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He S, Guan Y, Cheng CH, Moore TL, Luebke JI, Killiany RJ, Rosene DL, Koo BB, Ou Y. Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age. Front Aging Neurosci 2023; 15:1249415. [PMID: 38020785 PMCID: PMC10646581 DOI: 10.3389/fnagi.2023.1249415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
The application of artificial intelligence (AI) to summarize a whole-brain magnetic resonance image (MRI) into an effective "brain age" metric can provide a holistic, individualized, and objective view of how the brain interacts with various factors (e.g., genetics and lifestyle) during aging. Brain age predictions using deep learning (DL) have been widely used to quantify the developmental status of human brains, but their wider application to serve biomedical purposes is under criticism for requiring large samples and complicated interpretability. Animal models, i.e., rhesus monkeys, have offered a unique lens to understand the human brain - being a species in which aging patterns are similar, for which environmental and lifestyle factors are more readily controlled. However, applying DL methods in animal models suffers from data insufficiency as the availability of animal brain MRIs is limited compared to many thousands of human MRIs. We showed that transfer learning can mitigate the sample size problem, where transferring the pre-trained AI models from 8,859 human brain MRIs improved monkey brain age estimation accuracy and stability. The highest accuracy and stability occurred when transferring the 3D ResNet [mean absolute error (MAE) = 1.83 years] and the 2D global-local transformer (MAE = 1.92 years) models. Our models identified the frontal white matter as the most important feature for monkey brain age predictions, which is consistent with previous histological findings. This first DL-based, anatomically interpretable, and adaptive brain age estimator could broaden the application of AI techniques to various animal or disease samples and widen opportunities for research in non-human primate brains across the lifespan.
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Affiliation(s)
- Sheng He
- Harvard Medical School, Boston Children's Hospital, Boston, MA, United States
| | - Yi Guan
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Chia Hsin Cheng
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Tara L. Moore
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Jennifer I. Luebke
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Ronald J. Killiany
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Douglas L. Rosene
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Bang-Bon Koo
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Yangming Ou
- Department of Anatomy & Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
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Urban A, Sidorenko D, Zagirova D, Kozlova E, Kalashnikov A, Pushkov S, Naumov V, Sarkisova V, Leung GHD, Leung HW, Pun FW, Ozerov IV, Aliper A, Ren F, Zhavoronkov A. Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery. Aging (Albany NY) 2023; 15:4649-4666. [PMID: 37315204 PMCID: PMC10292881 DOI: 10.18632/aging.204788] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 05/24/2023] [Indexed: 06/16/2023]
Abstract
Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, we propose a novel approach to multimodal aging clock we call Precious1GPT utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. While the accuracy of the multimodal transformer is lower within each individual data type compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform.
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Affiliation(s)
- Anatoly Urban
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | | | - Diana Zagirova
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | | | | | - Stefan Pushkov
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | | | | | | | - Hoi Wing Leung
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | - Frank W. Pun
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | - Ivan V. Ozerov
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
| | - Alex Aliper
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
- Insilico Medicine, Masdar City, United Arab Emirates
| | - Feng Ren
- Insilico Medicine, Shanghai, China
| | - Alex Zhavoronkov
- Insilico Medicine, Pak Shek Kok, New Territories, Hong Kong
- Insilico Medicine, Masdar City, United Arab Emirates
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Olsen A, Harpaz Z, Ren C, Shneyderman A, Veviorskiy A, Dralkina M, Konnov S, Shcheglova O, Pun FW, Leung GHD, Leung HW, Ozerov IV, Aliper A, Korzinkin M, Zhavoronkov A. Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform. Aging (Albany NY) 2023; 15:2863-2876. [PMID: 37100462 DOI: 10.18632/aging.204678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/09/2023] [Indexed: 04/28/2023]
Abstract
Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease's negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers. In this work, we present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes. Several studies have recently validated the robustness and applicability of AI-driven computational methods for target identification in both cancer and aging-related diseases. Therefore, we leveraged the AI predictive power of the PandaOmics TargetID engine in order to rank the resulting target hypotheses and prioritize the most promising therapeutic gene targets. We propose cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1) as potential novel dual-purpose therapeutic targets to treat aging and GBM.
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Affiliation(s)
- Andrea Olsen
- The Youth Longevity Association, Sevenoaks, NA, United Kingdom
| | - Zachary Harpaz
- The Youth Longevity Association, Sevenoaks, NA, United Kingdom
- Pine Crest School Science Research Department, Fort Lauderdale, Florida 33334, USA
| | - Christopher Ren
- Shanghai High School International Division, Shanghai 200231, China
| | - Anastasia Shneyderman
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Alexander Veviorskiy
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Maria Dralkina
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Simon Konnov
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Olga Shcheglova
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Frank W Pun
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Geoffrey Ho Duen Leung
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Hoi Wing Leung
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Ivan V Ozerov
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Alex Aliper
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Mikhail Korzinkin
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Alex Zhavoronkov
- Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, New Territories, Hong Kong, China
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Artificial Intelligence, Deep Aging Clocks, and the Advent of ‘Biological Age’: A Christian Critique of AI-Powered Longevity Medicine with Particular Reference to Fasting. RELIGIONS 2022. [DOI: 10.3390/rel13040334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
I argue that the use of artificial intelligence (AI) in longevity medicine to slow human aging encourages individuals to see themselves as managers of their own biology. While such a stance is not entirely unwarranted, it may nevertheless preclude other perspectives of the body as it relates to spiritual formation: namely, the Christian discipline of fasting. Using a christological anthropology informed by Karl Barth, I explore the potential impact of AI-fueled markers such as deep aging clocks (DACs) and the related technological construct of “biological age” (as distinct from chronological age) and how this construct might impact the Christian practice of fasting.
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Zhavoronkov A, Bischof E, Lee KF. Artificial intelligence in longevity medicine. NATURE AGING 2021; 1:5-7. [PMID: 37118000 DOI: 10.1038/s43587-020-00020-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Affiliation(s)
- Alex Zhavoronkov
- Deep Longevity, Inc., Hong Kong, China.
- Insilico Medicine, Inc., Hong Kong, China.
- The Buck Institute for Research on Aging, Novato, CA, USA.
| | - Evelyne Bischof
- University Hospital of Basel, Division of Internal Medicine, University of Basel, Basel, Switzerland
- Shanghai University of Medicine and Health Sciences, College of Clinical Medicine, Shanghai, China
| | - Kai-Fu Lee
- Sinovation Ventures, Beijing, China
- Sinovation AI Institute, Beijing, China
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Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 2020; 4:24. [PMID: 34191197 PMCID: PMC8218106 DOI: 10.1186/s41824-020-00094-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/26/2020] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.
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Affiliation(s)
- Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
- Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Francesco Bartoli
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Andrea Marciano
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberta Zanca
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Riemer H J A Slart
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands
- Faculty of Science and Technology, Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands
| | - Paola A Erba
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands.
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Myskja BK, Steinsbekk KS. Personalized medicine, digital technology and trust: a Kantian account. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2020; 23:577-587. [PMID: 32888101 PMCID: PMC7538445 DOI: 10.1007/s11019-020-09974-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/27/2020] [Indexed: 05/05/2023]
Abstract
Trust relations in the health services have changed from asymmetrical paternalism to symmetrical autonomy-based participation, according to a common account. The promises of personalized medicine emphasizing empowerment of the individual through active participation in managing her health, disease and well-being, is characteristic of symmetrical trust. In the influential Kantian account of autonomy, active participation in management of own health is not only an opportunity, but an obligation. Personalized medicine is made possible by the digitalization of medicine with an ensuing increased tailoring of diagnostics, treatment and prevention to the individual. The ideal is to increase wellness by minimizing the layer of interpretation and translation between relevant health information and the patient or user. Arguably, this opens for a new level of autonomy through increased participation in treatment and prevention, and by that, increased empowerment of the individual. However, the empirical realities reveal a more complicated landscape disturbed by information 'noise' and involving a number of complementary areas of expertise and technologies, hiding the source and logic of data interpretation. This has lead to calls for a return to a mild form of paternalism, allowing expertise coaching of patients and even withholding information, with patients escaping responsibility through blind or lazy trust. This is morally unacceptable, according to Kant's ideal of enlightenment, as we have a duty to take responsibility by trusting others reflexively, even as patients. Realizing the promises of personalized medicine requires a system of institutional controls of information and diagnostics, accessible for non-specialists, supported by medical expertise that can function as the accountable gate-keeper taking moral responsibility required for an active, reflexive trust.
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Affiliation(s)
- Bjørn K Myskja
- Department of Philosophy and Religious Studies, Norwegian University of Science and Technology - NTNU, Trondheim, Norway.
| | - Kristin S Steinsbekk
- Department of Biomedical Laboratory Science, Norwegian University of Science and Technology - NTNU, Trondheim, Norway
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Zhavoronkov A, Li R, Ma C, Mamoshina P. Deep biomarkers of aging and longevity: from research to applications. Aging (Albany NY) 2019; 11:10771-10780. [PMID: 31767810 PMCID: PMC6914424 DOI: 10.18632/aging.102475] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Accepted: 11/08/2019] [Indexed: 12/14/2022]
Abstract
Multiple recent advances in machine learning enabled computer systems to exceed human performance in many tasks including voice, text, and speech recognition and complex strategy games. Aging is a complex multifactorial process driven by and resulting in the many minute changes transpiring at every level of the human organism. Deep learning systems trained on the many measurable features changing in time can generalize and learn the many biological processes on the population and individual levels. The deep age predictors can help advance aging research by establishing causal relationships in non-linear systems. Deep aging clocks can be used for identification of novel therapeutic targets, evaluating the efficacy of the various interventions, data quality control, data economics, prediction of health trajectories, mortality, and many other applications. Here we present the current state of development of the deep aging clocks in the context of the pharmaceutical research and development and clinical applications.
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Affiliation(s)
- Alex Zhavoronkov
- Insilico Medicine, Hong Kong Science and Technology Park, Hong Kong, China
- The Buck Institute for Research on Aging, Novato, CA 84945, USA
- The Biogerontology Research Foundation, London, UK
| | - Ricky Li
- Sinovation Ventures, Beijing, China
| | | | - Polina Mamoshina
- Deep Longevity, Ltd, Hong Kong Science and Technology Park, Hong Kong, China
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