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Getahun KA, Angaw DA, Asres MS, Kahaliw W, Petros Z, Abay SM, Yimer G, Berhane N. The Role of Pharmacogenomics Studies for Precision Medicine Among Ethiopian Patients and Their Clinical Implications: A Scoping Review. Pharmgenomics Pers Med 2024; 17:347-361. [PMID: 38974617 PMCID: PMC11226858 DOI: 10.2147/pgpm.s454328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 06/12/2024] [Indexed: 07/09/2024] Open
Abstract
Background Pharmacogenomics research is currently revolutionizing treatment optimization by discovering molecular markers. Medicines are the cornerstone of treatment for both acute and chronic diseases. Pharmacogenomics associated treatment response varies from 20% to 95%, resulting in from lack of efficacy to serious toxicity. Pharmacogenomics has emerged as a useful tool for therapy optimization and plays a bigger role in clinical care going forward. However, in Africa, in particular in Ethiopia, such studies are scanty and not generalizing. Therefore, the objective of this review was to outline such studies, generating comprehensive evidence and identify studied variants' association with treatment responses in Ethiopian patients. Methods The Joanna Briggs Institute's updated 2020 methodological guidelines for conducting and guidance for scoping reviews were used. We meticulously adhered to the systemic review reporting items checklist and scoping review meta-analyses extension. Results Two hundred twenty-nine possibly relevant studies were searched. These include: 64, 54, 21, 48 and 42 from PubMed, Scopus, Google Scholar, EMBASE, and manual search, respectively. Seventy-seven duplicate studies were removed. Thirty-nine papers were rejected with justification, whereas 58 studies were qualified for full-text screening. Finally 19 studies were examined. The primary pharmacogene that was found to have a significant influence on the pharmacokinetics of efavirenz was CYP2B6. Drug-induced liver injury has frequently identified toxicity among studied medications. Conclusion and Future Perspectives Pharmacogenomics studies in Ethiopian populations are less abundant. The studies conducted focused on infectious diseases, specifically on HAART commonly efavirenz and backbone first-line anti-tuberculosis drugs. There is a high need for further pharmacogenomics research to verify the discrepancies among the studies and for guiding precision medicine. Systematic review and meta-analysis are also recommended for pooled effects of different parameters in pharmacogenomics studies.
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Affiliation(s)
- Kefyalew Ayalew Getahun
- Department of Pharmacology, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Dessie Abebaw Angaw
- Department of Biostatistics and Epidemiology, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Mezgebu Silamsaw Asres
- Department of Internal Medicine, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Wubayehu Kahaliw
- Department of Pharmacology, School of Pharmacy, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | - Zelalem Petros
- Department of Pharmacology and Clinical Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Solomon Mequanente Abay
- Department of Pharmacology and Clinical Pharmacy, School of Pharmacy, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
| | - Getnet Yimer
- Department of Genetics and Center for Global Genomics and Health Equity, School of Medicine, University of Pennsylvania, Pennsylvania, US, USA
| | - Nega Berhane
- Department of Medical Biotechnology, Institute of Biotechnology, University of Gondar, Gondar, Ethiopia
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Bang D, Gu J, Park J, Jeong D, Koo B, Yi J, Shin J, Jung I, Kim S, Lee S. A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective. Int J Mol Sci 2022; 23:ijms231911498. [PMID: 36232792 PMCID: PMC9570358 DOI: 10.3390/ijms231911498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/18/2022] [Accepted: 09/26/2022] [Indexed: 02/01/2023] Open
Abstract
Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.
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Affiliation(s)
- Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
| | - Joonhyeong Park
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Korea
| | - Dabin Jeong
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Bonil Koo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Jungseob Yi
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
| | - Jihye Shin
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Inuk Jung
- Department of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Korea
- MOGAM Institute for Biomedical Research, Yongin-si 16924, Korea
| | - Sunho Lee
- AIGENDRUG Co., Ltd., Seoul 08826, Korea
- Correspondence:
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Mo H, Breitling R, Francavilla C, Schwartz JM. Data integration and mechanistic modelling for breast cancer biology: Current state and future directions. CURRENT OPINION IN ENDOCRINE AND METABOLIC RESEARCH 2022; 24:None. [PMID: 36034741 PMCID: PMC9402443 DOI: 10.1016/j.coemr.2022.100350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Breast cancer is one of the most common cancers threatening women worldwide. A limited number of available treatment options, frequent recurrence, and drug resistance exacerbate the prognosis of breast cancer patients. Thus, there is an urgent need for methods to investigate novel treatment options, while taking into account the vast molecular heterogeneity of breast cancer. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics and metabolomics data, enable approaching breast cancer biology at multiple levels of omics interaction networks. Systems biology approaches, including computational inference of ‘big data’ and mechanistic modelling of specific pathways, are emerging to identify potential novel combinations of breast cancer subtype signatures and more diverse targeted therapies.
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Stahlschmidt SR, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review. Brief Bioinform 2022; 23:bbab569. [PMID: 35089332 PMCID: PMC8921642 DOI: 10.1093/bib/bbab569] [Citation(s) in RCA: 76] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/06/2021] [Accepted: 12/11/2021] [Indexed: 02/06/2023] Open
Abstract
Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships. Therefore, we review the current state-of-the-art of such methods and propose a detailed taxonomy that facilitates more informed choices of fusion strategies for biomedical applications, as well as research on novel methods. By doing so, we find that deep fusion strategies often outperform unimodal and shallow approaches. Additionally, the proposed subcategories of fusion strategies show different advantages and drawbacks. The review of current methods has shown that, especially for intermediate fusion strategies, joint representation learning is the preferred approach as it effectively models the complex interactions of different levels of biological organization. Finally, we note that gradual fusion, based on prior biological knowledge or on search strategies, is a promising future research path. Similarly, utilizing transfer learning might overcome sample size limitations of multimodal data sets. As these data sets become increasingly available, multimodal DL approaches present the opportunity to train holistic models that can learn the complex regulatory dynamics behind health and disease.
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Affiliation(s)
| | | | - Jane Synnergren
- Systems Biology Research Center, University of Skövde, Sweden
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Chen X, Cheng G, Wang FL, Tao X, Xie H, Xu L. Machine and cognitive intelligence for human health: systematic review. Brain Inform 2022; 9:5. [PMID: 35150379 PMCID: PMC8840949 DOI: 10.1186/s40708-022-00153-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 01/25/2022] [Indexed: 12/27/2022] Open
Abstract
Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.
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Affiliation(s)
- Xieling Chen
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China
| | - Gary Cheng
- Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong SAR, China.
| | - Fu Lee Wang
- School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China
| | - Xiaohui Tao
- School of Sciences, University of Southern Queensland, Toowoomba, Australia
| | - Haoran Xie
- Department of Computing and Decision Sciences, Lingnan University, Hong Kong SAR, China
| | - Lingling Xu
- School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR, China
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Piyawajanusorn C, Nguyen LC, Ghislat G, Ballester PJ. A gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling. Brief Bioinform 2021; 22:6343527. [PMID: 34368843 DOI: 10.1093/bib/bbab312] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 06/25/2021] [Accepted: 07/20/2021] [Indexed: 12/16/2022] Open
Abstract
A central goal of precision oncology is to administer an optimal drug treatment to each cancer patient. A common preclinical approach to tackle this problem has been to characterize the tumors of patients at the molecular and drug response levels, and employ the resulting datasets for predictive in silico modeling (mostly using machine learning). Understanding how and why the different variants of these datasets are generated is an important component of this process. This review focuses on providing such introduction aimed at scientists with little previous exposure to this research area.
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Affiliation(s)
- Chayanit Piyawajanusorn
- Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France.,Institut Paoli-Calmettes, F-13009 Marseille, France.,Aix-Marseille Université, F-13284 Marseille, France.,CNRS UMR7258, F-13009 Marseille, France.,Faculty of Medicine and Public Health, HRH Princess Chulabhorn College of Medical Science, Chulabhorn Royal Academy, Bangkok, Thailand
| | - Linh C Nguyen
- Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France.,Institut Paoli-Calmettes, F-13009 Marseille, France.,Aix-Marseille Université, F-13284 Marseille, France.,CNRS UMR7258, F-13009 Marseille, France.,Department of Life Sciences, University of Science and Technology of Hanoi, Vietnam Academy of Science and Technology, Hanoi, Vietnam
| | - Ghita Ghislat
- U1104, CNRS UMR7280, Centre d'Immunologie de Marseille-Luminy, Inserm, Marseille, France
| | - Pedro J Ballester
- Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France.,Institut Paoli-Calmettes, F-13009 Marseille, France.,Aix-Marseille Université, F-13284 Marseille, France.,CNRS UMR7258, F-13009 Marseille, France
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