [Revised: 05/18/2024] [Accepted: 06/03/2024] [Indexed: 06/11/2024]
Abstract
Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.
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Affiliation(s)
- Yuning Cheng
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Si-Mei Xu
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Kristina Santucci
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Grace Lindner
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Michael Janitz
- School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, 2052, Australia.
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2
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Afroz S, Islam N, Habib MA, Reza MS, Ashad Alam M. Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network. Methods 2024; 226:138-150. [PMID: 38670415 DOI: 10.1016/j.ymeth.2024.04.017] [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: 07/18/2023] [Revised: 04/02/2024] [Accepted: 04/20/2024] [Indexed: 04/28/2024] Open
Abstract
In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.
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Affiliation(s)
- Sabrin Afroz
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh
| | - Nadira Islam
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh
| | - Md Ahsan Habib
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Bangladesh; Statistical Learning Group, Bangladesh
| | - Md Selim Reza
- Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA 70112, USA; Statistical Learning Group, Bangladesh
| | - Md Ashad Alam
- Ochsner Center for Outcomes Research, Ochsner Research, Ochsner Clinic Foundation, New Orleans, LA 70121, USA; Statistical Learning Group, Bangladesh.
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3
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Cusworth S, Gkoutos GV, Acharjee A. A novel generative adversarial networks modelling for the class imbalance problem in high dimensional omics data. BMC Med Inform Decis Mak 2024; 24:90. [PMID: 38549123 PMCID: PMC10979623 DOI: 10.1186/s12911-024-02487-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 03/22/2024] [Indexed: 04/01/2024] Open
Abstract
Class imbalance remains a large problem in high-throughput omics analyses, causing bias towards the over-represented class when training machine learning-based classifiers. Oversampling is a common method used to balance classes, allowing for better generalization of the training data. More naive approaches can introduce other biases into the data, being especially sensitive to inaccuracies in the training data, a problem considering the characteristically noisy data obtained in healthcare. This is especially a problem with high-dimensional data. A generative adversarial network-based method is proposed for creating synthetic samples from small, high-dimensional data, to improve upon other more naive generative approaches. The method was compared with 'synthetic minority over-sampling technique' (SMOTE) and 'random oversampling' (RO). Generative methods were validated by training classifiers on the balanced data.
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Affiliation(s)
- Samuel Cusworth
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, UK
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, B15 2TT, Birmingham, UK
- Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, B15 2TT, Birmingham, UK
- MRC Health Data Research UK (HDR), Midlands Site, UK
- Centre for Health Data Research, University of Birmingham, B15 2TT, Birmingham, UK
- NIHR Experimental Cancer Medicine Centre, B15 2TT, Birmingham, UK
| | - Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, B15 2TT, Birmingham, UK.
- Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, B15 2TT, Birmingham, UK.
- MRC Health Data Research UK (HDR), Midlands Site, UK.
- Centre for Health Data Research, University of Birmingham, B15 2TT, Birmingham, UK.
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4
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Kim Y, Cheon M. GAN Learning Methods for Bulk RNA-Seq Data and Their Interpretive Application in the Context of Disease Progression. Methods Mol Biol 2024; 2812:259-274. [PMID: 39068368 DOI: 10.1007/978-1-0716-3886-6_15] [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] [Indexed: 07/30/2024]
Abstract
A generative adversarial network (GAN) is a generative model that consists of two adversarial networks, a discriminator and a generator, usually in the form of neural networks. One of the useful things about applying GANs is that they can synthesize two states to produce an intermediate output that implies a semantic feature. When applied to omics data that determine phenotypes of a disease, GANs can be used to associate these intermediate outputs with the progression of the disease. In this chapter, to realize the above idea, we will introduce the application of GAN methods to bulk RNA-seq data, which cover data preprocessing, training, and latent interpolation between different phenotypes describing disease progression.
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Affiliation(s)
- Yongjin Kim
- Dementia Research Group, Korea Brain Research Institute (KBRI), Daegu, Republic of Korea
| | - Mookyung Cheon
- Dementia Research Group, Korea Brain Research Institute (KBRI), Daegu, Republic of Korea.
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5
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Erfanian N, Heydari AA, Feriz AM, Iañez P, Derakhshani A, Ghasemigol M, Farahpour M, Razavi SM, Nasseri S, Safarpour H, Sahebkar A. Deep learning applications in single-cell genomics and transcriptomics data analysis. Biomed Pharmacother 2023; 165:115077. [PMID: 37393865 DOI: 10.1016/j.biopha.2023.115077] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 06/22/2023] [Accepted: 06/23/2023] [Indexed: 07/04/2023] Open
Abstract
Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.
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Affiliation(s)
- Nafiseh Erfanian
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - A Ali Heydari
- Department of Applied Mathematics, University of California, Merced, CA, USA; Health Sciences Research Institute, University of California, Merced, CA, USA
| | - Adib Miraki Feriz
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Pablo Iañez
- Cellular Systems Genomics Group, Josep Carreras Research Institute, Barcelona, Spain
| | - Afshin Derakhshani
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, Canada
| | | | - Mohsen Farahpour
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
| | - Seyyed Mohammad Razavi
- Department of Electronics, Faculty of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
| | - Saeed Nasseri
- Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran
| | - Hossein Safarpour
- Cellular and Molecular Research Center, Birjand University of Medical Sciences, Birjand, Iran.
| | - Amirhossein Sahebkar
- Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran; Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran; Department of Biotechnology, School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran.
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Abstract
Following the widespread use of deep learning for genomics, deep generative modeling is also becoming a viable methodology for the broad field. Deep generative models (DGMs) can learn the complex structure of genomic data and allow researchers to generate novel genomic instances that retain the real characteristics of the original dataset. Aside from data generation, DGMs can also be used for dimensionality reduction by mapping the data space to a latent space, as well as for prediction tasks via exploitation of this learned mapping or supervised/semi-supervised DGM designs. In this review, we briefly introduce generative modeling and two currently prevailing architectures, we present conceptual applications along with notable examples in functional and evolutionary genomics, and we provide our perspective on potential challenges and future directions.
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Affiliation(s)
- Burak Yelmen
- Laboratoire Interdisciplinaire des Sciences du Numérique, CNRS UMR 9015, INRIA, Université Paris-Saclay, Orsay, France;
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Flora Jay
- Laboratoire Interdisciplinaire des Sciences du Numérique, CNRS UMR 9015, INRIA, Université Paris-Saclay, Orsay, France;
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Lacan A, Sebag M, Hanczar B. GAN-based data augmentation for transcriptomics: survey and comparative assessment. Bioinformatics 2023; 39:i111-i120. [PMID: 37387181 DOI: 10.1093/bioinformatics/btad239] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2023] Open
Abstract
MOTIVATION Transcriptomics data are becoming more accessible due to high-throughput and less costly sequencing methods. However, data scarcity prevents exploiting deep learning models' full predictive power for phenotypes prediction. Artificially enhancing the training sets, namely data augmentation, is suggested as a regularization strategy. Data augmentation corresponds to label-invariant transformations of the training set (e.g. geometric transformations on images and syntax parsing on text data). Such transformations are, unfortunately, unknown in the transcriptomic field. Therefore, deep generative models such as generative adversarial networks (GANs) have been proposed to generate additional samples. In this article, we analyze GAN-based data augmentation strategies with respect to performance indicators and the classification of cancer phenotypes. RESULTS This work highlights a significant boost in binary and multiclass classification performances due to augmentation strategies. Without augmentation, training a classifier on only 50 RNA-seq samples yields an accuracy of, respectively, 94% and 70% for binary and tissue classification. In comparison, we achieved 98% and 94% of accuracy when adding 1000 augmented samples. Richer architectures and more expensive training of the GAN return better augmentation performances and generated data quality overall. Further analysis of the generated data shows that several performance indicators are needed to assess its quality correctly. AVAILABILITY AND IMPLEMENTATION All data used for this research are publicly available and comes from The Cancer Genome Atlas. Reproducible code is available on the GitLab repository: https://forge.ibisc.univ-evry.fr/alacan/GANs-for-transcriptomics.
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Affiliation(s)
- Alice Lacan
- IBISC, University Paris-Saclay (Univ. Evry), Evry 91000, France
| | - Michèle Sebag
- TAU, CNRS-INRIA-LISN, University Paris-Saclay, Gif-sur-Yvette 91190, France
| | - Blaise Hanczar
- IBISC, University Paris-Saclay (Univ. Evry), Evry 91000, France
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Zhang WB, Huang Y, Guo XR, Zhang MQ, Yuan XS, Zu HB. DHCR24 reverses Alzheimer's disease-related pathology and cognitive impairment via increasing hippocampal cholesterol levels in 5xFAD mice. Acta Neuropathol Commun 2023; 11:102. [PMID: 37344916 DOI: 10.1186/s40478-023-01593-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 05/30/2023] [Indexed: 06/23/2023] Open
Abstract
Accumulating evidences reveal that cellular cholesterol deficiency could trigger the onset of Alzheimer's disease (AD). As a key regulator, 24-dehydrocholesterol reductase (DHCR24) controls cellular cholesterol homeostasis, which was found to be downregulated in AD vulnerable regions and involved in AD-related pathological activities. However, DHCR24 as a potential therapeutic target for AD remains to be identified. In present study, we demonstrated the role of DHCR24 in AD by employing delivery of adeno-associated virus carrying DHCR24 gene into the hippocampus of 5xFAD mice. Here, we found that 5xFAD mice had lower levels of cholesterol and DHCR24 expression, and the cholesterol loss was alleviated by DHCR24 overexpression. Surprisingly, the cognitive impairment of 5xFAD mice was significantly reversed after DHCR24-based gene therapy. Moreover, we revealed that DHCR24 knock-in successfully prevented or reversed AD-related pathology in 5xFAD mice, including amyloid-β deposition, synaptic injuries, autophagy, reactive astrocytosis, microglial phagocytosis and apoptosis. In conclusion, our results firstly demonstrated that the potential value of DHCR24-mediated regulation of cellular cholesterol level as a promising treatment for AD.
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Affiliation(s)
- Wen-Bin Zhang
- Department of Neurology, Jinshan Hospital Affiliated to Fudan University, No.1508 Long-Hang Road, Jinshan District, Shanghai, 201508, China
| | - Yue Huang
- Department of Neurology, Jinshan Hospital Affiliated to Fudan University, No.1508 Long-Hang Road, Jinshan District, Shanghai, 201508, China
| | - Xiao-Rou Guo
- Department of Neurology, Jinshan Hospital Affiliated to Fudan University, No.1508 Long-Hang Road, Jinshan District, Shanghai, 201508, China
| | - Meng-Qi Zhang
- Department of Neurology, Jinshan Hospital Affiliated to Fudan University, No.1508 Long-Hang Road, Jinshan District, Shanghai, 201508, China
| | - Xiang-Shan Yuan
- Department of Neurology, Jinshan Hospital Affiliated to Fudan University, No.1508 Long-Hang Road, Jinshan District, Shanghai, 201508, China.
- Department of Anatomy and Histoembryology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science, Fudan University, Shanghai, 200032, China.
| | - Heng-Bing Zu
- Department of Neurology, Jinshan Hospital Affiliated to Fudan University, No.1508 Long-Hang Road, Jinshan District, Shanghai, 201508, China.
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9
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Investigation of early molecular alterations in tauopathy with generative adversarial networks. Sci Rep 2023; 13:732. [PMID: 36639689 PMCID: PMC9839697 DOI: 10.1038/s41598-023-28081-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 01/12/2023] [Indexed: 01/15/2023] Open
Abstract
The recent advances in deep learning-based approaches hold great promise for unravelling biological mechanisms, discovering biomarkers, and predicting gene function. Here, we deployed a deep generative model for simulating the molecular progression of tauopathy and dissecting its early features. We applied generative adversarial networks (GANs) for bulk RNA-seq analysis in a mouse model of tauopathy (TPR50-P301S). The union set of differentially expressed genes from four comparisons (two phenotypes with two time points) was used as input training data. We devised four-way transition curves for a virtual simulation of disease progression, clustered and grouped the curves by patterns, and identified eight distinct pattern groups showing different biological features from Gene Ontology enrichment analyses. Genes that were upregulated in early tauopathy were associated with vasculature development, and these changes preceded immune responses. We confirmed significant disease-associated differences in the public human data for the genes of the different pattern groups. Validation with weighted gene co-expression network analysis suggested that our GAN-based approach can be used to detect distinct patterns of early molecular changes during disease progression, which may be extremely difficult in in vivo experiments. The generative model is a valid systematic approach for exploring the sequential cascades of mechanisms and targeting early molecular events related to dementia.
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Serov N, Vinogradov V. Artificial intelligence to bring nanomedicine to life. Adv Drug Deliv Rev 2022; 184:114194. [PMID: 35283223 DOI: 10.1016/j.addr.2022.114194] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/04/2022] [Accepted: 03/07/2022] [Indexed: 12/13/2022]
Abstract
The technology of drug delivery systems (DDSs) has demonstrated an outstanding performance and effectiveness in production of pharmaceuticals, as it is proved by many FDA-approved nanomedicines that have an enhanced selectivity, manageable drug release kinetics and synergistic therapeutic actions. Nonetheless, to date, the rational design and high-throughput development of nanomaterial-based DDSs for specific purposes is far from a routine practice and is still in its infancy, mainly due to the limitations in scientists' capabilities to effectively acquire, analyze, manage, and comprehend complex and ever-growing sets of experimental data, which is vital to develop DDSs with a set of desired functionalities. At the same time, this task is feasible for the data-driven approaches, high throughput experimentation techniques, process automatization, artificial intelligence (AI) technology, and machine learning (ML) approaches, which is referred to as The Fourth Paradigm of scientific research. Therefore, an integration of these approaches with nanomedicine and nanotechnology can potentially accelerate the rational design and high-throughput development of highly efficient nanoformulated drugs and smart materials with pre-defined functionalities. In this Review, we survey the important results and milestones achieved to date in the application of data science, high throughput, as well as automatization approaches, combined with AI and ML to design and optimize DDSs and related nanomaterials. This manuscript mission is not only to reflect the state-of-art in data-driven nanomedicine, but also show how recent findings in the related fields can transform the nanomedicine's image. We discuss how all these results can be used to boost nanomedicine translation to the clinic, as well as highlight the future directions for the development, data-driven, high throughput experimentation-, and AI-assisted design, as well as the production of nanoformulated drugs and smart materials with pre-defined properties and behavior. This Review will be of high interest to the chemists involved in materials science, nanotechnology, and DDSs development for biomedical applications, although the general nature of the presented approaches enables knowledge translation to many other fields of science.
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Affiliation(s)
- Nikita Serov
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, Saint-Petersburg 191002, Russian Federation
| | - Vladimir Vinogradov
- International Institute "Solution Chemistry of Advanced Materials and Technologies", ITMO University, Saint-Petersburg 191002, Russian Federation.
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11
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Unveiling OASIS family as a key player in hypoxia-ischemia cases induced by cocaine using generative adversarial networks. Sci Rep 2022; 12:6734. [PMID: 35469040 PMCID: PMC9038918 DOI: 10.1038/s41598-022-10772-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/08/2022] [Indexed: 11/17/2022] Open
Abstract
Repeated cocaine use poses many serious health risks to users. One of the risks is hypoxia and ischemia (HI). To restore the biological system against HI, complex biological mechanisms operate at the gene level. Despite the complexity of biological mechanisms, there are common denominator genes that play pivotal roles in various defense systems. Among these genes, the cAMP response element-binding (Creb) protein contributes not only to various aspects of drug-seeking behavior and drug reward, but also to protective mechanisms. However, it is still unclear which Creb members are key players in the protection of cocaine-induced HI conditions. Herein, using one of the state-of-the-art deep learning methods, the generative adversarial network, we revealed that the OASIS family, one of the Creb family, is a key player in various defense mechanisms such as angiogenesis and unfolded protein response against the HI state by unveiling hidden mRNA expression profiles. Furthermore, we identified mysterious kinases in the OASIS family and are able to explain why the prefrontal cortex and hippocampus are vulnerable to HI at the genetic level.
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Qu C, Zou Y, Ma Y, Chen Q, Luo J, Fan H, Jia Z, Gong Q, Chen T. Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta-Analysis. Front Aging Neurosci 2022; 14:841696. [PMID: 35527734 PMCID: PMC9068970 DOI: 10.3389/fnagi.2022.841696] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 03/03/2022] [Indexed: 12/28/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common form of dementia. Currently, only symptomatic management is available, and early diagnosis and intervention are crucial for AD treatment. As a recent deep learning strategy, generative adversarial networks (GANs) are expected to benefit AD diagnosis, but their performance remains to be verified. This study provided a systematic review on the application of the GAN-based deep learning method in the diagnosis of AD and conducted a meta-analysis to evaluate its diagnostic performance. A search of the following electronic databases was performed by two researchers independently in August 2021: MEDLINE (PubMed), Cochrane Library, EMBASE, and Web of Science. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to assess the quality of the included studies. The accuracy of the model applied in the diagnosis of AD was determined by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A bivariate random-effects model was used to calculate the pooled sensitivity and specificity with their 95% CIs. Fourteen studies were included, 11 of which were included in the meta-analysis. The overall quality of the included studies was high according to the QUADAS-2 assessment. For the AD vs. cognitively normal (CN) classification, the GAN-based deep learning method exhibited better performance than the non-GAN method, with significantly higher accuracy (OR 1.425, 95% CI: 1.150–1.766, P = 0.001), pooled sensitivity (0.88 vs. 0.83), pooled specificity (0.93 vs. 0.89), and area under the curve (AUC) of the summary receiver operating characteristic curve (SROC) (0.96 vs. 0.93). For the progressing MCI (pMCI) vs. stable MCI (sMCI) classification, the GAN method exhibited no significant increase in the accuracy (OR 1.149, 95% CI: 0.878–1.505, P = 0.310) or the pooled sensitivity (0.66 vs. 0.66). The pooled specificity and AUC of the SROC in the GAN group were slightly higher than those in the non-GAN group (0.81 vs. 0.78 and 0.81 vs. 0.80, respectively). The present results suggested that the GAN-based deep learning method performed well in the task of AD vs. CN classification. However, the diagnostic performance of GAN in the task of pMCI vs. sMCI classification needs to be improved. Systematic Review Registration: [PROSPERO], Identifier: [CRD42021275294].
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Affiliation(s)
- Changxing Qu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu, China
| | - Yinxi Zou
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- West China School of Medicine, Sichuan University, Chengdu, China
| | - Yingqiao Ma
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qin Chen
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Jiawei Luo
- West China Biomedical Big Data Center, West China Clinical Medical College of Sichuan University, Chengdu, China
| | - Huiyong Fan
- College of Education Science, Bohai University, Jinzhou, China
| | - Zhiyun Jia
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, China
- Qiyong Gong,
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- *Correspondence: Taolin Chen,
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13
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Bai X, Mai M, Yao K, Zhang M, Huang Y, Zhang W, Guo X, Xu Y, Zhang Y, Qurban A, Duan L, Bu J, Zhang J, Wu J, Zhao Y, Yuan X, Zu H. The role of DHCR24 in the pathogenesis of AD: re-cognition of the relationship between cholesterol and AD pathogenesis. Acta Neuropathol Commun 2022; 10:35. [PMID: 35296367 PMCID: PMC8925223 DOI: 10.1186/s40478-022-01338-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 02/27/2022] [Indexed: 02/01/2023] Open
Abstract
Previous studies show that 3β-hydroxysterol-Δ24 reductase (DHCR24) has a remarked decline in the brain of AD patients. In brain cholesterol synthetic metabolism, DHCR24 is known as the heavily key synthetase in cholesterol synthesis. Moreover, mutations of DHCR24 gene result in inhibition of the enzymatic activity of DHCR24, causing brain cholesterol deficiency and desmosterol accumulation. Furthermore, in vitro studies also demonstrated that DHCR24 knockdown lead to the inhibition of cholesterol synthesis, and the decrease of plasma membrane cholesterol and intracellular cholesterol level. Obviously, DHCR24 could play a crucial role in maintaining cholesterol homeostasis via the control of cholesterol synthesis. Over the past two decades, accumulating data suggests that DHCR24 activity is downregulated by major risk factors for AD, suggesting a potential link between DHCR24 downregulation and AD pathogenesis. Thus, the brain cholesterol loss seems to be induced by the major risk factors for AD, suggesting a possible causative link between brain cholesterol loss and AD. According to previous data and our study, we further found that the reduced cholesterol level in plasma membrane and intracellular compartments by the deficiency of DHCR24 activity obviously was involved in β-amyloid generation, tau hyperphosphorylation, apoptosis. Importantly, increasing evidences reveal that the brain cholesterol loss and lipid raft disorganization are obviously linked to neuropathological impairments which are associated with AD pathogenesis. Therefore, based on previous data and research on DHCR24, we suppose that the brain cholesterol deficiency/loss might be involved in the pathogenesis of AD.
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14
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Ahmed KT, Sun J, Cheng S, Yong J, Zhang W. Multi-omics data integration by generative adversarial network. Bioinformatics 2021; 38:179-186. [PMID: 34415323 DOI: 10.1093/bioinformatics/btab608] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/27/2021] [Accepted: 08/18/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals. RESULTS Large-scale experiments on The Cancer Genome Atlas breast cancer, lung cancer and ovarian cancer datasets validate that (i) the model can effectively integrate two omics data (e.g. mRNA and microRNA expression data) and their interaction network (e.g. microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (ii) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals. AVAILABILITY AND IMPLEMENTATION Source code is available at: https://github.com/CompbioLabUCF/omicsGAN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Khandakar Tanvir Ahmed
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
- Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL 32816, USA
| | - Jiao Sun
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
- Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL 32816, USA
| | - Sze Cheng
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
| | - Jeongsik Yong
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
| | - Wei Zhang
- Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA
- Genomics and Bioinformatics Cluster, University of Central Florida, Orlando, FL 32816, USA
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15
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Qu C, Zou Y, Dai Q, Ma Y, He J, Liu Q, Kuang W, Jia Z, Chen T, Gong Q. Advancing diagnostic performance and clinical applicability of deep learning-driven generative adversarial networks for Alzheimer's disease. PSYCHORADIOLOGY 2021; 1:225-248. [PMID: 38666217 PMCID: PMC10917234 DOI: 10.1093/psyrad/kkab017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/18/2021] [Accepted: 11/25/2021] [Indexed: 02/05/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that severely affects the activities of daily living in aged individuals, which typically needs to be diagnosed at an early stage. Generative adversarial networks (GANs) provide a new deep learning method that show good performance in image processing, while it remains to be verified whether a GAN brings benefit in AD diagnosis. The purpose of this research is to systematically review psychoradiological studies on the application of a GAN in the diagnosis of AD from the aspects of classification of AD state and AD-related image processing compared with other methods. In addition, we evaluated the research methodology and provided suggestions from the perspective of clinical application. Compared with other methods, a GAN has higher accuracy in the classification of AD state and better performance in AD-related image processing (e.g. image denoising and segmentation). Most studies used data from public databases but lacked clinical validation, and the process of quantitative assessment and comparison in these studies lacked clinicians' participation, which may have an impact on the improvement of generation effect and generalization ability of the GAN model. The application value of GANs in the classification of AD state and AD-related image processing has been confirmed in reviewed studies. Improvement methods toward better GAN architecture were also discussed in this paper. In sum, the present study demonstrated advancing diagnostic performance and clinical applicability of GAN for AD, and suggested that the future researchers should consider recruiting clinicians to compare the algorithm with clinician manual methods and evaluate the clinical effect of the algorithm.
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Affiliation(s)
- Changxing Qu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610044, China
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu 610044, China
| | - Yinxi Zou
- West China School of Medicine, Sichuan University, Chengdu 610044, China
| | - Qingyi Dai
- State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, West China School of Stomatology, Sichuan University, Chengdu 610044, China
| | - Yingqiao Ma
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610044, China
| | - Jinbo He
- School of Psychology, Central China Normal University, Wuhan 430079, China
| | - Qihong Liu
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu 610065, China
| | - Zhiyun Jia
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610044, China
| | - Taolin Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610044, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan Provience, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610044, China
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu 610041, Sichuan, P.R. China
- Functional and Molecular Imaging Key Laboratory of Sichuan Provience, Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan, P.R. China
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16
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Neethirajan S. Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming. Front Vet Sci 2021; 8:740253. [PMID: 34888374 PMCID: PMC8649769 DOI: 10.3389/fvets.2021.740253] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/02/2021] [Indexed: 11/17/2022] Open
Abstract
Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics (digital avatars or metaverse) where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby productivity, and sustainability of the farming industry. The interactive 3D avatars and the digital twins of farm animals enabled by deepfake technology offers a timely and essential way in the digital transformation toward exploring the subtle nuances of animal behavior and cognition in enhancing farm animal welfare. Without offering conclusive remarks, the presented mini review is exploratory in nature due to the nascent stages of the deepfake technology.
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Affiliation(s)
- Suresh Neethirajan
- Farmworx, Adaptation Physiology Group, Animal Sciences Department, Wageningen University and Research, Wageningen, Netherlands
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17
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Monaco A, Pantaleo E, Amoroso N, Lacalamita A, Lo Giudice C, Fonzino A, Fosso B, Picardi E, Tangaro S, Pesole G, Bellotti R. A primer on machine learning techniques for genomic applications. Comput Struct Biotechnol J 2021; 19:4345-4359. [PMID: 34429852 PMCID: PMC8365460 DOI: 10.1016/j.csbj.2021.07.021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/23/2021] [Accepted: 07/23/2021] [Indexed: 11/28/2022] Open
Abstract
High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous "omic" data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available.
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Affiliation(s)
- Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy
| | - Ester Pantaleo
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "Aldo Moro", Via G. Amendola 173, 70125 Bari, Italy
| | - Nicola Amoroso
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy.,Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via A. Orabona 4, 70125 Bari, Italy
| | - Antonio Lacalamita
- National Institute of Gastroenterology "S. de Bellis", Research Hospital, 70013 Castellana Grotte (Bari), Italy
| | - Claudio Lo Giudice
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università degli Studi di Bari "Aldo Moro", Via A. Orabona 4, 70125 Bari, Italy
| | - Adriano Fonzino
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università degli Studi di Bari "Aldo Moro", Via A. Orabona 4, 70125 Bari, Italy
| | - Bruno Fosso
- Istituto di Biomembrane, Bioenergetica e Biotecnologie Molecolari, Consiglio Nazionale delle Ricerche, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Ernesto Picardi
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università degli Studi di Bari "Aldo Moro", Via A. Orabona 4, 70125 Bari, Italy.,Istituto di Biomembrane, Bioenergetica e Biotecnologie Molecolari, Consiglio Nazionale delle Ricerche, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy.,Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi di Bari "Aldo Moro", Bari, Via G. Amendola 165, 70125 Bari, Italy
| | - Graziano Pesole
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, Università degli Studi di Bari "Aldo Moro", Via A. Orabona 4, 70125 Bari, Italy.,Istituto di Biomembrane, Bioenergetica e Biotecnologie Molecolari, Consiglio Nazionale delle Ricerche, Via G. Amendola 122/O, 70126 Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy.,Dipartimento Interateneo di Fisica "M. Merlin", Università degli Studi di Bari "Aldo Moro", Via G. Amendola 173, 70125 Bari, Italy
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18
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Lin E, Lin CH, Lane HY. Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease. Int J Mol Sci 2021; 22:7911. [PMID: 34360676 PMCID: PMC8347529 DOI: 10.3390/ijms22157911] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/17/2021] [Accepted: 07/22/2021] [Indexed: 12/21/2022] Open
Abstract
A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer's disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA;
- Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan
- School of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung 40402, Taiwan
- Department of Psychiatry, China Medical University Hospital, Taichung 40447, Taiwan
- Brain Disease Research Center, China Medical University Hospital, Taichung 40447, Taiwan
- Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung 41354, Taiwan
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19
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Cho YY, Kwon OH, Chung S. Preferred Endocytosis of Amyloid Precursor Protein from Cholesterol-Enriched Lipid Raft Microdomains. Molecules 2020; 25:molecules25235490. [PMID: 33255194 PMCID: PMC7727664 DOI: 10.3390/molecules25235490] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 11/19/2020] [Accepted: 11/20/2020] [Indexed: 12/20/2022] Open
Abstract
Amyloid precursor protein (APP) at the plasma membrane is internalized via endocytosis and delivered to endo/lysosomes, where neurotoxic amyloid-β (Aβ) is produced via β-, γ-secretases. Hence, endocytosis plays a key role in the processing of APP and subsequent Aβ generation. β-, γ-secretases as well as APP are localized in cholesterol-enriched lipid raft microdomains. However, it is still unclear whether lipid rafts are the site where APP undergoes endocytosis and whether cholesterol levels affect this process. In this study, we found that localization of APP in lipid rafts was increased by elevated cholesterol level. We also showed that increasing or decreasing cholesterol levels increased or decreased APP endocytosis, respectively. When we labeled cell surface APP, APP localized in lipid rafts preferentially underwent endocytosis compared to nonraft-localized APP. In addition, APP endocytosis from lipid rafts was regulated by cholesterol levels. Our results demonstrate for the first time that cholesterol levels regulate the localization of APP in lipid rafts affecting raft-dependent APP endocytosis. Thus, regulating the microdomain localization of APP could offer a new therapeutic strategy for Alzheimer’s disease.
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