George Matsopoulos

National Technical University of Athens Greece

George K. Matsopoulos received his Diploma in Electrical Engineering in 1988 from the National Technical University of Athens. He received his M.Sc. in 1989 and his Ph.D. in Bioengineering Unit in 1993 from the University of Strathclyde, U.K. He is currently working as an Assistant Professor at the Institute of Communication and Computer Systems, National Technical University of Athens. His interests include nonlinear image processing applied to medical applications, 2-D and 3-D registration of medical images and computer vision applications. Dr. Matsopoulos is a member of the IEEE, the Technical Chamber of Greece and the Hellenic Society of Biomedical Engineering.

1books edited

2chapters authored

Latest work with IntechOpen by George Matsopoulos

The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning technique to train itself in an unsupervised manner. SOMs are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space and they have been used to create an ordered representation of multi-dimensional data which simplifies complexity and reveals meaningful relationships. Prof. T. Kohonen in the early 1980s first established the relevant theory and explored possible applications of SOMs. Since then, a number of theoretical and practical applications of SOMs have been reported including clustering, prediction, data representation, classification, visualization, etc. This book was prompted by the desire to bring together some of the more recent theoretical and practical developments on SOMs and to provide the background for future developments in promising directions. The book comprises of 25 Chapters which can be categorized into three broad areas: methodology, visualization and practical applications.

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Self-Organizing Maps

Edited by George Matsopoulos

Chapters authored

Fingerprint Matching with Self Organizing Maps

By Anastasia N. Ouzounoglou, Pantelis A. Asvestas and George K. Matsopoulos

Part of the book: Self-Organizing Maps

A Bioinformatics Method for the Production of Antibody-Drug Conjugates Through Site-Specific Cysteine Conjugation

By Arianna Filntisi, Dimitrios Vlachakis and George K. Matsopoulos

Antibody-drug conjugates (ADCs) have emerged as a promising class of targeted anticancer therapy, and it is distinguished from traditional chemotherapeutic approaches by its potential to kill cancer cells with limited side effects. Site-specific conjugation is one of the current challenges in ADC development because it allows for controlled conjugation and production of homogeneous ADCs. This chapter describes a computational method for the generation of antibody-drug conjugates as PDB files through site-specific cysteine conjugation, given the PDB files of a drug, a linker, and an antibody. The drug and linker are reconfigured using the rotation and translation functions of an affine transformation, which is brought in appropriate positions for the bonds to occur between the three molecules. The hydrogen and disulfide bonds are employed to connect the linker and drug as well as the linker with the antibody, respectively. Examples of conjugates produced with the presented method have been demonstrated.

Part of the book: Bioinformatics

George Matsopoulos

Latest work with IntechOpen by George Matsopoulos

Self-Organizing Maps

Chapters authored

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