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Balsano C, Burra P, Duvoux C, Alisi A, Piscaglia F, Gerussi A. Artificial Intelligence and liver: Opportunities and barriers. Dig Liver Dis 2023; 55:1455-1461. [PMID: 37718227 DOI: 10.1016/j.dld.2023.08.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/14/2023] [Accepted: 08/17/2023] [Indexed: 09/19/2023]
Abstract
Artificial Intelligence (AI) has recently been shown as an excellent tool for the study of the liver; however, many obstacles still have to be overcome for the digitalization of real-world hepatology. The authors present an overview of the current state of the art on the use of innovative technologies in different areas (big data, translational hepatology, imaging, and transplant setting). In clinical practice, physicians must integrate a vast array of data modalities (medical history, clinical data, laboratory tests, imaging, and pathology slides) to achieve a diagnostic or therapeutic decision. Unfortunately, machine learning and deep learning are still far from really supporting clinicians in real life. In fact, the accuracy of any technological support has no value in medicine without the support of clinicians. To make better use of new technologies, it is essential to improve clinicians' knowledge about them. To this end, the authors propose that collaborative networks for multidisciplinary approaches will improve the rapid implementation of AI systems for developing disease-customized AI-powered clinical decision support tools. The authors also discuss ethical, educational, and legal challenges that must be overcome to build robust bridges and deploy potentially effective AI in real-world clinical settings.
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Affiliation(s)
- Clara Balsano
- Department of Life, Health and Environmental Sciences-MESVA, School of Emergency-Urgency Medicine, University of L'Aquila, Piazzale Salvatore Tommasi 1, Coppito, L'Aquila 67100, Italy.
| | - Patrizia Burra
- Multivisceral Transplant Unit Gastroenterology Department of Surgery, Oncology and Gastroenterology, Padua University Hospital, Padua, Italy
| | - Christophe Duvoux
- Department of Hepatology, Medical Liver Transplant Unit, Hospital Henri Mondor AP-HP, University of Paris-Est Créteil (UPEC), France
| | - Anna Alisi
- Research Unit of Molecular Genetics of Complex Phenotypes, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Fabio Piscaglia
- Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy
| | - Alessio Gerussi
- Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; European Reference Network on Hepatological Diseases (ERN RARE-LIVER), Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
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Wu Y, Yang L, Wu X, Wang L, Qi H, Feng Q, Peng B, Ding Y, Tang J. Identification of the hub genes in polycystic ovary syndrome based on disease-associated molecule network. FASEB J 2023; 37:e23056. [PMID: 37342921 DOI: 10.1096/fj.202202103r] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/20/2023] [Accepted: 06/12/2023] [Indexed: 06/23/2023]
Abstract
Revealing the key genes involved in polycystic ovary syndrome (PCOS) and elucidating its pathogenic mechanism is of extreme importance for the development of targeted clinical therapy for PCOS. Investigating disease by integrating several associated and interacting molecules in biological systems will make it possible to discover new pathogenic genes. In this study, an integrative disease-associated molecule network, combining protein-protein interactions and protein-metabolites interactions (PPMI) network was constructed based on the PCOS-associated genes and metabolites systematically collected. This new PPMI strategy identified several potential PCOS-associated genes, which have unreported in previous publications. Moreover, the systematic analysis of five benchmarks data sets indicated the DERL1 was identified as downregulated in PCOS granulosa cell and has good classification performance between PCOS patients and healthy controls. CCR2 and DVL3 were upregulated in PCOS adipose tissues and have good classification performance. The expression of novel gene FXR2 identified in this study is significantly increased in ovarian granulosa cells of PCOS patients compared with controls via quantitative analysis. Our study uncovers substantial differences in the PCOS-specific tissue and provides a plethora of information on dysregulated genes and metabolites that are linked to PCOS. This knowledgebase could have the potential to benefit the scientific and clinical community. In sum, the identification of novel gene associated with PCOS provides valuable insights into the underlying molecular mechanisms of PCOS and could potentially lead to the development of new diagnostic and therapeutic strategies.
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Affiliation(s)
- Yue Wu
- School of Basic Medicine, Chongqing Medical University, Chongqing, P.R. China
- Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, P.R. China
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Lingping Yang
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing, P.R. China
| | - Xianglu Wu
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing, P.R. China
| | - Lidan Wang
- School of Basic Medicine, Chongqing Medical University, Chongqing, P.R. China
| | - Hongbo Qi
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, P.R. China
| | - Qian Feng
- Department of Gynecology, Chongqing Hospital of Traditional Chinese Medicine, Chongqing, P.R. China
| | - Bin Peng
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing, P.R. China
| | - Yubin Ding
- Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Chongqing, P.R. China
- Department of Pharmacology, Academician Workstation, Changsha Medical University, Changsha, P.R. China
| | - Jing Tang
- School of Basic Medicine, Chongqing Medical University, Chongqing, P.R. China
- Joint International Research Laboratory of Reproduction and Development of the Ministry of Education of China, School of Public Health, Chongqing Medical University, Chongqing, P.R. China
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Guo X, Han J, Song Y, Yin Z, Liu S, Shang X. Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype–phenotype interactions. Front Genet 2022; 13:921775. [PMID: 36046233 PMCID: PMC9421127 DOI: 10.3389/fgene.2022.921775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Motivation: A central goal of current biology is to establish a complete functional link between the genotype and phenotype, known as the so-called genotype–phenotype map. With the continuous development of high-throughput technology and the decline in sequencing costs, multi-omics analysis has become more widely employed. While this gives us new opportunities to uncover the correlation mechanisms between single-nucleotide polymorphism (SNP), genes, and phenotypes, multi-omics still faces certain challenges, specifically: 1) When the sample size is large enough, the number of omics types is often not large enough to meet the requirements of multi-omics analysis; 2) each omics’ internal correlations are often unclear, such as the correlation between genes in genomics; 3) when analyzing a large number of traits (p), the sample size (n) is often smaller than p, n << p, hindering the application of machine learning methods in the classification of disease outcomes.Results: To solve these issues with multi-omics and build a robust classification model, we propose a graph-embedded deep neural network (G-EDNN) based on expression quantitative trait loci (eQTL) data, which achieves sparse connectivity between network layers to prevent overfitting. The correlation within each omics is also considered such that the model more closely resembles biological reality. To verify the capabilities of this method, we conducted experimental analysis using the GSE28127 and GSE95496 data sets from the Gene Expression Omnibus (GEO) database, tested various neural network architectures, and used prior data for feature selection and graph embedding. Results show that the proposed method could achieve a high classification accuracy and easy-to-interpret feature selection. This method represents an extended application of genotype–phenotype association analysis in deep learning networks.
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Affiliation(s)
- Xinpeng Guo
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China
- School of Air and Missile Defense, Air Force Engineering University, Xi’an, China
| | - Jinyu Han
- School of Economics and Management, Chang ‘an University, Xi’an, China
| | - Yafei Song
- School of Air and Missile Defense, Air Force Engineering University, Xi’an, China
| | - Zhilei Yin
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China
| | - Shuaichen Liu
- School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, China
| | - Xuequn Shang
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China
- *Correspondence: Xuequn Shang,
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