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Clinical Data Science

Clinical Data Science Team will aim to investigate highly significant medical problems (such as the ongoing COVID-19 and its impact on our world, globally and the patients coping with Cancer Chemotherapy treatment). Using means of Algorithm, Machine Learning, and Artificial Intelligence, the Team will focus on addressing such problem from a data science perspective. This means that our work will always start with data and will be indulged in the analysis of data. Such analyses will aim to provide clinical insights in which drug to use or how various patients respond and cope with chemo. Clearly, this will not only help save lives, but also provide a better quality of lives after receiving treatment. This Team will act as the eyes and ears for clinical matter it investigates. It should strive to also inform healthcare providers and pharmaceutical companies on whether they need to consider revisiting the drug’s mechanism of action for example. What is proposed here is in line with what Sano’s mission for computational medicine.  

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Dr. Ahmed Abdeen Hamed, Ph.D.

Leader of the Sano Science Research Team for Clinical Data Science

Dr. Ahmed Abdeen Hamed, Ph.D., completed his Ph.D. at the University of Vermont in 2014. 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 antelligence 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. During the pandemic, Dr. Hamed was one of the very early scientists who investigated the possibility of providing a COVID-19 treatment. He published a paper that recommended 30-drugs which to date is considered the foundation for many of the publications that followed after.

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.

With many years of experience, in both industry and academia, he has achieved the following:

  • Actively published in highly specialized and well-ranked journals
  • A first inventor for molecule ranking and drug discovery for the pharma industry
  • Helped a startup company to be awarded a multi-million dollar grant for building a recommendation engine
  • Was selected among The FastCompany MostCreative in 2016

Top-5 Publications:

  • 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.
  • Gates LE, Hamed AA; The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation
    J Med Internet Res 2020;22(8):e21169 doi: 10.2196/21169
  • 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.
  • 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.
  • 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.

Pharma Patent:

  • 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.

Team Members

Alicja Gawalska

Medicinal Chemistry Specialist

Ala graduated from Jagiellonian University Medical College in 2018 with a Master of Science in Pharmacy, and is currently completing her PhD studies in pharmaceutical sciences. She also finished the postgraduate studies in data science. Her doctoral project concerns computer-aided drug design in the field of multi-target ligands with potential application in the treatment of asthma and COPD. She is interested in design of new compounds using molecular modelling approach, the application of machine learning and artificial intelligence in drug design, as well as the analysis of medical and pharmaceutical data. A lover of German language and German culture.

Adam Sulek

PostDoc in Clinical Data Science

Ph.D. student in the Department of Chemistry at Jagiellonian University. His Ph.D. defense is planned for December 2022. His interdisciplinary research was performed in collaboration with the Chemistry Department of the University of Coimbra (Portugal), the Malopolska Biotechnology Center, and the Faculty of Mathematics and Computer Science of the Jagiellonian University. His research interests include computational chemistry and biology, computer-aided drug design, molecular docking and Graph Neural Network.

Jakub Jończyk

PostDoc in Clinical Data Science

Jakub got a PhD at the Faculty of Pharmacy, Jagiellonian University Medical College in Kraków. His doctoral research concerns the application of molecular modeling in drug design, specifically in designing multitarget ligands for future Alzheimer’s treatment. After that Jakub spent one year as a assistant professor with the Medicinal Chemistry Department at Jagiellonian University teaching and expanding his research with machine learning techniques. At Sano, he joined the Clinical Data Science Team for his PostDoc training. He aims to build his computational knowledge to advance his career as a medical chemistry. He is excited is collaborate with a vibrant interdiscplanary team of computer scientists, biologists, pharmacologist to provide a better response to the needs of patients and medical professionals around the world.

Filip Katulski

Master Student in Clinical Data Science

Filip is a student of Computer Science at the Faculty of Computer Science, Electronics and Telecommunications at AGH University of Science and Technology. At the same university he obtained two engineering diplomas: Power Engineering and Electronics. His engineering thesis in Electronics focused on the development of a MOX-type gas sensor control system for the Biomarker Analysis Laboratory. His Master's thesis is being carried out in Prof Hamed's team in collaboration with Prof Malawski. The topic is the design of a federated text search engine for Biomedical text analysis. Filip interned as a Technical Student at CERN in the Data Aquisition team, responsible for the Computational Infrastructure of the ATLAS project, one of the four components of the Large Hadron Collider. As part of his career, he is interested in topics such as Cloud Computing, Large Scale Computing and Natural Language Processing. In his free time, he enjoys travelling, snowboarding, going to the opera, theatre, and cinema.

Samaneh Salehi Nasab

PhD Student in Clinical Data Science

Samaneh graduated from Universiti Teknologi Malaysia in 2016 with a PhD in Computer Science. Since then, she has worked in both academia and industry. During her PhD, her main focus was on successful systems implementation. She worked as a product owner in various industries such as logestic and algo trading and as a Python programmer in data processing and visualization. She taught undergraduate students courses such as Agile Development and Systems Analysis. She moved to Sano to pursue a second PhD in biomedical semantic data integration. She is currently working on an ontology-based text mining project. In her free time, she enjoys traveling, watching movies, listening to podcasts, and reading.

Current projects

The project aims to explore the clinical trials for FDA-approved drugs that could show promise as an antiviral for the COVID-19 disease. We plan on continuing the drug repurposing for COVID-19. Future phases would investigate how COVID-19 antiviral drugs may be suited for pre-existing condition patients (Cancer, Alzheimer’s, Depression, and Asthma). 

The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials. The expected methods of the project are centred around Natural Language Processing, Algorithms, and Machine Learning. 

Contemporary social media in platforms such as Twitter and Facebook thrive on the experiences that people are sharing in their posts. It is important to capture the essence of these experiences by studying the word selection in the tweets. Understanding crowd expressions has occupied the literature space since social media has emerged. The availability of various platforms and fresh data based on peoples’ experiences has indeed excited researchers and has given them the much-needed tools to work with. Because people interact with each other while they share experiences and feelings, social media has become an apparent source of exploration and discoveries. This is because such communications and expressions form interesting networks which they different forms and topologies. Networks in general are attractive models to study in terms of their contents and but also structure. 

  • The modelling and simulation of spread of COVID-19 on communities such as workplaces, and University campuses and schools. 
  • The investigation of clinical and health misinformation on the web. 
  • The investigation of G-protein Coupled Receptors (GPCR) and how they produce health analytics for Cancer and Alzheimer’s. 
  • Pre-existing Conditions Preventing Vaccination. 

Scientific Directions:

There is no doubt that the Drug Repurposing for COVID-19 will help with our immediate needs. Even with the availability of vaccines the world is unclear about whether they will be able to stand against the ever-evolving Coronavirus. At the time of writing this document, the world is racing to learn about the Omicron variant. It is inventible to have an antiviral treatment for the disease to be able to save the lives of the unfortunate people who get infected. However, we are also confident that such atreatment will be found in and various options may become available. Despite the uncertainly, Merck’s Oral Antiviral Molnupiravir Reduced the Risk of Hospitalization or Death by Approximately 50 Percent. This is great news to our world, and it also shows the significance of the science of Drug Repurposing.

Drug Repurposing makes significant discoveries, we will be investigating Drug Repurposing for cancer and Alzheimer’s. Using means of Data Science, the team will strive to explore the current FDA-approved drugs. The team should strive to create a publicly accessible knowledgebase that can be shared with the scientific and industrial community. Perhaps it can be also used as a source for monetizing on analytics that will be requested by industry and research labs. 


Hamed, Ahmed Abdeen; Fandy, Tamer E.; Tkaczuk, Karolina L.; Lee, Byung Suk

COVID-19 Drug Repurposing: a Network-based Framework for Exploring BioMedical Literature and Clinical Trials for Possible Treatments Journal Article

In: Pharmaceutics , 2022.

Abstract | BibTeX | Links: