Former Research Team Leader<\/p>\n","protected":false},"featured_media":0,"template":"","people_teams":[24],"class_list":["post-542","people","type-people","status-publish","hentry","people_teams-alumni"],"yoast_head":"\n
Ahmed Abdeen Hamed - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/people\/ahmed-abdeen-hamed\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Ahmed Abdeen Hamed\" \/>\n<meta property=\"og:description\" content=\"Former Research Team Leader\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/ahmed-abdeen-hamed\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2024-08-18T19:42:54+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/ahmed_abdeen_hamed.png\" \/>\n\t<meta property=\"og:image:width\" content=\"350\" \/>\n\t<meta property=\"og:image:height\" content=\"350\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/people\\\/ahmed-abdeen-hamed\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/ahmed-abdeen-hamed\\\/\",\"name\":\"Ahmed Abdeen Hamed - 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Centre for Computational Personalized Medicine","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sano.science\/people\/ahmed-abdeen-hamed\/","og_locale":"en_US","og_type":"article","og_title":"Ahmed Abdeen Hamed","og_description":"Former Research Team Leader","og_url":"https:\/\/sano.science\/people\/ahmed-abdeen-hamed\/","og_site_name":"Centre for Computational Personalized Medicine","article_publisher":"https:\/\/www.facebook.com\/sano.science\/","article_modified_time":"2024-08-18T19:42:54+00:00","og_image":[{"width":350,"height":350,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/ahmed_abdeen_hamed.png","type":"image\/png"}],"twitter_card":"summary_large_image","twitter_site":"@sanoscience","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sano.science\/people\/ahmed-abdeen-hamed\/","url":"https:\/\/sano.science\/people\/ahmed-abdeen-hamed\/","name":"Ahmed Abdeen Hamed - 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His dissertation presented novel network models and algorithms that explored social media data, news articles, and biomedical literature. Particularly, his work investigated digital recruitment, adverse drug events, and rankings. While he was in the pharma industry, he designed a network algorithm that provided ranking to small molecules based on their specificity. In 2019, Dr. Hamed also served as an assistant Professor of data science and artificial intelligence at Norwich University. He has led the development of several academic programs (in data science, business analytics, and information systems) for both undergraduate and graduate levels. He also served as a program director for those academic programs.<\/p>\n","social_media":false,"tabs":[{"title":"Highlights of accomplishments","content":"<p>Dr. Hamed’s research continues to strive to solve real-world problems. He is currenlty focusing on advancing our knowledge to understand disease and treatment. His research on drug repurposing is currently focusing on advancing our understanding of COVID-19 treatment but constructing knowledge from the clinical trials and biomedical literature. Dr. Hamed joined Sano to continue to pursue his clinical research using computational means of data science and artificial intelligence. He will be collaborating with the other Sano teams and beyond while he will be supervising grdaute Ph.D. Students and train PostDoctoral fellows.<\/p>\n"},{"title":"Professional experience","content":"<p>With many years of experience, in both industry and academia, he has achieved the following:<\/p>\n<ul>\n<li>Actively published in highly specialized and well-ranked journals<\/li>\n<li>A first inventor for molecule ranking and drug discovery for the pharma industry<\/li>\n<li>Helped a startup company to be awarded a multi-million dollar grant for building a recommendation engine<\/li>\n<li>Was selected among The FastCompany MostCreative in 2016<\/li>\n<\/ul>\n"},{"title":"Publications","content":"<p><strong>Top-5 Publications:<\/strong><\/p>\n<ul>\n<li>Hamed, A.A.; Fandy, T.E.; Tkaczuk, K.L.; Verspoor, K.; Lee, B.S. COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments. Pharmaceutics 2022, 14, 567. https:\/\/doi.org\/10.3390\/pharmaceutics14030567<\/li>\n<li>Gates LE, Hamed AA; The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation<br \/>\nJ Med Internet Res 2020;22(8):e21169 doi: 10.2196\/21169<\/li>\n<li>Abdeen, M.A.R.; Hamed, A.A.; Wu, X. Fighting the COVID-19 Infodemic in News Articles and False Publications: The NeoNet Text Classifier, a Supervised Machine Learning Algorithm. Appl. Sci. 2021, 11, 7265. https:\/\/doi.org\/10.3390\/app11167265<\/li>\n<li>Hamed, A. A., Leszczynska, A., & Schreiber, M. (2019, March). MolecRank: a specificity-based network analysis algorithm. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 159-168). Springer, Cham.<\/li>\n<li>Hamed, A. A., Wu, X., Erickson, R., & Fandy, T. (2015). Twitter KH networks in action: Advancing biomedical literature for drug search. Journal of biomedical informatics, 56, 157-168.<\/li>\n<\/ul>\n<p><strong>Pharma Patent:<\/strong><\/p>\n<ul>\n<li>Hamed, A.A. and Leszczynska, A., Merck Sharp and Dohme Corp, 2021. Systems and methods for providing a specificity-based network analysis algorithm for searching and ranking therapeutic molecules. U.S. Patent 10,978,178.<\/li>\n<\/ul>\n"}],"email":"","position_with_team":{"text_before_link":"Former Research Team Leader","link_text":"Clinical Data Science","text_after_link":"","link":"https:\/\/sano.science\/research-teams\/clinical-data-science\/"},"publications":[{"ID":15035,"post_author":"5","post_date":"2024-01-18 10:23:08","post_date_gmt":"2024-01-18 09:23:08","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-rQfA5d\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Ahmed Abdeen Hamed; Jakub Jonczyk; Mohammad Zaiyan Alam; Ewa Deelman; Byung Suk Lee<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-ySoCXT\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-d34jLw\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature; (2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction; (3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene\/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-ySoCXT\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a8ede86c296\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/ieeexplore.ieee.org\/document\/10029987\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Mining Literature-Based Knowledge Graph for Predicting Combination Therapeutics: A COVID-19 Use Case","post_excerpt":"In: IEEE Xplore, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"mining-literature-based-knowledge-graph-for-predicting-combination-therapeutics-a-covid-19-use-case","to_ping":"","pinged":"","post_modified":"2024-01-18 10:23:08","post_modified_gmt":"2024-01-18 09:23:08","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15035","menu_order":35,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15049,"post_author":"5","post_date":"2024-01-18 10:38:44","post_date_gmt":"2024-01-18 09:38:44","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-RAzE0W\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Matthias Eisenmann,\u00a0Annika Reinke,\u00a0Vivienn Weru,\u00a0Minu Dietlinde Tizabi,\u00a0Fabian Isensee,\u00a0Tim J. Adler,\u00a0Sharib Ali,\u00a0Vincent Andrearczyk,\u00a0Marc Aubreville,\u00a0Ujjwal Baid,\u00a0Spyridon Bakas,\u00a0Niranjan Balu,\u00a0Sophia Bano,\u00a0Jorge Bernal,\u00a0Sebastian Bodenstedt,\u00a0Alessandro Casella,\u00a0Veronika Cheplygina,\u00a0Marie Daum,\u00a0Marleen de Bruijne,\u00a0Adrien Depeursinge,\u00a0Reuben Dorent,\u00a0Jan Egger,\u00a0David G. Ellis,\u00a0Sandy Engelhardt,\u00a0Melanie Ganz,\u00a0Noha Ghatwary,\u00a0Gabriel Girard,\u00a0Patrick Godau,\u00a0Anubha Gupta,\u00a0Lasse Hansen,\u00a0Kanako Harada,\u00a0Mattias Heinrich,\u00a0Nicholas Heller,\u00a0Alessa Hering,\u00a0Arnaud Huaulm\u00e9,\u00a0Pierre Jannin,\u00a0Ali Emre Kavur,\u00a0Old\u0159ich Kodym,\u00a0Michal Kozubek,\u00a0Jianning Li,\u00a0Hongwei Li,\u00a0Jun Ma,\u00a0Carlos Mart\u00edn-Isla,\u00a0Bjoern Menze,\u00a0Alison Noble,\u00a0Valentin Oreiller,\u00a0Nicolas Padoy,\u00a0Sarthak Pati,\u00a0Kelly Payette,\u00a0Tim R\u00e4dsch,\u00a0Jonathan Rafael-Pati\u00f1o,\u00a0Vivek Singh Bawa,\u00a0Stefanie Speidel,\u00a0Carole H. Sudre,\u00a0Kimberlin van Wijnen,\u00a0Martin Wagner,\u00a0Donglai Wei,\u00a0Amine Yamlahi,\u00a0Moi Hoon Yap,\u00a0Chun Yuan,\u00a0Maximilian Zenk,\u00a0Aneeq Zia,\u00a0David Zimmerer,\u00a0Dogu Baran Aydogan,\u00a0Binod Bhattarai,\u00a0Louise Bloch,\u00a0Raphael Br\u00fcngel,\u00a0Jihoon Cho,\u00a0Chanyeol Choi,\u00a0Qi Dou,\u00a0Ivan Ezhov,\u00a0Christoph M. Friedrich,\u00a0Clifton Fuller,\u00a0Rebati Raman Gaire,\u00a0Adrian Galdran,\u00a0\u00c1lvaro Garc\u00eda Faura,\u00a0Maria Grammatikopoulou,\u00a0SeulGi Hong,\u00a0Mostafa Jahanifar,\u00a0Ikbeom Jang,\u00a0Abdolrahim Kadkhodamohammadi,\u00a0Inha Kang,\u00a0Florian Kofler,\u00a0Satoshi Kondo,\u00a0Hugo Kuijf,\u00a0Mingxing Li,\u00a0Minh Huan Luu,\u00a0Toma\u017e Martin\u010di\u010d,\u00a0Pedro Morais,\u00a0Mohamed A. Naser,\u00a0Bruno Oliveira,\u00a0David Owen,\u00a0Subeen Pang,\u00a0Jinah Park,\u00a0Sung-Hong Park,\u00a0Szymon P\u0142otka,\u00a0Elodie Puybareau,\u00a0Nasir Rajpoot,\u00a0Kanghyun Ryu,\u00a0Numan Saeed\u00a0et al. (25 additional authors not shown)<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-koslDw\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-SRn8tU\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common char- acteristics of winning solutions. These typically include the use of multi-task learning (63%) and\/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The \u201ctypical\u201d lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyz- ing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain prob- lem. The insights of our study could help researchers (1) improve algorithm development strategies when approach- ing new problems, and (2) focus on open research questions revealed by this work.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-koslDw\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a8f193fa877\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/openaccess.thecvf.com\/content\/CVPR2023\/papers\/Eisenmann_Why_Is_the_Winner_the_Best_CVPR_2023_paper.pdf\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Why is the winner the best?","post_excerpt":"In: 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"why-is-the-winner-the-best","to_ping":"","pinged":"","post_modified":"2024-01-18 10:38:44","post_modified_gmt":"2024-01-18 09:38:44","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15049","menu_order":31,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15083,"post_author":"5","post_date":"2024-01-18 20:49:59","post_date_gmt":"2024-01-18 19:49:59","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-gt9bVE\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Ahmed Abdeen Hamed, Malgorzata Zachara-Szymanska, Xindong Wu<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-ISauic\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-LHiJju\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">As the influence of Transformer-based approaches in general and generative AI in particular continues to expand across various domains, concerns regarding authenticity and explainability are on the rise. Here, we share our perspective on the necessity of implementing effective detection, verification, and explainability mechanisms to counteract the potential harms arising from the proliferation of AI-generated inauthentic content and science. We recognize the transformative potential of generative AI, exemplified by ChatGPT, in the scientific landscape. However, we also emphasize the urgency of addressing associated challenges, particularly in light of the risks posed by dis-information, misinformation, and unreproducible science. This perspective serves as a response to the call for concerted efforts to safeguard the authen-ticity of information in the age of AI. By prioritizing detection, fact- checking, and explainability policies, we aim to foster a climate of trust, uphold ethical standards, and harness the full potential of AI for the betterment of science and society.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-ISauic\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a9809b9b16b\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.cell.com\/iscience\/pdf\/S2589-0042(24)00003-8.pdf?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS2589004224000038%3Fshowall%3Dtrue\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Safeguarding Authenticity for Mitigating the Harms of Generative AI: Issues, Research Agenda, and Policies for Detection, Fact-Checking, and Ethical AI","post_excerpt":"In: Cell Press iScience, 2024.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"safeguarding-authenticity-for-mitigating-the-harms-of-generative-ai-issues-research-agenda-and-policies-for-detection-fact-checking-and-ethical-ai","to_ping":"","pinged":"","post_modified":"2024-01-18 20:49:59","post_modified_gmt":"2024-01-18 19:49:59","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15083","menu_order":1,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12697,"post_author":"5","post_date":"2023-07-13 13:51:07","post_date_gmt":"2023-07-13 11:51:07","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-t8PrKt\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Hamed, Ahmed Abdeen; Fandy, Tamer E.; Tkaczuk, Karolina L.; Lee, Byung Suk<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-QsVBmI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-bvpyWp\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Background: With the Coronavirus becoming a new reality of our world, global efforts continue to seek answers to many questions regarding the spread, variants, vaccinations, and medications. Particularly, with the emergence of several strains (e.g., Delta, Omicron), vaccines will need further development to offer complete protection against the new variants. It is critical to identify antiviral treatments while the development of vaccines continues. In this regard, the repurposing of already FDA-approved drugs remains a major effort. In this paper, we investigate the hypothesis that a combination of FDA-approved drugs may be considered as a candidate for COVID-19 treatment if (1) there exists an evidence in the COVID-19 biomedical literature that suggests such a combination, and (2) there is match in the clinical trials space that validates this drug combination. Methods: We present a computational framework that is designed for detecting drug combinations, using the following components (a) a Text-mining module: to extract drug names from the abstract section of the biomedical publications and the intervention\/treatment sections of clinical trial records. (b) a network model constructed from the drug names and their associations, (c) a clique similarity algorithm to identify candidate drug treatments. Result and Conclusions: Our framework has identified treatments in the form of two, three, or four drug combinations (e.g., hydroxychloroquine, doxycycline, and azithromycin). The identifications of the various treatment candidates provided sufficient evidence that supports the trustworthiness of our hypothesis.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-3o8Ev6\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64afe50b33d5a\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.mdpi.com\/1999-4923\/14\/3\/567\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"COVID-19 Drug Repurposing: a Network-based Framework for Exploring BioMedical Literature and Clinical Trials for Possible Treatments\u00a0","post_excerpt":"In: Pharmaceutics, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"covid-19-drug-repurposing-a-network-based-framework-for-exploring-biomedical-literature-and-clinical-trials-for-possible-treatments","to_ping":"","pinged":"","post_modified":"2024-01-05 14:55:40","post_modified_gmt":"2024-01-05 13:55:40","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12697","menu_order":72,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/542","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/people"}],"version-history":[{"count":11,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/542\/revisions"}],"predecessor-version":[{"id":17212,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/542\/revisions\/17212"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=542"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=542"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}