GRAPHLET TECHNOLOGIES
IN BIOMEDICINE
Extracting new precision medicine knowledge by eXplainable AI (XAI) from multi-omic data augmented by network topology
OUR TECHNOLOGY
Our deep-tech, state-of-the-art XAI methods for fusion of multi-omic data offer the potential to leverage the entire molecular landscape of patients, thus becoming an invaluable tool for multiple tasks of precision medicine. The state-of-the-art algorithmic solution and efficient computational implementation that we achieved, is coupled with our rare, multi-disciplinary know-how applying them correctly to molecular multi-omics and patient data, that extend beyond computer science and that few possess.
MARKET GOALS
We aim to cover the following market segments within precision medicine and drug discovery:
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Subtyping (stratification) of patients of one disease into sub-groups that should be treated differently.
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Uncovering new biomarkers.
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Uncovering new drug targets (proteins to which drugs bind).
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Drug redirection: detecting drug collisions and synergies and proposing rescue / replacements of drugs, possibly in combination with other drugs, in addition to drug repurposing.
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De novo drug discovery by fusion of all molecular multi-omic data, which would be unique at the market, hence expanding the space of compounds (potential drugs), yielding more interesting ones for the pharmaceutical industry.
VALUES TO THE MARKET
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Optimize Pharma and Biotech Data Science operations
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De-risk portfolio assets in Pharma and Biotech companies
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Explainable and transparent AI methods for Precision Medicine (market segments 1-4 above)
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Explainable and transparent AI methods for de novo drug discovery in silico (market segment 5)
OUR TEAM
NATASA PRZULJ
Director
Short bio
Professor Natasa Przulj is an ICREA Research Professor and a Group Leader at Barcelona Supercomputing Center. She is a leader in network science and AI algorithms for biomedical data fusion applied to precision medicine. Her research has been cited over 10,000 times, h-index=45, i10-index=70 (Google Scholar) and supported by over €15 million in competitive funding. Notably, she received three prestigious, single PI, European Research Council (ERC) grants: Consolidator (2018-2024), Proof of Concept (2020-2022) and Starting (2012-2017). She has been elected into: The Serbian Royal Academy of Scientists and Artists in 2019; Academia Europaea, The Academy of Europe, in 2017; Fellow of the British Computer Society (BCS) Academy of Computing, in 2013. In 2014, she received the BCS Roger Needham Award, sponsored by Microsoft Research, in recognition of the potential her research has to revolutionize health and pharmaceutics. She obtained a PhD in Computer Science from the University of Toronto.
Contact
E-mail: natasha@graphlet-tech.com
Tel: +34 689 205 258
Enabled by the support of ERC Proof of Concept, GENETTA
NOEL MALOD-DOGNIN
Chief Scientific Officer (CSO)
Short bio
Dr. Noel Malod-Dognin is an Established Researcher at the Barcelona Supercomputing Center, and a Honorary Senior Researcher at University College London. His research focuses on the development of models and efficient algorithms for fusing, comparing and mining systems-level, large, complex and heterogeneous networked data. He is applying these methods on structural, molecular and clinical data to yield new insight into problems related to human health, and on world trade data for tracking the dynamics of economic systems. He was hired as a scientist in two European Research Council projects on the fusion and the analysis of omics and medical data, and also worked in one French national research agency (ANR) project on the large-scale analysis of protein structures: ERC Consolidator Grant (€2M) project titled “Integrated Connectedness for a New Representation of Biology (ICON-BIO),” from 2018 to 2024, ERC Starting Independent Researcher Grant (€1.64M) project titled “Biological Network Topology Complements Genome as a Source of Biological Information,” from 2012 to 2017, and ANR Calcul Intensif et Simulation (CIS) project PROTEUS (€484K), from 2006 to 2010. The PROTEUS project addresses fold recognition and inverse folding problem towards large scale protein structure prediction.
ALEXANDROS XENOS
Scientist
Short bio
Alexandros Xenos is a Ph.D. student at the Life Sciences Department of the Barcelona Supercomputing Center and a graduate of the National Technical University of Athens. His Ph.D. research is focused on designing network embedding frameworks to interpret and analyze complex biological networks. His research interests lie in the intersection of bioinformatics and machine learning, including data mining, data integration.
STEVAN A. MILINKOVIC
Chief Technology Officer (CTO)
Short bio
Prof. Stevan Milinkovic is the Dean of the Faculty of Computing (RAF), Union University, Belgrade. At the beginning of his career, he worked at the Boris Kidrič - Vinča Institute and also at the Military Technical Institute in Belgrade as an independent researcher. He has been involved in modeling mechanisms and devices for the detection of toxic substances, as well as techniques for recognizing chemical spectra, and has participated in several projects that require a multidisciplinary approach. He has finalized over 40 different microprocessor devices, measuring instruments, software or dedicated computer systems. He also worked at the Faculty of Technology and Metallurgy as an assistant in the subjects Automatic Regulation and Industrial Measurements and Measurements and Process Regulation. In May 1994, he was elected Assistant Professor for the subjects Automatic Regulation and Industrial Measurements and Digital Automatic Control Systems. In 1999, he was elected Associate Professor of Process Control and Digital Automatic Control Systems. From 2003 to 2004, he worked at the Faculty of Computer Science in Belgrade as an associate professor, and in 2004 he was elected full professor for the narrower scientific field of Computer Science. In the period from 2003 to 2007, he was the Dean of the Faculty of Computer Science in Belgrade (two terms). He has participated in international industrial software development projects, together with companies from the USA (Information Technology Resources), Canada (Maple Soft), Japan (Comoros) and South Africa (General Optical). From 2000 to 2002, he was in Canada where he led development teams at Quatrosense Environmental Limited (Richmond, Ontario) on a joint project with NASA SSC, USA, Chrysalis-ITS (Ottawa, Ontario) on joint projects with Macnica, Japan and Entrust, USA. He is a member of: IEEE (Institute of Electrical and Electronics Engineers), ACM (Association for Computing Machinery), IEEE Computer Society, IEEE Technical Committees (Operating Systems and Application Environments, Real-Time Systems, Software Engineering, Test Technology), IEEE Technical Councils (TCSE - Software Process, TCSE - Standards (SECS)), Belgrade Chamber of Commerce, Board of the Association of Informatics, New York Academy of Sciences, Marquis Who's Who in Science and Engineering, 8th edition, 2005-2006.
Carlos Garcia
RESEARCH ENGINEER
Short bio
Carlos Garcia is a Research Engineer at the Barcelona Supercomputing Center, working with a group of scientists to develop and improve the performance of several computer challenges employed in Life Sciences. His research focuses on Graph theory and NLP inspired methodologies to infer molecular similarity, which he applied to ligand-based virtual screening. He notably worked on the Marie Skłodowska-Curie grant No. 713679. Importantly, Dr. Garcia Hernandez is an experienced Software Engineer with more than 8 years of experience in private companies: Lear Corporation, Spain (2017), CMSYS, Florida, USA (2015-2016), and RFID SERVICES, Venezuela (2010-2015), where he developed many software solutions.
SCIENTIFIC ADVISORY BOARD
Top scientists in the field
IGOR JURISICA
Professor
Short bio
Senior Scientist at Krembil Research Institute. Professor in the departments of Computer Science and Medical Biophysics at the University of Toronto, also Visiting Scientist at IBM Center for Advanced Studies and one of the Top-100 AI Leaders in Drug Discovery and Advanced Healthcare.
MARINKA ZITNIK
Professor
Short bio
Assistant Professor at Harvard Medical School, Department of Biomedical Informatics, named a Rising Star in EECS by MIT. She is specializing in machine learning for bio-medical sciences, focusing on designing new methods blending machine learning with statistical methods and network science, which is exactly what Prof. Przulj's lab does as well.
DESMOND HIGHAM
Professor
Short bio
Prof. Des Higham's main area of research is stochastic computation, with applications in computational biology, technological/sociological/security networks and mathematical finance. He held a Royal Society Wolfson Research Merit Award (2012–2017) and is a Society for Industrial and Applied Mathematics (SIAM) Fellow and Fellow of the Royal Society of Edinburgh. He won the 2005 SIAM Germund Dahlquist Prize. He holds an Established Career Fellowship from the EPSRC/URKI Digital Economy programme and is institutional lead on the EPSRC Mathematical Sciences Programme Grant Inference, Computation and Numerics for Insights into Cities (ICONIC). He is a member of Sub-panel 10, Mathematical Sciences, for the 2021 Research Excellence Framework (REF 2021). Higham has authored four books and edited the book: Network Science: Complexity in Nature and Technology (2010, with Ernesto Estrada, Maria Fox and Gian-Luca Oppo). He is Editor-in-Chief of SIAM Review and is a member of the editorial boards of several other journals.
VOLKER MARKL
Professor
Short bio
Volker Markl is a German computer scientist, database systems researcher, and a full professor. He leads the Chair of Database Systems and Information Management and is Director of the Berlin Institute for the Foundations of Learning and Data at the Technische Universität Berlin. He is also Chief Scientist and Head of the Intelligent Analytics for Massive Data Research Group at the German Research Center for Artificial Intelligence in Berlin. He is currently President of the VLDB Endowment, and serves on the academic advisory council to the Alexander von Humboldt Institute for Internet and Society as well as the scientific advisory board to Software AG. Additionally, he co-chairs the Technological Enablers and Data Science Interdisciplinary Working Group of the ‘Plattform Lernende Systeme,‘ a platform of leading experts who are developing a roadmap for the responsible use of self-learning systems and AI, sponsored by BMBF, the German Federal Ministry of Education and Research. He is a strong proponent of data literacy, systems-oriented research, and computer science education.
(SAB membership pending TU Berlin approval)
GET IN TOUCH
Graphlet Technologies Sociedad Limitada
Calle Rambla Catalunya,
Num 47 Planta 1
08007 Barcelona, Spain
+34 689 205 258