Rohit shakya biography of albert
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Interview with Rafal Marszalek, Chief Editor of Scientific Reports
In-house Editors
Chief Editor: Rafal Marszalek, PhD; Springer Nature, UK
Rafal's background is analytical and biological chemistry. He did his PhD and postdoctoral research in single-cell proteomics at Imperial College London, UK. He was an editor at Genome Biology before joining Scientific Reports in August
ORCID
Deputy Editor: Elizabeth Mann, PhD; Springer Nature, UK
Elizabeth has a background in pharmacology and completed her PhD in neuropharmacology at King's College London, UK. She joi
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Dr. R. K. Saket
Department/School/Unit Name
Department of Electrical Engineering IIT(BHU)
Phone No(s): ,
Email: @, rksaket@, drrksaket@
Area of Interest: Reliability Engineering, Power System Reliability, Reliability Analysis of Modern Power Systems, Reliability Enhancement of Electrical Machines & Drives, Renewable Power Generation Systems, Reliability Assessment of Power Electronics Converters, WECS Controller Design, Modern Power System Analysis
Dr. R.K. SaketFIE (India), FIETE (India), SMIEEE (USA), MIET (UK)
B.E. (Electrical Engineering); M.E. (Power Electronics & Drives); Ph.D. (Power System Reliability Engineering)
GYTI Award : Appreciated by Hon'ble President of India at Rashtrapati Bhavan, New Delhi, India
IEEE IAS Global Distinguished Educators Award (USA) : The Maldives National University, Republic of Maldives
Professor
Electrical Engineering Department
INDIAN INSTITUTE OF TECHNOLOGY
(Banaras Hindu University), Varanasi, (UP)
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Abstract
Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the struktur of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes. Biomedical knowledge graphs have often been constructed by integrating databases that were populated by experts via manual curation, but we are now seeing a more robust use of automated systems. A number of techniques are used to represent knowledge graphs, but often machine learning methods are used to construct a low-dimensional representation that can support many different applications. This representation fryst vatten designed to preserve a knowledge graph’s local and/or global structure. Additional machine learning methods can be applied to this representation to man predictions within genomic, pharmaceutical, and clinical domains. We frame our discussion first around