Zobrazeno 1 - 10
of 602
pro vyhledávání: '"Gogishvili A"'
Autor:
Gogishvili, Natali
Structural identifiability is an important property of parametric ODE models. When conducting an experiment and inferring the parameter value from the time-series data, we want to know if the value is globally, locally, or non-identifiable. Global id
Externí odkaz:
http://arxiv.org/abs/2406.16132
Hydrophobic patches on protein surfaces play important functional roles in protein-protein and protein-ligand interactions. Large hydrophobic surfaces are also involved in the progression of aggregation diseases. Predicting exposed hydrophobic patche
Externí odkaz:
http://arxiv.org/abs/2405.15928
Publikováno v:
Communications Medicine, Vol 4, Iss 1, Pp 1-3 (2024)
Digital health interventions (DHIs) are being increasingly adopted to improve care outcomes and experiences for people living with HIV (PLHIV). Here, we highlight the importance of DHIs in the context of HIV management and recommendations for their e
Externí odkaz:
https://doaj.org/article/9bf2a6df596a42e597d8b9745b958392
Autor:
Ivanova, Olga, Gavaldá-Garciá, Jose, Gogishvili, Dea, Houtkamp, Isabel, Bouwmeester, Robbin, Feenstra, K. Anton, Abeln, Sanne
While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an
Externí odkaz:
http://arxiv.org/abs/2307.02170
Autor:
Maja Guberina, Nika Guberina, C. Hoffmann, A. Gogishvili, F. Freisleben, A. Herz, J. Hlouschek, T. Gauler, S. Lang, K. Stähr, B. Höing, C. Pöttgen, F. Indenkämpen, A. Santiago, A. Khouya, S. Mattheis, M. Stuschke
Publikováno v:
Radiation Oncology, Vol 19, Iss 1, Pp 1-17 (2024)
Abstract Background The aim of the present study is to examine the impact of kV-CBCT-based online adaptive radiation therapy (ART) on dosimetric parameters in comparison to image-guided-radiotherapy (IGRT) in consecutive patients with tumors in the h
Externí odkaz:
https://doaj.org/article/9fde0f96fb67444689ee2f39a440cd67
Autor:
Baramidze, Ana, Makharadze, Tamta, Gogishvili, Miranda, Melkadze, Tamar, Giorgadze, Davit, Penkov, Konstantin, Laktionov, Konstantin, Nemsadze, Gia, Nechaeva, Marina, Rozhkova, Irina, Kalinka, Ewa, AG McIntyre, Debra, Perez, Javier, Kaul, Manika, Quek, Ruben G.W., Seebach, Frank, Rietschel, Petra, Pouliot, Jean-Francois
Publikováno v:
In Lung Cancer July 2024 193
Autor:
van Gils, Juami Hermine Mariama, Gogishvili, Dea, van Eck, Jan, Bouwmeester, Robbin, van Dijk, Erik, Abeln, Sanne
Proteins tend to bury hydrophobic residues inside their core during the folding process to provide stability to the protein structure and to prevent aggregation. Nevertheless, proteins do expose some 'sticky' hydrophobic residues to the solvent. Thes
Externí odkaz:
http://arxiv.org/abs/2107.11837
Autor:
Mustafa Ozguroglu, Saadettin Kilickap, Igor Bondarenko, Ahmet Sezer, Mahmut Gümüş, Miranda Gogishvili, Xuanyao He, Giuseppe Gullo, Petra Rietschel, Ruben GW Quek
Publikováno v:
Journal for ImmunoTherapy of Cancer, Vol 11, Iss Suppl 1 (2023)
Externí odkaz:
https://doaj.org/article/d567674c409344d1a377a09a1890d0a4
Autor:
Teunissen, Charlotte E., Kimble, Leighann, Bayoumy, Sherif, Bolsewig, Katharina, Burtscher, Felicia, Coppens, Salomé, Das, Shreyasee, Gogishvili, Dea, Fernandes Gomes, Bárbara, Gómez de San José, Nerea, Mavrina, Ekaterina, Meda, Francisco J., Mohaupt, Pablo, Mravinacová, Sára, Waury, Katharina, Wojdała, Anna Lidia, Abeln, Sanne, Chiasserini, Davide, Hirtz, Christophe, Gaetani, Lorenzo, Vermunt, Lisa, Bellomo, Giovanni, Halbgebauer, Steffen, Lehmann, Sylvain, Månberg, Anna, Nilsson, Peter, Otto, Markus, Vanmechelen, Eugeen, Verberk, Inge M.W., Willemse, Eline, Zetterberg, Henrik
Publikováno v:
In Molecular & Cellular Proteomics October 2023 22(10)
Autor:
Dea Gogishvili, Eleonora M. Vromen, Sascha Koppes-den Hertog, Afina W. Lemstra, Yolande A. L. Pijnenburg, Pieter Jelle Visser, Betty M. Tijms, Marta Del Campo, Sanne Abeln, Charlotte E. Teunissen, Lisa Vermunt
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-13 (2023)
Abstract Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques with the aim to identify cerebrospinal fluid (CS
Externí odkaz:
https://doaj.org/article/df9cdcb95df74b00868389130e51501d