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159 results on '"Erdős, Gábor"'

1. Sim2Real Grasp Pose Estimation for Adaptive Robotic Applications

Author

Horváth, Dániel, Bocsi, Kristóf, Erdős, Gábor, and Istenes, Zoltán

Subjects
Computer Science - Robotics
Abstract

Adaptive robotics plays an essential role in achieving truly co-creative cyber physical systems. In robotic manipulation tasks, one of the biggest challenges is to estimate the pose of given workpieces. Even though the recent deep-learning-based models show promising results, they require an immense dataset for training. In this paper, two vision-based, multi-object grasp pose estimation models (MOGPE), the MOGPE Real-Time and the MOGPE High-Precision are proposed. Furthermore, a sim2real method based on domain randomization to diminish the reality gap and overcome the data shortage. Our methods yielded an 80% and a 96.67% success rate in a real-world robotic pick-and-place experiment, with the MOGPE Real-Time and the MOGPE High-Precision model respectively. Our framework provides an industrial tool for fast data generation and model training and requires minimal domain-specific data., Comment: Accpeted for publication in the proceedings of the 22nd IFAC World Congress

Published
2022
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2. Object Detection Using Sim2Real Domain Randomization for Robotic Applications

Author

Horváth, Dániel, Erdős, Gábor, Istenes, Zoltán, Horváth, Tomáš, and Földi, Sándor

Subjects
Computer Science - Robotics, Computer Science - Computer Vision and Pattern Recognition
Abstract

Robots working in unstructured environments must be capable of sensing and interpreting their surroundings. One of the main obstacles of deep-learning-based models in the field of robotics is the lack of domain-specific labeled data for different industrial applications. In this article, we propose a sim2real transfer learning method based on domain randomization for object detection with which labeled synthetic datasets of arbitrary size and object types can be automatically generated. Subsequently, a state-of-the-art convolutional neural network, YOLOv4, is trained to detect the different types of industrial objects. With the proposed domain randomization method, we could shrink the reality gap to a satisfactory level, achieving 86.32% and 97.38% mAP50 scores, respectively, in the case of zero-shot and one-shot transfers, on our manually annotated dataset containing 190 real images. Our solution fits for industrial use as the data generation process takes less than 0.5 s per image and the training lasts only around 12 h, on a GeForce RTX 2080 Ti GPU. Furthermore, it can reliably differentiate similar classes of objects by having access to only one real image for training. To our best knowledge, this is the only work thus far satisfying these constraints., Comment: Published in IEEE Transactions on Robotics (T-RO)

Published
2022
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5. List of contributors

Author

Bhowmick, Jayantika, primary, Brocca, Stefania, additional, Cascarina, Sean M., additional, Chandra, Soumyanetra, additional, Chen, Limin, additional, Davé, Vrushank, additional, Di Bartolo, Ary Lautaro, additional, Dosztányi, Zsuzsanna, additional, Drake, Justin A., additional, Erdős, Gábor, additional, Gondelaud, Frank, additional, Graham, Kristin D., additional, Grandori, Rita, additional, Gupta, Munishwar Nath, additional, Harrison, Paul M., additional, Huang, Qiaojing, additional, Lai, Luhua, additional, Liu, Zhirong, additional, Longhi, Sonia, additional, Masone, Diego, additional, Natalello, Antonino, additional, Pettitt, B. Montgomery, additional, Ross, Eric D., additional, Santambrogio, Carlo, additional, Schramm, Antoine, additional, Stachowiak, Jeanne C., additional, Uversky, Vladimir N., additional, Varadarajan, Raghavan, additional, Workman, Riley J., additional, Yuan, Feng, additional, and Zeno, Wade F., additional

Published
2023
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11. ViralPrimer: a web server to monitor viral nucleic acid amplification tests' primer efficiency during pandemics, with emphasis on SARS-CoV-2 and Mpox.

Author

Deutsch, Norbert, Dosztányi, Zsuzsanna, Csabai, István, Medgyes-Horváth, Anna, Pipek, Orsolya Anna, Stéger, József, Papp, Krisztián, Visontai, Dávid, Erdős, Gábor, and Mentes, Anikó

Subjects
NUCLEIC acid amplification techniques, DATABASES, MONKEYPOX, NUCLEOTIDE sequencing, SARS-CoV-2, INTERNET servers
Abstract

Summary Accurate pathogen identification is crucial during outbreaks, especially with the emergence of new variants requiring frequent primer updates. However, resources for maintaining up-to-date verification of primer sequences are often limited, which poses challenges for reliable diagnosis and hinders potential monitoring efforts based on genome sequencing. To address this, we introduce ViralPrimer, a web server facilitating primer design, SARS-CoV-2 and Mpox variant monitoring, and adaptation to future threats. ViralPrimer aims to enhance diagnostic accuracy with its comprehensive primer database, mutation analysis assistance, and user primer upload feature. Its adaptable design allows monitoring of other rapidly mutating pathogens, contributing to broader public health protection efforts. Availability and implementation ViralPrimer is freely accessible and open to all users with no login requirement at https://viralprimer.elte.hu/. The application is hosted on a DJANGO v3.2.13 web server, with a PostgreSQL database, and the frontend was implemented using jQuery v3.6.0, vanilla JavaScript vES6, and Bootstrap v5.1. [ABSTRACT FROM AUTHOR]

Published
2024
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36. DisProt in 2022 : improved quality and accessibility of protein intrinsic disorder annotation

Author

Quaglia, Federica, Mészáros, Bálint, Salladini, Edoardo, Hatos, András, Pancsa, Rita, Chemes, Lucía B., Pajkos, Mátyás, Lazar, Tamas, Peña-Díaz, Samuel, Santos, Jaime, Ács, Veronika, Farahi, Nazanin, Fichó, Erzsébet, Aspromonte, Maria Cristina, Bassot, Claudio, Chasapi, Anastasia, Davey, Norman E., Davidović, Radoslav, Dobson, Laszlo, Elofsson, Arne, Erdős, Gábor, Gaudet, Pascale, Giglio, Michelle, Glavina, Juliana, Iserte, Javier, Iglesias, Valentín, Kálmán, Zsófia, Lambrughi, Matteo, Leonardi, Emanuela, Longhi, Sonia, Macedo-Ribeiro, Sandra, Maiani, Emiliano, Marchetti, Julia, Marino-Buslje, Cristina, Mészáros, Attila, Monzon, Alexander Miguel, Minervini, Giovanni, Nadendla, Suvarna, Nilsson, Juliet F., Novotný, Marian, Ouzounis, Christos A., Palopoli, Nicolás, Papaleo, Elena, Barbosa Pereira, Pedro José, Pozzati, Gabriele, Promponas, Vasilis J., Pujols, Jordi, Sanchez Rocha, Alma Carolina, Salas, Martin, Rodriguez Sawicki, Luciana, Schad, Eva, Shenoy, Aditi, Szaniszló, Tamás, Tsirigos, Konstantinos D., Veljkovic, Nevena, Parisi, Gustavo, Ventura, Salvador, Dosztányi, Zsuzsanna, Tompa, Peter, Tosatto, Silvio C. E., Piovesan, Damiano, Quaglia, Federica, Mészáros, Bálint, Salladini, Edoardo, Hatos, András, Pancsa, Rita, Chemes, Lucía B., Pajkos, Mátyás, Lazar, Tamas, Peña-Díaz, Samuel, Santos, Jaime, Ács, Veronika, Farahi, Nazanin, Fichó, Erzsébet, Aspromonte, Maria Cristina, Bassot, Claudio, Chasapi, Anastasia, Davey, Norman E., Davidović, Radoslav, Dobson, Laszlo, Elofsson, Arne, Erdős, Gábor, Gaudet, Pascale, Giglio, Michelle, Glavina, Juliana, Iserte, Javier, Iglesias, Valentín, Kálmán, Zsófia, Lambrughi, Matteo, Leonardi, Emanuela, Longhi, Sonia, Macedo-Ribeiro, Sandra, Maiani, Emiliano, Marchetti, Julia, Marino-Buslje, Cristina, Mészáros, Attila, Monzon, Alexander Miguel, Minervini, Giovanni, Nadendla, Suvarna, Nilsson, Juliet F., Novotný, Marian, Ouzounis, Christos A., Palopoli, Nicolás, Papaleo, Elena, Barbosa Pereira, Pedro José, Pozzati, Gabriele, Promponas, Vasilis J., Pujols, Jordi, Sanchez Rocha, Alma Carolina, Salas, Martin, Rodriguez Sawicki, Luciana, Schad, Eva, Shenoy, Aditi, Szaniszló, Tamás, Tsirigos, Konstantinos D., Veljkovic, Nevena, Parisi, Gustavo, Ventura, Salvador, Dosztányi, Zsuzsanna, Tompa, Peter, Tosatto, Silvio C. E., and Piovesan, Damiano

Abstract

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.

Published
2022
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39. DisCanVis: Visualizing integrated structural and functional annotations to better understand the effect of cancer mutations located within disordered proteins.

Author

Deutsch, Norbert, Pajkos, Mátyás, Erdős, Gábor, and Dosztányi, Zsuzsanna

Abstract

Intrinsically disordered proteins (IDPs) play important roles in a wide range of biological processes and have been associated with various diseases, including cancer. In the last few years, cancer genome projects have systematically collected genetic variations underlying multiple cancer types. In parallel, the number and different types of disordered proteins characterized by experimental methods have also significantly increased. Nevertheless, the role of IDPs in various types of cancer is still not well understood. In this work, we present DisCanVis, a novel visualization tool for cancer mutations with a special focus on IDPs. In order to aid the interpretation of observed mutations, genome level information is combined with information about the structural and functional properties of proteins. The web server enables users to inspect individual proteins, collect examples with existing annotations of protein disorder and associated function or to discover currently uncharacterized examples with likely disease relevance. Through a REST API interface and precompiled tables the analysis can be extended to a group of proteins. [ABSTRACT FROM AUTHOR]

Published
2023
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40. DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation

Author

Quaglia, Federica, primary, Mészáros, Bálint, additional, Salladini, Edoardo, additional, Hatos, András, additional, Pancsa, Rita, additional, Chemes, Lucía B, additional, Pajkos, Mátyás, additional, Lazar, Tamas, additional, Peña-Díaz, Samuel, additional, Santos, Jaime, additional, Ács, Veronika, additional, Farahi, Nazanin, additional, Fichó, Erzsébet, additional, Aspromonte, Maria Cristina, additional, Bassot, Claudio, additional, Chasapi, Anastasia, additional, Davey, Norman E, additional, Davidović, Radoslav, additional, Dobson, Laszlo, additional, Elofsson, Arne, additional, Erdős, Gábor, additional, Gaudet, Pascale, additional, Giglio, Michelle, additional, Glavina, Juliana, additional, Iserte, Javier, additional, Iglesias, Valentín, additional, Kálmán, Zsófia, additional, Lambrughi, Matteo, additional, Leonardi, Emanuela, additional, Longhi, Sonia, additional, Macedo-Ribeiro, Sandra, additional, Maiani, Emiliano, additional, Marchetti, Julia, additional, Marino-Buslje, Cristina, additional, Mészáros, Attila, additional, Monzon, Alexander Miguel, additional, Minervini, Giovanni, additional, Nadendla, Suvarna, additional, Nilsson, Juliet F, additional, Novotný, Marian, additional, Ouzounis, Christos A, additional, Palopoli, Nicolás, additional, Papaleo, Elena, additional, Pereira, Pedro José Barbosa, additional, Pozzati, Gabriele, additional, Promponas, Vasilis J, additional, Pujols, Jordi, additional, Rocha, Alma Carolina Sanchez, additional, Salas, Martin, additional, Sawicki, Luciana Rodriguez, additional, Schad, Eva, additional, Shenoy, Aditi, additional, Szaniszló, Tamás, additional, Tsirigos, Konstantinos D, additional, Veljkovic, Nevena, additional, Parisi, Gustavo, additional, Ventura, Salvador, additional, Dosztányi, Zsuzsanna, additional, Tompa, Peter, additional, Tosatto, Silvio C E, additional, and Piovesan, Damiano, additional

Published
2021
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42. Critical assessment of protein intrinsic disorder prediction

Author

Necci, Marco, Piovesan, Damiano, Hoque Md, Tamjidul, Walsh, Ian, Iqbal, Sumaiya, Vendruscolo, Michele, Sormanni, Pietro, Wang, Chen, Raimondi, Daniele, Sharma, Ronesh, Zhou, Yaoqi, Litfin, Thomas, Galzitskaya Oxana, Valerianovna, Lobanov Michail, Yu, Vranken, Wim, Wallner, Björn, Mirabello, Claudio, Malhis, Nawar, Dosztányi, Zsuzsanna, Erdős, Gábor, Mészáros, Bálint, Gao, Jianzhao, Wang, Kui, Hu, Gang, Wu, Zhonghua, Sharma, Alok, Hanson, Jack, Paliwal, Kuldip, Callebaut, Isabelle, Bitard-Feildel, Tristan, Orlando, Gabriele, Peng, Zhenling, Xu, Jinbo, Wang, Sheng, Jones David, T., Cozzetto, Domenico, Meng, Fanchi, Yan, Jing, Gsponer, Jörg, Cheng, Jianlin, Wu, Tianqi, Kurgan, Lukasz, Promponas Vasilis, J., Tamana, Stella, Marino-Buslje, Cristina, Martínez-Pérez, Elizabeth, Chasapi, Anastasia, Ouzounis, Christos, Dunker A., Keith, Kajava Andrey, V., Leclercq Jeremy, Y., Aykac-Fas, Burcu, Lambrughi, Matteo, Maiani, Emiliano, Papaleo, Elena, Chemes Lucia, Beatriz, Álvarez, Lucía, González-Foutel Nicolás, S., Iglesias, Valentin, Pujols, Jordi, Ventura, Salvador, Palopoli, Nicolás, Benítez Guillermo, Ignacio, Parisi, Gustavo, Bassot, Claudio, Elofsson, Arne, Govindarajan, Sudha, Lamb, John, Salvatore, Marco, Hatos, András, Monzon Alexander, Miguel, Bevilacqua, Martina, Mičetić, Ivan, Minervini, Giovanni, Paladin, Lisanna, Quaglia, Federica, Leonardi, Emanuela, Davey, Norman, Horvath, Tamas, Kovacs Orsolya, Panna, Murvai, Nikoletta, Pancsa, Rita, Schad, Eva, Szabo, Beata, Tantos, Agnes, Macedo-Ribeiro, Sandra, Manso Jose, Antonio, Pereira Pedro José, Barbosa, Davidović, Radoslav, Veljkovic, Nevena, Hajdu-Soltész, Borbála, Pajkos, Mátyás, Szaniszló, Tamás, Guharoy, Mainak, Lazar, Tamas, Macossay-Castillo, Mauricio, Tompa, Peter, Tosatto Silvio C., E., Caid, Predictors, DisProt, Curators, Università degli Studi di Padova = University of Padua (Unipd), Institut de minéralogie, de physique des matériaux et de cosmochimie (IMPMC), Muséum national d'Histoire naturelle (MNHN)-Institut de recherche pour le développement [IRD] : UR206-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Necci, Marco [0000-0001-9377-482X], Piovesan, Damiano [0000-0001-8210-2390], Tosatto, Silvio C. E. [0000-0003-4525-7793], Apollo - University of Cambridge Repository, Informatics and Applied Informatics, Chemistry, Basic (bio-) Medical Sciences, Department of Bio-engineering Sciences, Faculty of Sciences and Bioengineering Sciences, Structural Biology Brussels, Tosatto, Silvio CE [0000-0003-4525-7793], ANR-17-CE12-0016,FUNBRCA2,Caractérisation d'un nouveau site de liaison à l'ADN dans la protéine BRCA2(2017), Universita degli Studi di Padova, CAID Predictors, and DisProt Curators

Subjects
Protein Folding, Protein Conformation, Computer science, 631/45/612, analysis, [SDV]Life Sciences [q-bio], purl.org/becyt/ford/1.7 [https], MESH: Amino Acid Sequence, Biochemistry, purl.org/becyt/ford/1 [https], Protein structure, MESH: Protein Conformation, 631/114/2398, Databases, Protein, Biological sciences, ComputingMilieux_MISCELLANEOUS, MESH: Intrinsically Disordered Proteins, 0303 health sciences, 030302 biochemistry & molecular biology, disorder, Critical assessment, Protein folding, Protein Binding, Biotechnology, MESH: Computational Biology, MESH: Databases, Protein, disorder prediction, MESH: Protein Folding, Computational biology, Intrinsically disordered proteins, Orders of magnitude (entropy), 03 medical and health sciences, MESH: Software, Computational platforms and environments, 631/114/2411, Machine learning, Molecule, MESH: Protein Binding, [INFO]Computer Science [cs], Amino Acid Sequence, Molecular Biology, 030304 developmental biology, business.industry, Deep learning, Computational Biology, Proteins, Cell Biology, 631/114/1305, Intrinsically Disordered Proteins, CAID, 631/114/794, Protein structure predictions, Artificial intelligence, business, Software
Abstract

Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude., Results are presented from the first Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment, a community-based blind test to determine the state of the art in predicting intrinsically disordered regions in proteins.

Published
2021
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49. PhaSePro: the database of proteins driving liquid–liquid phase separation

Author

Mészáros, Bálint, primary, Erdős, Gábor, additional, Szabó, Beáta, additional, Schád, Éva, additional, Tantos, Ágnes, additional, Abukhairan, Rawan, additional, Horváth, Tamás, additional, Murvai, Nikoletta, additional, Kovács, Orsolya P, additional, Kovács, Márton, additional, Tosatto, Silvio C E, additional, Tompa, Péter, additional, Dosztányi, Zsuzsanna, additional, and Pancsa, Rita, additional

Published
2019
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