Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Csaba, Molnar"'
Autor:
Abel Szkalisity, Filippo Piccinini, Attila Beleon, Tamas Balassa, Istvan Gergely Varga, Ede Migh, Csaba Molnar, Lassi Paavolainen, Sanna Timonen, Indranil Banerjee, Elina Ikonen, Yohei Yamauchi, Istvan Ando, Jaakko Peltonen, Vilja Pietiäinen, Viktor Honti, Peter Horvath
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
High-content screening prompted the development of software enabling discrete phenotypic analysis of single cells. Here, the authors show that supervised continuous machine learning can drive novel discoveries in diverse imaging experiments and prese
Externí odkaz:
https://doaj.org/article/f49f0d2d8b5f4b588375e657d0654bfa
Autor:
Csilla Brasko, Kevin Smith, Csaba Molnar, Nora Farago, Lili Hegedus, Arpad Balind, Tamas Balassa, Abel Szkalisity, Farkas Sukosd, Katalin Kocsis, Balazs Balint, Lassi Paavolainen, Marton Z. Enyedi, Istvan Nagy, Laszlo G. Puskas, Lajos Haracska, Gabor Tamas, Peter Horvath
Publikováno v:
Nature Communications, Vol 9, Iss 1, Pp 1-7 (2018)
The isolation of single cells while retaining context is important for quantifying cellular heterogeneity but technically challenging. Here, the authors develop a high-throughput, scalable workflow for microscopy-based single cell isolation using mac
Externí odkaz:
https://doaj.org/article/45fa759495a24fc3831e7ab0ebc0ea21
Publikováno v:
BMC Ophthalmology, Vol 18, Iss 1, Pp 1-6 (2018)
Abstract Background Immunoglobulin G4-related disease (IgG4-rd) is characterized by lymphoplasmacytic infiltration and tissue fibrosis. Orbital manifestations of IgG4-rd may include unilateral or bilateral proptosis, cicatricial extraocular muscle my
Externí odkaz:
https://doaj.org/article/43f547bde71849a1a5274e8ad1a2958d
Autor:
Andrea Rónavári, Nóra Igaz, Dóra I. Adamecz, Bettina Szerencsés, Csaba Molnar, Zoltán Kónya, Ilona Pfeiffer, Monika Kiricsi
Publikováno v:
Molecules, Vol 26, Iss 4, p 844 (2021)
The nanomaterial industry generates gigantic quantities of metal-based nanomaterials for various technological and biomedical applications; however, concomitantly, it places a massive burden on the environment by utilizing toxic chemicals for the pro
Externí odkaz:
https://doaj.org/article/f70a12706b144ca78c202dcb37fa04f0
Autor:
Csaba Molnar, Attila Beleon, Vilja Pietiäinen, Lassi Paavolainen, Tamas Balassa, Jaakko Peltonen, Istvan Gergely Varga, Elina Ikonen, Indranil Banerjee, Sanna Timonen, Ede Migh, Yohei Yamauchi, Filippo Piccinini, Abel Szkalisity, Peter Horvath, István Andó, Viktor Honti
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
Nature Communications
Nature Communications
Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce the Regression Plane (RP), a user-friendly discovery tool e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::308189ac3560bd35b66934cc329b6da8
https://doaj.org/article/f49f0d2d8b5f4b588375e657d0654bfa
https://doaj.org/article/f49f0d2d8b5f4b588375e657d0654bfa
Autor:
Mushriq Al-Jazrawe, Csaba Molnar, Niklas Rindtorff, Steven Blum, William Colgan, Maria Alimova, Sean Misek, Carmen Rios, Moony Tseng, James M. McFarland, Aviv Regev, Beth A. Cimini, Anne E. Carpenter, Adam Bass, Samuel J. Klempner, Jesse Boehm
Publikováno v:
Cancer Research. 82:639-639
Successful mapping of cancer dependencies requires conducting genetic and pharmacological screens in a diversity of cell models. However, existing model development approaches require long periods of culture time during which evolutionary pressures r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdf56ee273aed60ea149c01f3ca84c98
https://doi.org/10.1158/1538-7445.am2022-639
https://doi.org/10.1158/1538-7445.am2022-639
Autor:
Zoltán Balázs, Csaba Molnár
This book introduces the reader into the discursive political pluralism of modern Hungary, roughly from the mid-19th century, with a particular emphasis on the spectrum of contemporary political thought. The book relies on Michael Freeden's method of
Autor:
Dóra Izabella Adamecz, Nóra Igaz, Andrea Rónavári, Csaba Molnar, Bettina Szerencsés, Zoltán Kónya, Ilona Pfeiffer, Mónika Kiricsi
Publikováno v:
Molecules
Molecules, Vol 26, Iss 844, p 844 (2021)
Molecules, Vol 26, Iss 844, p 844 (2021)
The nanomaterial industry generates gigantic quantities of metal-based nanomaterials for various technological and biomedical applications; however, concomitantly, it places a massive burden on the environment by utilizing toxic chemicals for the pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ee099a5ab53cf6cd3ab88e3112051f93
https://pubmed.ncbi.nlm.nih.gov/33562781
https://pubmed.ncbi.nlm.nih.gov/33562781
Autor:
Botond Mathe, Abel Szkalisity, Jozsef Molnar, Krisztian Koos, Allen Goodman, Reka Hollandi, Ferenc Kovács, Ede Migh, Tamas Balassa, Mate Gorbe, Csaba Molnar, Maria Kovacs, Ervin Tasnadi, Arpad Balind, Tivadar Danka, Wenyu Wang, Kevin Smith, Istvan Grexa, Anne E. Carpenter, Lassi Paavolainen, Tímea Tóth, Andras Kriston, Norbert Bara, Peter Horvath
Single cell segmentation is typically one of the first and most crucial tasks of image-based cellular analysis. We present a deep learning approach aiming towards a truly general method for localizing nuclei across a diverse range of assays and light
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4eeafc5fc3a1d837b95509c39db9edb
https://doi.org/10.1101/580605
https://doi.org/10.1101/580605
Autor:
Jozsef Molnar, Peter Horvath, Abel Szkalisity, Botond Mathe, Tivadar Danka, Krisztian Koos, Wenyu Wang, Ervin Tasnadi, Lassi Paavolainen, Kevin Smith, Mate Gorbe, Istvan Grexa, Anne E. Carpenter, Juan C. Caicedo, Tímea Tóth, Ferenc Kovács, Csaba Molnar, Norbert Bara, Allen Goodman, Ede Migh, Maria Kovacs, Andras Kriston, Arpad Balind, Reka Hollandi, Tamas Balassa
Publikováno v:
Cell systems
SUMMARY Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and lig
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7cdc60947a5a356715945272253980cd
https://doi.org/10.1016/j.cels.2020.04.003
https://doi.org/10.1016/j.cels.2020.04.003