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Gupta RR. Application of Artificial Intelligence and Machine Learning in Drug Discovery. Methods Mol Biol 2022; 2390:113-124. [PMID: 34731466 DOI: 10.1007/978-1-0716-1787-8_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Machine Learning (ML) and Deep Learning (DL) are two subclasses of Artificial Intelligence (AI), that, in this day and age of big data provides significant opportunities to pharmaceutical discovery research and development by translating data to information and ultimately to knowledge. Machine Learning or AI is not really new but over last few years, application of better methods have emerged and they have been successfully applied for drug discovery and development. This chapter would provide an overview of these methods and how they have been applied across various work streams, e.g., generative chemistry, ADMET prediction, retrosynthetic analysis, etc. within drug discovery process. This chapter would also attempt to provide caution and pit falls in utilizing these methods blindly while summarizing challenges and limitations.
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Affiliation(s)
- Rishi R Gupta
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA.
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52
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Shen X, Wu B, Jiang W, Li Y, Zhang Y, Zhao K, Nie N, Gong L, Liu Y, Zou X, Liu J, Jin J, Ouyang H. Scale bar of aging trajectories for screening personal rejuvenation treatments. Comput Struct Biotechnol J 2022; 20:5750-5760. [DOI: 10.1016/j.csbj.2022.10.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/15/2022] [Accepted: 10/15/2022] [Indexed: 11/27/2022] Open
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53
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Radenkovic D, Zhavoronkov A, Bischof E. AI in Longevity Medicine. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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54
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Bouska MJ, Bai H. Loxl2 is a mediator of cardiac aging in Drosophila melanogaster; genetically examining the role of aging clock genes. G3-GENES GENOMES GENETICS 2021; 12:6420708. [PMID: 34734976 PMCID: PMC8727986 DOI: 10.1093/g3journal/jkab381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 10/23/2021] [Indexed: 11/12/2022]
Abstract
Transcriptomic, proteomic, and methylation aging clocks demonstrate that aging has a predictable preset program, while Transcriptome Trajectory Turning Points indicate that the 20 to 40 age range in humans is the likely stage at which the progressive loss of homeostatic control, and in turn aging, begins to have detrimental effects. Turning points in this age range overlapping with human aging clock genes revealed five candidates that we hypothesized could play a role in aging or age-related physiological decline. To examine these gene's effects on lifespan and health-span, we utilized whole body and heart specific gene knockdown of human orthologs in Drosophila melanogaster. Whole body Loxl2, fz3, and Glo1 RNAi positively affected lifespan as did heart-specific Loxl2 knockdown. Loxl2 inhibition concurrently reduced age-related cardiac arrythmia and collagen (Pericardin) fiber width. Loxl2 binds several transcription factors in humans and RT-qPCR confirmed that a conserved transcriptional target CDH1 (Drosophila CadN2), has expression levels which correlate with Loxl2 reduction in Drosophila. These results point to conserved pathways and multiple mechanisms by which inhibition of Loxl2 can be beneficial to heart health and organismal aging.
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Affiliation(s)
- Mark J Bouska
- Department of Genetics, Development, & Cell Biology, Iowa State University, Ames, IA 50011, USA
| | - Hua Bai
- Department of Genetics, Development, & Cell Biology, Iowa State University, Ames, IA 50011, USA
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55
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Selvaraj C, Chandra I, Singh SK. Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries. Mol Divers 2021; 26:1893-1913. [PMID: 34686947 PMCID: PMC8536481 DOI: 10.1007/s11030-021-10326-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 09/24/2021] [Indexed: 12/27/2022]
Abstract
The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.
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Affiliation(s)
- Chandrabose Selvaraj
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
| | - Ishwar Chandra
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India
| | - Sanjeev Kumar Singh
- CADD and Molecular Modelling Lab, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India.
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56
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Aghamiri SS, Amin R, Helikar T. Recent applications of quantitative systems pharmacology and machine learning models across diseases. J Pharmacokinet Pharmacodyn 2021; 49:19-37. [PMID: 34671863 PMCID: PMC8528185 DOI: 10.1007/s10928-021-09790-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/07/2021] [Indexed: 12/29/2022]
Abstract
Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development.
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Affiliation(s)
- Sara Sadat Aghamiri
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Rada Amin
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
| | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
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57
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Secci R, Hartmann A, Walter M, Grabe HJ, Van der Auwera-Palitschka S, Kowald A, Palmer D, Rimbach G, Fuellen G, Barrantes I. Biomarkers of geroprotection and cardiovascular health: An overview of omics studies and established clinical biomarkers in the context of diet. Crit Rev Food Sci Nutr 2021; 63:2426-2446. [PMID: 34648415 DOI: 10.1080/10408398.2021.1975638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The slowdown, inhibition, or reversal of age-related decline (as a composite of disease, dysfunction, and, ultimately, death) by diet or natural compounds can be defined as dietary geroprotection. While there is no single reliable biomarker to judge the effects of dietary geroprotection, biomarker signatures based on omics (epigenetics, gene expression, microbiome composition) are promising candidates. Recently, omic biomarkers started to supplement established clinical ones such as lipid profiles and inflammatory cytokines. In this review, we focus on human data. We first summarize the current take on genetic biomarkers based on epidemiological studies. However, most of the remaining biomarkers that we describe, whether omics-based or clinical, are related to intervention studies. Then, because of their promising potential in the context of dietary geroprotection, we focus on the effects of berry-based interventions, which up to now have been mostly described employing clinical markers. We provide an aggregation and tabulation of all the recent systematic reviews and meta-analyses that we could find related to this topic. Finally, we present evidence for the importance of the "nutribiography," that is, the influence that an individual's history of diet and natural compound consumption can have on the effects of dietary geroprotection.
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Affiliation(s)
- Riccardo Secci
- Junior Research Group Translational Bioinformatics, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Alexander Hartmann
- Institute for Clinical Chemistry and Laboratory Medicine, University Medical Center Rostock, University of Rostock, Rostock, Germany
| | - Michael Walter
- Institute for Clinical Chemistry and Laboratory Medicine, University Medical Center Rostock, University of Rostock, Rostock, Germany.,Institute of Laboratory Medicine, Clinical Chemistry, and Pathobiochemistry, Charite University Medical Center, Berlin, Germany
| | - Hans Jörgen Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.,German Centre for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Sandra Van der Auwera-Palitschka
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.,German Centre for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Greifswald, Germany
| | - Axel Kowald
- Institute for Biostatistics and Informatics in Medicine and Aging Research, Rostock University Medical Center, Rostock, Germany
| | - Daniel Palmer
- Institute for Biostatistics and Informatics in Medicine and Aging Research, Rostock University Medical Center, Rostock, Germany
| | - Gerald Rimbach
- Institute of Human Nutrition and Food Science, University of Kiel, Kiel, Germany
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Aging Research, Rostock University Medical Center, Rostock, Germany
| | - Israel Barrantes
- Junior Research Group Translational Bioinformatics, Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
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58
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Tumor Nonimmune-Microenvironment-Related Gene Expression Signature Predicts Brain Metastasis in Lung Adenocarcinoma Patients after Surgery: A Machine Learning Approach Using Gene Expression Profiling. Cancers (Basel) 2021; 13:cancers13174468. [PMID: 34503278 PMCID: PMC8430997 DOI: 10.3390/cancers13174468] [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: 07/01/2021] [Revised: 08/30/2021] [Accepted: 09/02/2021] [Indexed: 12/26/2022] Open
Abstract
Simple Summary It is important to be able to predict brain metastasis in lung adenocarcinoma patients; however, research in this area is still lacking. Much of the previous work on tumor microenvironments in lung adenocarcinoma with brain metastasis concerns the tumor immune microenvironment. The importance of the tumor nonimmune microenvironment (extracellular matrix (ECM), epithelial–mesenchymal transition (EMT) feature, and angiogenesis) has been overlooked with regard to brain metastasis. We evaluated tumor nonimmune-microenvironment-related gene expression signatures that could predict brain metastasis after the surgical resection of lung adenocarcinoma using a machine learning approach. We identified a tumor nonimmune-microenvironment-related 17-gene expression signature, and this signature showed high brain metastasis predictive power in four machine learning classifiers. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. Our tumor nonimmune-microenvironment-related gene expression signatures are important biological markers that can predict brain metastasis and provide patient-specific treatment options. Abstract Using a machine learning approach with a gene expression profile, we discovered a tumor nonimmune-microenvironment-related gene expression signature, including extracellular matrix (ECM) remodeling, epithelial–mesenchymal transition (EMT), and angiogenesis, that could predict brain metastasis (BM) after the surgical resection of 64 lung adenocarcinomas (LUAD). Gene expression profiling identified a tumor nonimmune-microenvironment-related 17-gene expression signature that significantly correlated with BM. Of the 17 genes, 11 were ECM-remodeling-related genes. The 17-gene expression signature showed high BM predictive power in four machine learning classifiers (areas under the receiver operating characteristic curve = 0.845 for naïve Bayes, 0.849 for support vector machine, 0.858 for random forest, and 0.839 for neural network). Subgroup analysis revealed that the BM predictive power of the 17-gene signature was higher in the early-stage LUAD than in the late-stage LUAD. Pathway enrichment analysis showed that the upregulated differentially expressed genes were mainly enriched in the ECM–receptor interaction pathway. The immunohistochemical expression of the top three genes of the 17-gene expression signature yielded similar results to NanoString tests. The tumor nonimmune-microenvironment-related gene expression signatures found in this study are important biological markers that can predict BM and provide patient-specific treatment options.
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59
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Abstract
This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.
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Affiliation(s)
- Suresh Dara
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Swetha Dhamercherla
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Surender Singh Jadav
- Centre for Molecular Cancer Research (CMCR) and Vishnu Institute of Pharmaceutical Education and Research (VIPER), Narsapur, Medak, 502313 Telangana India
| | - CH Madhu Babu
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Mohamed Jawed Ahsan
- Department of Pharmaceutical Chemistry, Maharishi Arvind College of Pharmacy, Jaipur, 302023 Rajasthan India
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60
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Galkin F, Mamoshina P, Kochetov K, Sidorenko D, Zhavoronkov A. DeepMAge: A Methylation Aging Clock Developed with Deep Learning. Aging Dis 2021; 12:1252-1262. [PMID: 34341706 PMCID: PMC8279523 DOI: 10.14336/ad.2020.1202] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 12/02/2020] [Indexed: 12/26/2022] Open
Abstract
DNA methylation aging clocks have become an invaluable tool in biogerontology research since their inception in 2013. Today, a variety of machine learning approaches have been tested for the purpose of predicting human age based on molecular-level features. Among these, deep learning, or neural networks, is an especially promising approach that has been used to construct accurate clocks using blood biochemistry, transcriptomics, and microbiomics data-feats unachieved by other algorithms. In this article, we explore how deep learning performs in a DNA methylation setting and compare it to the current industry standard-the 353 CpG clock published in 2013. The aging clock we are presenting (DeepMAge) is a neural network regressor trained on 4,930 blood DNA methylation profiles from 17 studies. Its absolute median error was 2.77 years in an independent verification set of 1,293 samples from 15 studies. DeepMAge shows biological relevance by assigning a higher predicted age to people with various health-related conditions, such as ovarian cancer, irritable bowel diseases, and multiple sclerosis.
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Affiliation(s)
- Fedor Galkin
- Deep Longevity Limited, Hong Kong.
- Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK.
| | | | | | - Denis Sidorenko
- Insilico Medicine Hong Kong Limited, Hong Kong Science and Technology Park, Hong Kong.
| | - Alex Zhavoronkov
- Deep Longevity Limited, Hong Kong.
- Insilico Medicine Hong Kong Limited, Hong Kong Science and Technology Park, Hong Kong.
- Buck Institute for Research on Aging, Novato, CA, USA.
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61
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Xia X, Wang Y, Yu Z, Chen J, Han JDJ. Assessing the rate of aging to monitor aging itself. Ageing Res Rev 2021; 69:101350. [PMID: 33940202 DOI: 10.1016/j.arr.2021.101350] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 04/05/2021] [Accepted: 04/26/2021] [Indexed: 12/22/2022]
Abstract
Healthy aging is the prime goal of aging research and interventions. Healthy aging or not can be quantified by biological aging rates estimated by aging clocks. Generation and accumulation of large scale high-dimensional biological data together with maturation of artificial intelligence among other machine learning techniques, have enabled and spurred the rapid development of various aging rate estimators (aging clocks). Here we review the data sources and compare the algorithms of recent human aging clocks, and the applications of these clocks in both researches and daily life. We envision that not only more and multiscale data on cross-sectional data will add momentum to the aging clock development, new longitudinal and interventional data will further raise the aging clock development to the next level to be trained by true biological age such as morbidity and mortality age.
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62
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Baum ZJ, Yu X, Ayala PY, Zhao Y, Watkins SP, Zhou Q. Artificial Intelligence in Chemistry: Current Trends and Future Directions. J Chem Inf Model 2021; 61:3197-3212. [PMID: 34264069 DOI: 10.1021/acs.jcim.1c00619] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.
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Affiliation(s)
- Zachary J Baum
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Xiang Yu
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Philippe Y Ayala
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Yanan Zhao
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Steven P Watkins
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
| | - Qiongqiong Zhou
- Chemical Abstracts Service, 2540 Olentangy River Road, Columbus, Ohio 43210, United States
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63
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Reel PS, Reel S, Pearson E, Trucco E, Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol Adv 2021; 49:107739. [PMID: 33794304 DOI: 10.1016/j.biotechadv.2021.107739] [Citation(s) in RCA: 243] [Impact Index Per Article: 81.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/01/2021] [Accepted: 03/25/2021] [Indexed: 02/06/2023]
Abstract
With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.
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Affiliation(s)
- Parminder S Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Smarti Reel
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Ewan Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Emanuele Trucco
- VAMPIRE project, Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Emily Jefferson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.
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64
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Searching for female reproductive aging and longevity biomarkers. Aging (Albany NY) 2021; 13:16873-16894. [PMID: 34156973 PMCID: PMC8266318 DOI: 10.18632/aging.203206] [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: 11/09/2020] [Accepted: 05/31/2021] [Indexed: 12/21/2022]
Abstract
Female reproductive aging is, in a way, a biological phenomenon that develops along canonical molecular pathways; however, it has particular features. Recent studies revealed complexity of the interconnections between reproductive aging and aging of other systems, and even suggested a cause-effect uncertainty between them. It was also shown that reproductive aging can impact aging processes in an organism at the level of cells, tissues, organs, and systems. Women at the end of their reproductive lives are characterized by the accelerated incidence of age-related diseases. Timing of the onset of menarche and menopause and variability in the duration of reproductive life carry a latent social risk: not having enough information about the reproductive potential, women keep on postponing childbirth. Identification and use of the most accurate and sensitive aging biomarkers enable the prediction of menopause timing and quantification of the true biological and reproductive ages of an organism. We discuss current views on reproductive aging and peculiarities of using available biomarkers of aging. We also consider latest advances in the search for potential genetic markers of reproductive aging. Finally, we posit the importance of determining the female biological age and highlight potential research directions in this area.
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65
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Holzscheck N, Falckenhayn C, Söhle J, Kristof B, Siegner R, Werner A, Schössow J, Jürgens C, Völzke H, Wenck H, Winnefeld M, Grönniger E, Kaderali L. Modeling transcriptomic age using knowledge-primed artificial neural networks. NPJ Aging Mech Dis 2021; 7:15. [PMID: 34075044 PMCID: PMC8169742 DOI: 10.1038/s41514-021-00068-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 04/26/2021] [Indexed: 02/04/2023] Open
Abstract
The development of 'age clocks', machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current clocks offer little insight into the molecular biological processes driving aging, and their inner workings often remain non-transparent. Here we propose a new type of age clock, one that couples predictivity with interpretability of the underlying biology, achieved through the incorporation of prior knowledge into the model design. The clock, an artificial neural network constructed according to well-described biological pathways, allows the prediction of age from gene expression data of skin tissue with high accuracy, while at the same time capturing and revealing aging states of the pathways driving the prediction. The model recapitulates known associations of aging gene knockdowns in simulation experiments and demonstrates its utility in deciphering the main pathways by which accelerated aging conditions such as Hutchinson-Gilford progeria syndrome, as well as pro-longevity interventions like caloric restriction, exert their effects.
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Affiliation(s)
- Nicholas Holzscheck
- grid.432589.10000 0001 2201 4639Front End Innovation, Beiersdorf AG, Hamburg, Germany ,grid.5603.0Institute for Bioinformatics, University Medicine Greifswald, Greifswald, Germany
| | - Cassandra Falckenhayn
- grid.432589.10000 0001 2201 4639Front End Innovation, Beiersdorf AG, Hamburg, Germany
| | - Jörn Söhle
- grid.432589.10000 0001 2201 4639Front End Innovation, Beiersdorf AG, Hamburg, Germany
| | - Boris Kristof
- grid.432589.10000 0001 2201 4639Front End Innovation, Beiersdorf AG, Hamburg, Germany
| | - Ralf Siegner
- grid.432589.10000 0001 2201 4639Front End Innovation, Beiersdorf AG, Hamburg, Germany
| | - André Werner
- grid.5603.0Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Janka Schössow
- grid.5603.0Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Clemens Jürgens
- grid.5603.0Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- grid.5603.0Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Horst Wenck
- grid.432589.10000 0001 2201 4639Front End Innovation, Beiersdorf AG, Hamburg, Germany
| | - Marc Winnefeld
- grid.432589.10000 0001 2201 4639Front End Innovation, Beiersdorf AG, Hamburg, Germany
| | - Elke Grönniger
- grid.432589.10000 0001 2201 4639Front End Innovation, Beiersdorf AG, Hamburg, Germany
| | - Lars Kaderali
- grid.5603.0Institute for Bioinformatics, University Medicine Greifswald, Greifswald, Germany
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66
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Hartmann A, Hartmann C, Secci R, Hermann A, Fuellen G, Walter M. Ranking Biomarkers of Aging by Citation Profiling and Effort Scoring. Front Genet 2021; 12:686320. [PMID: 34093670 PMCID: PMC8176216 DOI: 10.3389/fgene.2021.686320] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 04/23/2021] [Indexed: 01/10/2023] Open
Abstract
Aging affects most living organisms and includes the processes that reduce health and survival. The chronological and the biological age of individuals can differ remarkably, and there is a lack of reliable biomarkers to monitor the consequences of aging. In this review we give an overview of commonly mentioned and frequently used potential aging-related biomarkers. We were interested in biomarkers of aging in general and in biomarkers related to cellular senescence in particular. To answer the question whether a biological feature is relevant as a potential biomarker of aging or senescence in the scientific community we used the PICO strategy known from evidence-based medicine. We introduced two scoring systems, aimed at reflecting biomarker relevance and measurement effort, which can be used to support study designs in both clinical and research settings.
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Affiliation(s)
- Alexander Hartmann
- Institute of Clinical Chemistry and Laboratory Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christiane Hartmann
- Translational Neurodegeneration Section “Albrecht-Kossel”, Department of Neurology, Rostock University Medical Center, Rostock, Germany
- German Center for Neurodegenerative Diseases (DZNE) Rostock/Greifswald, Rostock, Germany
| | - Riccardo Secci
- Institute for Biostatistics and Informatics in Medicine and Aging Research, Rostock University Medical Center, Rostock, Germany
| | - Andreas Hermann
- Translational Neurodegeneration Section “Albrecht-Kossel”, Department of Neurology, Rostock University Medical Center, Rostock, Germany
- German Center for Neurodegenerative Diseases (DZNE) Rostock/Greifswald, Rostock, Germany
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Aging Research, Rostock University Medical Center, Rostock, Germany
| | - Michael Walter
- Institute of Clinical Chemistry and Laboratory Medicine, Rostock University Medical Center, Rostock, Germany
- Institute of Laboratory Medicine, Clinical Chemistry and Pathobiochemistry, Charité –Berlin Institute of Health, Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
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67
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Abstract
Age-associated changes in gene expression in skeletal muscle of healthy individuals reflect accumulation of damage and compensatory adaptations to preserve tissue integrity. To characterize these changes, RNA was extracted and sequenced from muscle biopsies collected from 53 healthy individuals (22-83 years old) of the GESTALT study of the National Institute on Aging-NIH. Expression levels of 57,205 protein-coding and non-coding RNAs were studied as a function of aging by linear and negative binomial regression models. From both models, 1134 RNAs changed significantly with age. The most differentially abundant mRNAs encoded proteins implicated in several age-related processes, including cellular senescence, insulin signaling, and myogenesis. Specific mRNA isoforms that changed significantly with age in skeletal muscle were enriched for proteins involved in oxidative phosphorylation and adipogenesis. Our study establishes a detailed framework of the global transcriptome and mRNA isoforms that govern muscle damage and homeostasis with age.
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68
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Zhang X, Jonassen I, Goksøyr A. Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data. Bioinformatics 2021. [DOI: 10.36255/exonpublications.bioinformatics.2021.ch4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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69
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Meyer DH, Schumacher B. BiT age: A transcriptome-based aging clock near the theoretical limit of accuracy. Aging Cell 2021; 20:e13320. [PMID: 33656257 PMCID: PMC7963339 DOI: 10.1111/acel.13320] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 12/22/2020] [Accepted: 01/12/2021] [Indexed: 12/11/2022] Open
Abstract
Aging clocks dissociate biological from chronological age. The estimation of biological age is important for identifying gerontogenes and assessing environmental, nutritional, or therapeutic impacts on the aging process. Recently, methylation markers were shown to allow estimation of biological age based on age‐dependent somatic epigenetic alterations. However, DNA methylation is absent in some species such as Caenorhabditis elegans and it remains unclear whether and how the epigenetic clocks affect gene expression. Aging clocks based on transcriptomes have suffered from considerable variation in the data and relatively low accuracy. Here, we devised an approach that uses temporal scaling and binarization of C. elegans transcriptomes to define a gene set that predicts biological age with an accuracy that is close to the theoretical limit. Our model accurately predicts the longevity effects of diverse strains, treatments, and conditions. The involved genes support a role of specific transcription factors as well as innate immunity and neuronal signaling in the regulation of the aging process. We show that this binarized transcriptomic aging (BiT age) clock can also be applied to human age prediction with high accuracy. The BiT age clock could therefore find wide application in genetic, nutritional, environmental, and therapeutic interventions in the aging process.
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Affiliation(s)
- David H. Meyer
- Institute for Genome Stability in Ageing and Disease Medical Faculty University of Cologne Cologne Germany
- Cologne Excellence Cluster for Cellular Stress Responses in Ageing‐Associated Diseases (CECAD) Center for Molecular Medicine Cologne (CMMC) University of Cologne Cologne Germany
| | - Björn Schumacher
- Institute for Genome Stability in Ageing and Disease Medical Faculty University of Cologne Cologne Germany
- Cologne Excellence Cluster for Cellular Stress Responses in Ageing‐Associated Diseases (CECAD) Center for Molecular Medicine Cologne (CMMC) University of Cologne Cologne Germany
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70
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Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Phys Med 2021; 83:194-205. [DOI: 10.1016/j.ejmp.2021.03.026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/07/2021] [Accepted: 03/16/2021] [Indexed: 02/08/2023] Open
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71
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Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. BIOTECHNOL BIOPROC E 2021; 25:895-930. [PMID: 33437151 PMCID: PMC7790479 DOI: 10.1007/s12257-020-0049-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/27/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023]
Abstract
As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.
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Affiliation(s)
- Hyunho Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Eunyoung Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Ingoo Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Bongsung Bae
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Minsu Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
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72
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Börsch A, Zavolan M. Transcription factor motif activity as a biomarker of muscle aging. AMERICAN JOURNAL OF AGING SCIENCE AND RESEARCH 2021; 2:19-23. [PMID: 35083472 PMCID: PMC7612261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
In prior work, we analyzed gene expression profiles of mouse, rat and human gastrocnemius muscles to identify conserved regulators of muscle aging processes. By further comparing data obtained from different muscles we found stronger conservation of aging-related factors at the level of molecular pathways than at the level of individual genes. Here we compared the predictive power of models based on gene expression levels and those based on transcription factor motif activities for an individual's age. Although somewhat less accurate than models based on gene expression, models based on motif activities achieve good prediction of muscle age, further providing insights into aging-related molecular pathways.
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73
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Shokhirev MN, Johnson AA. Modeling the human aging transcriptome across tissues, health status, and sex. Aging Cell 2021; 20:e13280. [PMID: 33336875 PMCID: PMC7811842 DOI: 10.1111/acel.13280] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/10/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022] Open
Abstract
Aging in humans is an incredibly complex biological process that leads to increased susceptibility to various diseases. Understanding which genes are associated with healthy aging can provide valuable insights into aging mechanisms and possible avenues for therapeutics to prolong healthy life. However, modeling this complex biological process requires an enormous collection of high‐quality data along with cutting‐edge computational methods. Here, we have compiled a large meta‐analysis of gene expression data from RNA‐Seq experiments available from the Sequence Read Archive. We began by reprocessing more than 6000 raw samples—including mapping, filtering, normalization, and batch correction—to generate 3060 high‐quality samples spanning a large age range and multiple different tissues. We then used standard differential expression analyses and machine learning approaches to model and predict aging across the dataset, achieving an R2 value of 0.96 and a root‐mean‐square error of 3.22 years. These models allow us to explore aging across health status, sex, and tissue and provide novel insights into possible aging processes. We also explore how preprocessing parameters affect predictions and highlight the reproducibility limits of these machine learning models. Finally, we develop an online tool for predicting the ages of human transcriptomic samples given raw gene expression counts. Together, this study provides valuable resources and insights into the transcriptomics of human aging.
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Affiliation(s)
- Maxim N. Shokhirev
- Razavi Newman Integrative Genomics and Bioinformatics Core Salk Institute for Biological Studies La Jolla CA USA
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74
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Radenkovic D, Zhavoronkov A, Bischof E. AI in Longevity Medicine. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_248-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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75
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Polykovskiy D, Zhebrak A, Sanchez-Lengeling B, Golovanov S, Tatanov O, Belyaev S, Kurbanov R, Artamonov A, Aladinskiy V, Veselov M, Kadurin A, Johansson S, Chen H, Nikolenko S, Aspuru-Guzik A, Zhavoronkov A. Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models. Front Pharmacol 2020; 11:565644. [PMID: 33390943 PMCID: PMC7775580 DOI: 10.3389/fphar.2020.565644] [Citation(s) in RCA: 197] [Impact Index Per Article: 49.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 10/26/2020] [Indexed: 01/06/2023] Open
Abstract
Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervized predictive models in the downstream tasks. While there are plenty of generative models, it is unclear how to compare and rank them. In this work, we introduce a benchmarking platform called Molecular Sets (MOSES) to standardize training and comparison of molecular generative models. MOSES provides training and testing datasets, and a set of metrics to evaluate the quality and diversity of generated structures. We have implemented and compared several molecular generation models and suggest to use our results as reference points for further advancements in generative chemistry research. The platform and source code are available at https://github.com/molecularsets/moses.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Mark Veselov
- Insilico Medicine Hong Kong Ltd., Pak Shek Kok, Hong Kong
| | - Artur Kadurin
- Insilico Medicine Hong Kong Ltd., Pak Shek Kok, Hong Kong
| | - Simon Johansson
- Molecular AI, DiscoverySciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Hongming Chen
- Molecular AI, DiscoverySciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Sergey Nikolenko
- Insilico Medicine Hong Kong Ltd., Pak Shek Kok, Hong Kong
- Neuromation OU, Tallinn, Estonia
- Computer Science Department, National Research University Higher School of Economics, St. Petersburg, Russia
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- CIFAR AI Chair, Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), Toronto, ON, Canada
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76
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PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence. Aging (Albany NY) 2020; 12:23548-23577. [PMID: 33303702 PMCID: PMC7762465 DOI: 10.18632/aging.202344] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 11/24/2020] [Indexed: 12/15/2022]
Abstract
Aging clocks that accurately predict human age based on various biodata types are among the most important recent advances in biogerontology. Since 2016 multiple deep learning solutions have been created to interpret facial photos, omics data, and clinical blood parameters in the context of aging. Some of them have been patented to be used in commercial settings. However, psychological changes occurring throughout the human lifespan have been overlooked in the field of “deep aging clocks”. In this paper, we present two deep learning predictors trained on social and behavioral data from Midlife in the United States (MIDUS) study: (a) PsychoAge, which predicts chronological age, and (b) SubjAge, which describes personal aging rate perception. Using 50 distinct features from the MIDUS dataset these models have achieved a mean absolute error of 6.7 years for chronological age and 7.3 years for subjective age. We also show that both PsychoAge and SubjAge are predictive of all-cause mortality risk, with SubjAge being a more significant risk factor. Both clocks contain actionable features that can be modified using social and behavioral interventions, which enables a variety of aging-related psychology experiment designs. The features used in these clocks are interpretable by human experts and may prove to be useful in shifting personal perception of aging towards a mindset that promotes productive and healthy behaviors.
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77
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Kamerzell TJ, Middaugh CR. Prediction Machines: Applied Machine Learning for Therapeutic Protein Design and Development. J Pharm Sci 2020; 110:665-681. [PMID: 33278409 DOI: 10.1016/j.xphs.2020.11.034] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/27/2020] [Accepted: 11/27/2020] [Indexed: 12/11/2022]
Abstract
The rapid growth in technological advances and quantity of scientific data over the past decade has led to several challenges including data storage and analysis. Accurate models of complex datasets were previously difficult to develop and interpret. However, improvements in machine learning algorithms have since enabled unparalleled classification and prediction capabilities. The application of machine learning can be seen throughout diverse industries due to their ease of use and interpretability. In this review, we describe popular machine learning algorithms and highlight their application in pharmaceutical protein development. Machine learning models have now been applied to better understand the nonlinear concentration dependent viscosity of protein solutions, predict protein oxidation and deamidation rates, classify sub-visible particles and compare the physical stability of proteins. We also applied several machine learning algorithms using previously published data and describe models with improved predictions and classification. The authors hope that this review can be used as a resource to others and encourage continued application of machine learning algorithms to problems in pharmaceutical protein development.
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Affiliation(s)
- Tim J Kamerzell
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, USA; Division of Internal Medicine, HCA MidWest Health, Overland Park, KS, USA.
| | - C Russell Middaugh
- Department of Pharmaceutical Chemistry, The University of Kansas, Lawrence, KS, USA
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78
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Kumar P, Sinha R, Shukla P. Artificial intelligence and synthetic biology approaches for human gut microbiome. Crit Rev Food Sci Nutr 2020; 62:2103-2121. [PMID: 33249867 DOI: 10.1080/10408398.2020.1850415] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The gut microbiome comprises a variety of microorganisms whose genes encode proteins to carry out crucial metabolic functions that are responsible for the majority of health-related issues in human beings. The advent of the technological revolution in artificial intelligence (AI) assisted synthetic biology (SB) approaches will play a vital role in the modulating the therapeutic and nutritive potential of probiotics. This can turn human gut as a reservoir of beneficial bacterial colonies having an immense role in immunity, digestion, brain function, and other health benefits. Hence, in the present review, we have discussed the role of several gene editing tools and approaches in synthetic biology that have equipped us with novel tools like Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR-Cas) systems to precisely engineer probiotics for diagnostic, therapeutic and nutritive value. A brief discussion over the AI techniques to understand the metagenomic data from the healthy and diseased gut microbiome is also presented. Further, the role of AI in potentially impacting the pace of developments in SB and its current challenges is also discussed. The review also describes the health benefits conferred by engineered microbes through the production of biochemicals, nutraceuticals, drugs or biotherapeutics molecules etc. Finally, the review concludes with the challenges and regulatory concerns in adopting synthetic biology engineered microbes for clinical applications. Thus, the review presents a synergistic approach of AI and SB toward human gut microbiome for better health which will provide interesting clues to researchers working in the area of rapidly evolving food and nutrition science.
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Affiliation(s)
- Prasoon Kumar
- Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, India.,Department of Medical Devices, National Institute of Pharmaceutical Education and Research, Ahmedabad, India
| | | | - Pratyoosh Shukla
- School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi, India.,Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak, India
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79
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Lehallier B, Shokhirev MN, Wyss‐Coray T, Johnson AA. Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging. Aging Cell 2020; 19:e13256. [PMID: 33031577 PMCID: PMC7681068 DOI: 10.1111/acel.13256] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/21/2020] [Accepted: 09/15/2020] [Indexed: 12/14/2022] Open
Abstract
We previously identified 529 proteins that had been reported by multiple different studies to change their expression level with age in human plasma. In the present study, we measured the q-value and age coefficient of these proteins in a plasma proteomic dataset derived from 4263 individuals. A bioinformatics enrichment analysis of proteins that significantly trend toward increased expression with age strongly implicated diverse inflammatory processes. A literature search revealed that at least 64 of these 529 proteins are capable of regulating life span in an animal model. Nine of these proteins (AKT2, GDF11, GDF15, GHR, NAMPT, PAPPA, PLAU, PTEN, and SHC1) significantly extend life span when manipulated in mice or fish. By performing machine-learning modeling in a plasma proteomic dataset derived from 3301 individuals, we discover an ultra-predictive aging clock comprised of 491 protein entries. The Pearson correlation for this clock was 0.98 in the learning set and 0.96 in the test set while the median absolute error was 1.84 years in the learning set and 2.44 years in the test set. Using this clock, we demonstrate that aerobic-exercised trained individuals have a younger predicted age than physically sedentary subjects. By testing clocks associated with 1565 different Reactome pathways, we also show that proteins associated with signal transduction or the immune system are especially capable of predicting human age. We additionally generate a multitude of age predictors that reflect different aspects of aging. For example, a clock comprised of proteins that regulate life span in animal models accurately predicts age.
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Affiliation(s)
- Benoit Lehallier
- Department of Neurology and Neurological SciencesStanford UniversityStanfordCaliforniaUSA
- Wu Tsai Neurosciences InstituteStanford UniversityStanfordCaliforniaUSA
- Paul F. Glenn Center for the Biology of AgingStanford UniversityStanfordCaliforniaUSA
| | - Maxim N. Shokhirev
- Razavi Newman Integrative Genomics and Bioinformatics CoreThe Salk Institute for Biological StudiesLa JollaCaliforniaUSA
| | - Tony Wyss‐Coray
- Department of Neurology and Neurological SciencesStanford UniversityStanfordCaliforniaUSA
- Wu Tsai Neurosciences InstituteStanford UniversityStanfordCaliforniaUSA
- Paul F. Glenn Center for the Biology of AgingStanford UniversityStanfordCaliforniaUSA
- Department of Veterans AffairsVA Palo Alto Health Care SystemPalo AltoCaliforniaUSA
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80
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Mitina M, Young S, Zhavoronkov A. Psychological aging, depression, and well-being. Aging (Albany NY) 2020; 12:18765-18777. [PMID: 32950973 PMCID: PMC7585090 DOI: 10.18632/aging.103880] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 07/25/2020] [Indexed: 01/24/2023]
Abstract
Aging is a multifactorial process, which affects the human body on every level and results in both biological and psychological changes. Multiple studies have demonstrated that a lower subjective age is associated with better mental and physical health, cognitive functions, well-being and satisfaction with life. In this work we propose a list of non-modifiable and modifiable factors that may possibly be influenced by subjective age and its changes across an individual's lifespan. These factors can be used for a future development of individual psychological aging clocks, which may be utilized as a sensitive measure for health status and overall life satisfaction. Furthermore, recent progress in artificial intelligence and biomarkers of biological aging have enabled scientists to discover and evaluate the efficacy of potential aging- and disease-modifying drugs and interventions. We propose that biomarkers of psychological age, which are just as important as those for biological age, may likewise be used for these purposes. Indeed, these two types of markers complement one another. We foresee the development of a broad range of parametric and deep psychological and biopsychological aging clocks, which may have implications for drug development and therapeutic interventions, and thus healthcare and other industries.
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Affiliation(s)
- Maria Mitina
- Deep Longevity, Inc., Three Exchange Square, The Landmark, Hong Kong, China
| | | | - Alex Zhavoronkov
- Deep Longevity, Inc., Three Exchange Square, The Landmark, Hong Kong, China,Insilico Medicine, Hong Kong Science and Technology Park (HKSTP), Hong Kong, China,The Buck Institute for Research on Aging, Novato, CA 94945, USA
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81
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Hendrickx JO, van Gastel J, Leysen H, Martin B, Maudsley S. High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases. Pharmacol Rev 2020; 72:191-217. [PMID: 31843941 DOI: 10.1124/pr.119.017921] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.
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Affiliation(s)
- Jhana O Hendrickx
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Jaana van Gastel
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Hanne Leysen
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Bronwen Martin
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
| | - Stuart Maudsley
- Receptor Biology Laboratory, Department of Biomedical Research (J.O.H., J.v.G., H.L., S.M.) and Faculty of Pharmacy, Biomedical and Veterinary Sciences (J.O.H., J.v.G., H.L., B.M., S.M.), University of Antwerp, Antwerp, Belgium
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82
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Novel insights into wound age estimation: combined with "up, no change, or down" system and cosine similarity in python environment. Int J Legal Med 2020; 134:2177-2186. [PMID: 32909067 DOI: 10.1007/s00414-020-02411-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 08/27/2020] [Indexed: 01/23/2023]
Abstract
Wound age estimation is a complex, multifactorial issue. It is considered to have great practical significance that combining multi-biomarkers and multi-methods for injury time estimation. We optimized our earlier "up, no change, or down" model by adding data on the expression levels of mRNAs encoding ABHD2, MAD2L2, and ARID5A, and we converted the relative quantitative expression levels of seven genes into a vector rather than a color model. We used Python to derive the cosine similarity (CS) between a test set and the vector matrix; the highest similarity most accurately reflected the injury time. For the optimized model, the internal and external verifications were approximately 0.71 and 0.66, respectively. The good double-blinded results indicated that the model was stable and reliable. In summary, we used a vector matrix and cosine similarities derived by Python to mine the levels of genes expressed in contused skeletal muscle. We are the first to combine several biomarkers and methods for wound age estimation.
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83
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Raschka S, Kaufman B. Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition. Methods 2020; 180:89-110. [PMID: 32645448 PMCID: PMC8457393 DOI: 10.1016/j.ymeth.2020.06.016] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 06/23/2020] [Accepted: 06/23/2020] [Indexed: 02/06/2023] Open
Abstract
In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery.
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Affiliation(s)
- Sebastian Raschka
- University of Wisconsin-Madison, Department of Statistics, United States.
| | - Benjamin Kaufman
- University of Wisconsin-Madison, Department of Biostatistics and Medical Informatics, United States
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84
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Kendiukhov I. AI-based investigation of molecular biomarkers of longevity. Biogerontology 2020; 21:731-744. [PMID: 32632778 DOI: 10.1007/s10522-020-09890-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Accepted: 06/30/2020] [Indexed: 01/01/2023]
Abstract
In this paper, I build deep neural networks of various structures and hyperparameters in order to predict human chronological age based on open-access biochemical indicators and their specifications from the NHANES database. In total, 1152 neural networks are trained and tested. The algorithms are trained and tested on incomplete data: missing values in data records are extrapolated by mean or median values for each parameter. I select the best neural networks in terms of validation accuracy (coefficient of determination and mean absolute error). It turns out that the most accurate results are delivered by multilayer networks (6 layers) with recurrent layers. Neural network types are selected by trial and error. The algorithms reached an accuracy of 78% in terms of coefficient of determination and 6.5 in terms of mean absolute error. I also list empirically determined features of neural networks that increase accuracy for the task of chronological age prediction. Obtained results can be considered as an approximation of human biological age. Parameters in training datasets are selected the most broadly: all potentially relevant parameters (926) from the NHANES database are used. Although the networks are trained on the incomplete data, they demonstrated the ability to make reasonable predictions (with R2 > 0.7) based on no more than 100 biochemical indicators. Hence, for practical reasons the full data on each of 926 indicators are not required, although the analysis of the impact of each indicator is useful for theoretical developments.
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Affiliation(s)
- Ihor Kendiukhov
- School of Business and Economics, Humboldt University of Berlin, Unter den Linden 6, 10099, Berlin, Germany. .,Faculty of Biology, Zaporizhzhia National University, Zhukovskogo st., 10, Zaporizhzhia, 69600, Ukraine.
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85
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Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities. Ageing Res Rev 2020; 60:101050. [PMID: 32272169 DOI: 10.1016/j.arr.2020.101050] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 02/06/2020] [Accepted: 03/22/2020] [Indexed: 02/08/2023]
Abstract
The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism's age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metabolite concentrations. More than a dozen aging clocks use molecular features to predict an organism's age, each of them utilizing different data types and training procedures. Here, we offer a detailed comparison of existing mouse and human aging clocks, discuss their technological limitations and the underlying machine learning algorithms. We also discuss promising future directions of research in biohorology - the science of measuring the passage of time in living systems. Overall, we expect deep learning, deep neural networks and generative approaches to be the next power tools in this timely and actively developing field.
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86
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Mueller AL, McNamara MS, Sinclair DA. Why does COVID-19 disproportionately affect older people? Aging (Albany NY) 2020; 12:9959-9981. [PMID: 32470948 PMCID: PMC7288963 DOI: 10.18632/aging.103344] [Citation(s) in RCA: 567] [Impact Index Per Article: 141.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022]
Abstract
The severity and outcome of coronavirus disease 2019 (COVID-19) largely depends on a patient's age. Adults over 65 years of age represent 80% of hospitalizations and have a 23-fold greater risk of death than those under 65. In the clinic, COVID-19 patients most commonly present with fever, cough and dyspnea, and from there the disease can progress to acute respiratory distress syndrome, lung consolidation, cytokine release syndrome, endotheliitis, coagulopathy, multiple organ failure and death. Comorbidities such as cardiovascular disease, diabetes and obesity increase the chances of fatal disease, but they alone do not explain why age is an independent risk factor. Here, we present the molecular differences between young, middle-aged and older people that may explain why COVID-19 is a mild illness in some but life-threatening in others. We also discuss several biological age clocks that could be used in conjunction with genetic tests to identify both the mechanisms of the disease and individuals most at risk. Finally, based on these mechanisms, we discuss treatments that could increase the survival of older people, not simply by inhibiting the virus, but by restoring patients' ability to clear the infection and effectively regulate immune responses.
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Affiliation(s)
- Amber L. Mueller
- Glenn Center for Biology of Aging Research, Blavatnik Institute, Harvard Medical School, Boston, MA 20115, USA
| | - Maeve S. McNamara
- Glenn Center for Biology of Aging Research, Blavatnik Institute, Harvard Medical School, Boston, MA 20115, USA
| | - David A. Sinclair
- Glenn Center for Biology of Aging Research, Blavatnik Institute, Harvard Medical School, Boston, MA 20115, USA
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87
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Galkin F, Mamoshina P, Aliper A, Putin E, Moskalev V, Gladyshev VN, Zhavoronkov A. Human Gut Microbiome Aging Clock Based on Taxonomic Profiling and Deep Learning. iScience 2020; 23:101199. [PMID: 32534441 PMCID: PMC7298543 DOI: 10.1016/j.isci.2020.101199] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 04/14/2020] [Accepted: 05/21/2020] [Indexed: 12/14/2022] Open
Abstract
The human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous metagenomic analyses of gut microflora revealed associations between specific microbes and host age. Nonetheless there was no reliable way to tell a host's age based on the gut community composition. Here we developed a method of predicting hosts' age based on microflora taxonomic profiles using a cross-study dataset and deep learning. Our best model has an architecture of a deep neural network that achieves the mean absolute error of 5.91 years when tested on external data. We further advance a procedure for inferring the role of particular microbes during human aging and defining them as potential aging biomarkers. The described intestinal clock represents a unique quantitative model of gut microflora aging and provides a starting point for building host aging and gut community succession into a single narrative. DNNs are the most appropriate model to predict host age from gut microflora profiles Our DNN models reach MAE of 5.9 years in independent verification Feature importance analysis gives a starting point for anti-aging intervention design
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Affiliation(s)
- Fedor Galkin
- Deep Longevity Inc, Hong Kong Science and Technology Park, Hong Kong; Integrative Genomics of Ageing Group, Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK
| | - Polina Mamoshina
- Deep Longevity Inc, Hong Kong Science and Technology Park, Hong Kong; Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong
| | - Alex Aliper
- Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong
| | - Evgeny Putin
- Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong
| | - Vladimir Moskalev
- Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, MA, USA
| | - Alex Zhavoronkov
- Deep Longevity Inc, Hong Kong Science and Technology Park, Hong Kong; Insilico Medicine Ltd, Hong Kong Science and Technology Park, Hong Kong; Buck Institute for Research on Aging, Novato, CA, USA; Biogerontology Research Foundation, London, UK.
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88
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Shayakhmetov R, Kuznetsov M, Zhebrak A, Kadurin A, Nikolenko S, Aliper A, Polykovskiy D. Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders. Front Pharmacol 2020; 11:269. [PMID: 32362822 PMCID: PMC7182000 DOI: 10.3389/fphar.2020.00269] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 02/25/2020] [Indexed: 01/18/2023] Open
Abstract
Gene expression profiles are useful for assessing the efficacy and side effects of drugs. In this paper, we propose a new generative model that infers drug molecules that could induce a desired change in gene expression. Our model-the Bidirectional Adversarial Autoencoder-explicitly separates cellular processes captured in gene expression changes into two feature sets: those related and unrelated to the drug incubation. The model uses related features to produce a drug hypothesis. We have validated our model on the LINCS L1000 dataset by generating molecular structures in the SMILES format for the desired transcriptional response. In the experiments, we have shown that the proposed model can generate novel molecular structures that could induce a given gene expression change or predict a gene expression difference after incubation of a given molecular structure. The code of the model is available at https://github.com/insilicomedicine/BiAAE.
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Affiliation(s)
| | | | | | | | - Sergey Nikolenko
- Insilico Medicine, Hong Kong, Hong Kong
- Neuromation OU, Tallinn, Estonia
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89
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Liu R, Gu Y, Shen M, Li H, Zhang K, Wang Q, Wei X, Zhang H, Wu D, Yu K, Cai W, Wang G, Zhang S, Sun Q, Huang P, Wang Z. Predicting postmortem interval based on microbial community sequences and machine learning algorithms. Environ Microbiol 2020; 22:2273-2291. [PMID: 32227435 DOI: 10.1111/1462-2920.15000] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 03/18/2020] [Accepted: 03/22/2020] [Indexed: 11/29/2022]
Abstract
Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis, Anaerosalibacter bizertensis, Lactobacillus reuteri, and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 ± 0.8 h within 24-h decomposition and 14.5 ± 4.4 h within 15-day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.
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Affiliation(s)
- Ruina Liu
- College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Yuexi Gu
- School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Mingwang Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi, 710061, China
| | - Huan Li
- Department of Microbiology and immunology, School of Basic Medical Sciences, Xi'an Jiaotong University, Xi'an, China
| | - Kai Zhang
- College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Qi Wang
- Chongqing Medical University, College of Basic Medicine, Department of Forensic Medicine, Chongqing, 400016, China
| | - Xin Wei
- College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Haohui Zhang
- College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Di Wu
- College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Kai Yu
- College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Wumin Cai
- College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Gongji Wang
- College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Siruo Zhang
- Department of Microbiology and immunology, School of Basic Medical Sciences, Xi'an Jiaotong University, Xi'an, China
| | - Qinru Sun
- College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China
| | - Ping Huang
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China
| | - Zhenyuan Wang
- College of Forensic Medicine, Xi'an Jiaotong University, Xi'an, 710061, China
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90
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Zhavoronkov A. Geroprotective and senoremediative strategies to reduce the comorbidity, infection rates, severity, and lethality in gerophilic and gerolavic infections. Aging (Albany NY) 2020; 12:6492-6510. [PMID: 32229705 PMCID: PMC7202545 DOI: 10.18632/aging.102988] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 03/24/2020] [Indexed: 01/08/2023]
Abstract
The recently identified SARS-CoV-2 betacoronavirus responsible for the COVID-19 pandemic has uncovered the age-associated vulnerability in the burden of disease and put aging research in the spotlight. The limited data available indicates that COVID-19 should be referred to as a gerolavic (from Greek, géros "old man" and epilavís, "harmful") infection because the infection rates, severity, and lethality are substantially higher in the population aged 60 and older. This is primarily due to comorbidity but may be partially due to immunosenescence, decreased immune function in the elderly, and general loss of function, fitness, and increased frailty associated with aging. Immunosenescence is a major factor affecting vaccination response, as well as the severity and lethality of infectious diseases. While vaccination reduces infection rates, and therapeutic interventions reduce the severity and lethality of infections, these interventions have limitations. Previous studies showed that postulated geroprotectors, such as sirolimus (rapamycin) and its close derivative rapalog everolimus (RAD001), decreased infection rates in a small sample of elderly patients. This article presents a review of the limited literature available on geroprotective and senoremediative interventions that may be investigated to decrease the disease burden of gerolavic infections. This article also highlights a need for rigorous clinical validation of deep aging clocks as surrogate markers of biological age. These could be used to assess the need for, and efficacy of, geroprotective and senoremediative interventions and provide better protection for elderly populations from gerolavic infections. This article does not represent medical advice and the medications described are not yet licensed or recommended as immune system boosters, as they have not undergone clinical evaluation for this purpose.
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Affiliation(s)
- Alex Zhavoronkov
- Insilico Medicine, Hong Kong Science and Technology Park (HKSTP), Tai Po, Hong Kong
- The Biogerontology Research Foundation, London, UK
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91
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Kudryashova KS, Burka K, Kulaga AY, Vorobyeva NS, Kennedy BK. Aging Biomarkers: From Functional Tests to Multi‐Omics Approaches. Proteomics 2020; 20:e1900408. [DOI: 10.1002/pmic.201900408] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 02/07/2020] [Indexed: 12/15/2022]
Affiliation(s)
| | - Ksenia Burka
- Centaura AG Bleicherweg 10 Zurich 8002 Switzerland
| | - Anton Y. Kulaga
- Centaura AG Bleicherweg 10 Zurich 8002 Switzerland
- Systems Biology of Aging GroupInstitute of Biochemistry of the Romanian Academy Splaiul Independentei 296 Bucharest 060031 Romania
| | | | - Brian K. Kennedy
- Departments of Biochemistry and Physiology Yong Loo Lin School of MedicineNational University of Singapore 8 Medical Drive, MD7, 117596 Singapore
- Singapore Institute for Clinical Sciences (SICS)Agency for Science and Technology (A*STAR)Brenner Centre for Molecular Medicine 30 Medical Drive Singapore 117609 Singapore
- Buck Institute for Research on Aging 8001 Redwood Blvd. Novato CA 94945‐1400 USA
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92
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Aman Y, Frank J, Lautrup SH, Matysek A, Niu Z, Yang G, Shi L, Bergersen LH, Storm-Mathisen J, Rasmussen LJ, Bohr VA, Nilsen H, Fang EF. The NAD +-mitophagy axis in healthy longevity and in artificial intelligence-based clinical applications. Mech Ageing Dev 2020; 185:111194. [PMID: 31812486 PMCID: PMC7545219 DOI: 10.1016/j.mad.2019.111194] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 11/24/2019] [Accepted: 12/03/2019] [Indexed: 12/11/2022]
Abstract
Nicotinamide adenine dinucleotide (NAD+) is an important natural molecule involved in fundamental biological processes, including the TCA cycle, OXPHOS, β-oxidation, and is a co-factor for proteins promoting healthy longevity. NAD+ depletion is associated with the hallmarks of ageing and may contribute to a wide range of age-related diseases including metabolic disorders, cancer, and neurodegenerative diseases. One of the central pathways by which NAD+ promotes healthy ageing is through regulation of mitochondrial homeostasis via mitochondrial biogenesis and the clearance of damaged mitochondria via mitophagy. Here, we highlight the contribution of the NAD+-mitophagy axis to ageing and age-related diseases, and evaluate how boosting NAD+ levels may emerge as a promising therapeutic strategy to counter ageing as well as neurodegenerative diseases including Alzheimer's disease. The potential use of artificial intelligence to understand the roles and molecular mechanisms of the NAD+-mitophagy axis in ageing is discussed, including possible applications in drug target identification and validation, compound screening and lead compound discovery, biomarker development, as well as efficacy and safety assessment. Advances in our understanding of the molecular and cellular roles of NAD+ in mitophagy will lead to novel approaches for facilitating healthy mitochondrial homoeostasis that may serve as a promising therapeutic strategy to counter ageing-associated pathologies and/or accelerated ageing.
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Affiliation(s)
- Yahyah Aman
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway
| | - Johannes Frank
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway
| | - Sofie Hindkjær Lautrup
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway
| | - Adrian Matysek
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway; School of Pharmacy and Division of Laboratory Medicine in Sosnowiec, Medical University of Silesia in Katowice, 40-055, Katowice, Poland
| | - Zhangming Niu
- Aladdin Healthcare Technologies Ltd., 24-26 Baltic Street West, London, EC1Y OUR, UK
| | - Guang Yang
- Cardiovascular Research Centre, Royal Brompton Hospital, London, SW3 6NP, UK; National Heart and Lung Institute, Imperial College London, London, SW7 2AZ, UK
| | - Liu Shi
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Linda H Bergersen
- The Brain and Muscle Energy Group, Electron Microscopy Laboratory, Department of Oral Biology, University of Oslo, NO-0316, Oslo, Norway; Amino Acid Transporters, Division of Anatomy, Department of Molecular Medicine, Institute of Basic Medical Sciences (IMB) and Healthy Brain Ageing Centre (SERTA), University of Oslo, NO-0317, Oslo, Norway; Center for Healthy Aging, Department of Neuroscience and Pharmacology, Faculty of Health Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway
| | - Jon Storm-Mathisen
- Amino Acid Transporters, Division of Anatomy, Department of Molecular Medicine, Institute of Basic Medical Sciences (IMB) and Healthy Brain Ageing Centre (SERTA), University of Oslo, NO-0317, Oslo, Norway; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway
| | - Lene J Rasmussen
- The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway; Center for Healthy Aging, Department of Cellular and Molecular Medicine, Faculty of Health Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark
| | - Vilhelm A Bohr
- Laboratory of Molecular Gerontology, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, United States; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway; Center for Healthy Aging, Department of Cellular and Molecular Medicine, Faculty of Health Sciences, University of Copenhagen, DK-2200, Copenhagen N, Denmark
| | - Hilde Nilsen
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway
| | - Evandro F Fang
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478, Lørenskog, Norway; The Norwegian Centre on Healthy Ageing (NO-Age), Oslo, Norway.
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93
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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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94
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Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 2019; 18:463-477. [PMID: 30976107 DOI: 10.1038/s41573-019-0024-5] [Citation(s) in RCA: 931] [Impact Index Per Article: 186.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.
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Affiliation(s)
- Jessica Vamathevan
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
| | - Dominic Clark
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | | | - Ian Dunham
- Open Targets and European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Edgardo Ferran
- European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - George Lee
- Bristol-Myers Squibb, Princeton, NJ, USA
| | - Bin Li
- Takeda Pharmaceuticals International Co., Cambridge, MA, USA
| | - Anant Madabhushi
- Case Western Reserve University, Cleveland, OH, USA.,Louis Stokes Cleveland Veterans Affair Medical Center, Cleveland, OH, USA
| | | | - Michaela Spitzer
- Open Targets and European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK
| | - Shanrong Zhao
- Pfizer Worldwide Research and Development, Cambridge, MA, USA
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95
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Koromina M, Pandi MT, Patrinos GP. Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2019; 23:539-548. [PMID: 31651216 DOI: 10.1089/omi.2019.0151] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as game-changing technologies, AI and machine learning algorithms have slowly but surely begun to revolutionize pharmaceutical industry and drug development over the past 5 years. In this expert review, we describe the most frequently used machine learning algorithms in drug development pipelines and the -omics databases well poised to support machine learning and drug discovery. Subsequently, we analyze the emerging new computational approaches to drug discovery and the in silico pipelines for drug repositioning and the synergies among -omics system sciences, AI and machine learning. As with system sciences, AI and machine learning embody a system scale and Big Data driven vision for drug discovery and development. We conclude with a future outlook on the ways in which machine learning approaches can be implemented to buttress and expedite drug discovery and precision medicine. As AI and machine learning are rapidly entering pharmaceutical industry and the art and science of drug development, we need to critically examine the attendant prospects and challenges to benefit patients and public health.
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Affiliation(s)
- Maria Koromina
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - Maria-Theodora Pandi
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece
| | - George P Patrinos
- Laboratory of Pharmacogenomics and Individualized Therapy, Department of Pharmacy, School of Health Sciences, University of Patras, Patras, Greece.,Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi.,Zayed Center of Health Sciences, United Arab Emirates University, Al-Ain, Abu Dhabi
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96
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Automatic Classification of Sarcopenia Level in Older Adults: A Case Study at Tijuana General Hospital. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16183275. [PMID: 31489909 PMCID: PMC6765933 DOI: 10.3390/ijerph16183275] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/29/2019] [Accepted: 08/31/2019] [Indexed: 01/10/2023]
Abstract
This paper presents a study based on data analysis of the sarcopenia level in older adults. Sarcopenia is a prevalent pathology in adults of around 50 years of age, whereby the muscle mass decreases by 1 to 2% a year, and muscle strength experiences an annual decrease of 1.5% between 50 and 60 years of age, subsequently increasing by 3% each year. The World Health Organisation estimates that 5–13% of individuals of between 60 and 70 years of age and 11–50% of persons of 80 years of age or over have sarcopenia. This study was conducted with 166 patients and 99 variables. Demographic data was compiled including age, gender, place of residence, schooling, marital status, level of education, income, profession, and financial support from the State of Baja California, and biochemical parameters such as glycemia, cholesterolemia, and triglyceridemia were determined. A total of 166 patients took part in the study, with an average age of 77.24 years. The purpose of the study was to provide an automatic classifier of sarcopenia level in older adults using artificial intelligence in addition to identifying the weight of each variable used in the study. We used machine learning techniques in this work, in which 10 classifiers were employed to assess the variables and determine which would provide the best results, namely, Nearest Neighbors (3), Linear SVM (Support Vector Machines) (C = 0.025), RBF (Radial Basis Function) SVM (gamma = 2, C = 1), Gaussian Process (RBF (1.0)), Decision Tree (max_depth = 3), Random Forest (max_depth=3, n_estimators = 10), MPL (Multilayer Perceptron) (alpha = 1), AdaBoost, Gaussian Naive Bayes, and QDA (Quadratic Discriminant Analysis). Feature selection determined by the mean for the variable ranking suggests that Age, Systolic Arterial Hypertension (HAS), Mini Nutritional Assessment (MNA), Number of chronic diseases (ECNumber), and Sodium are the five most important variables in determining the sarcopenia level, and are thus of great importance prior to establishing any treatment or preventive measure. Analysis of the relationships existing between the presence of the variables and classifiers used in moderate and severe sarcopenia revealed that the sarcopenia level using the RBF SVM classifier with Age, HAS, MNA, ECNumber, and Sodium variables has 82′5 accuracy, a 90′2 F1, and 82′8 precision.
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97
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Moore JH, Raghavachari N. Artificial Intelligence Based Approaches to Identify Molecular Determinants of Exceptional Health and Life Span-An Interdisciplinary Workshop at the National Institute on Aging. Front Artif Intell 2019; 2:12. [PMID: 33733101 PMCID: PMC7861312 DOI: 10.3389/frai.2019.00012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 07/08/2019] [Indexed: 01/01/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful approach for integrated analysis of the rapidly growing volume of multi-omics data, including many research and clinical tasks such as prediction of disease risk and identification of potential therapeutic targets. However, the potential for AI to facilitate the identification of factors contributing to human exceptional health and life span and their translation into novel interventions for enhancing health and life span has not yet been realized. As researchers on aging acquire large scale data both in human cohorts and model organisms, emerging opportunities exist for the application of AI approaches to untangle the complex physiologic process(es) that modulate health and life span. It is expected that efficient and novel data mining tools that could unravel molecular mechanisms and causal pathways associated with exceptional health and life span could accelerate the discovery of novel therapeutics for healthy aging. Keeping this in mind, the National Institute on Aging (NIA) convened an interdisciplinary workshop titled “Contributions of Artificial Intelligence to Research on Determinants and Modulation of Health Span and Life Span” in August 2018. The workshop involved experts in the fields of aging, comparative biology, cardiology, cancer, and computational science/AI who brainstormed ideas on how AI can be leveraged for the analyses of large-scale data sets from human epidemiological studies and animal/model organisms to close the current knowledge gaps in processes that drive exceptional life and health span. This report summarizes the discussions and recommendations from the workshop on future application of AI approaches to advance our understanding of human health and life span.
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Affiliation(s)
- Jason H Moore
- University of Pennsylvania, Philadelphia, PA, United States
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98
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Gensous N, Bacalini MG, Franceschi C, Meskers CGM, Maier AB, Garagnani P. Age-Related DNA Methylation Changes: Potential Impact on Skeletal Muscle Aging in Humans. Front Physiol 2019; 10:996. [PMID: 31427991 PMCID: PMC6688482 DOI: 10.3389/fphys.2019.00996] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 07/18/2019] [Indexed: 12/27/2022] Open
Abstract
Human aging is accompanied by a decline in muscle mass and muscle function, which is commonly referred to as sarcopenia. Sarcopenia is associated with detrimental clinical outcomes, such as a reduced quality of life, frailty, an increased risk of falls, fractures, hospitalization, and mortality. The exact underlying mechanisms of sarcopenia are poorly delineated and the molecular mechanisms driving the development and progression of this disorder remain to be uncovered. Previous studies have described age-related differences in gene expression, with one study identifying an age-specific expression signature of sarcopenia, but little is known about the influence of epigenetics, and specially of DNA methylation, in its pathogenesis. In this review, we will focus on the available knowledge in literature on the characterization of DNA methylation profiles during skeletal muscle aging and the possible impact of physical activity and nutrition. We will consider the possible use of the recently developed DNA methylation-based biomarkers of aging called epigenetic clocks in the assessment of physical performance in older individuals. Finally, we will discuss limitations and future directions of this field.
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Affiliation(s)
- Noémie Gensous
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
| | | | - Claudio Franceschi
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.,Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Carel G M Meskers
- Amsterdam UMC, Department of Rehabilitation Medicine, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Andrea B Maier
- Department of Human Movement Sciences, @AgeAmsterdam, Faculty of Behavioural and Movement Sciences, Amsterdam Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Department of Medicine and Aged Care, @AgeMelbourne, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, VIC, Australia
| | - Paolo Garagnani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy.,Clinical Chemistry, Department of Laboratory Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden.,Applied Biomedical Research Center (CRBA), Policlinico S.Orsola-Malpighi Polyclinic, Bologna, Italy.,CNR Institute of Molecular Genetics, Unit of Bologna, Bologna, Italy
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99
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Zhavoronkov A, Mamoshina P. Deep Aging Clocks: The Emergence of AI-Based Biomarkers of Aging and Longevity. Trends Pharmacol Sci 2019; 40:546-549. [DOI: 10.1016/j.tips.2019.05.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 05/19/2019] [Accepted: 05/20/2019] [Indexed: 12/18/2022]
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100
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Borisov N, Buzdin A. New Paradigm of Machine Learning (ML) in Personalized Oncology: Data Trimming for Squeezing More Biomarkers From Clinical Datasets. Front Oncol 2019; 9:658. [PMID: 31380288 PMCID: PMC6650540 DOI: 10.3389/fonc.2019.00658] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 07/05/2019] [Indexed: 11/13/2022] Open
Affiliation(s)
- Nicolas Borisov
- Department of Personalized Medicine, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Anton Buzdin
- Department of Personalized Medicine, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Department of Genomics and Postgenomic Technologies, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia.,Department of Bioinformatics and Molecular Networks, OmicsWay Corporation, Walnut, CA, United States
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