Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Konstantinos Pliakos"'
Publikováno v:
IEEE Access, Vol 11, Pp 41348-41367 (2023)
Random forests are machine learning methods characterised by high performance and robustness to overfitting. However, since multiple learners are combined, they are not as interpretable as a single decision tree. In this work we propose a novel metho
Externí odkaz:
https://doaj.org/article/4c0b38ceb74c4905a769531b5c27ecb1
Publikováno v:
IEEE Access, Vol 10, Pp 117189-117198 (2022)
The cold-start problem is one of the main challenges in recommender systems and specifically in collaborative filtering methods. Such methods, albeit effective, typically can not handle new items or users that do not have any prior interaction activi
Externí odkaz:
https://doaj.org/article/50e4a7ca5b0744a784d07155d2e408c7
Autor:
Konstantinos Pliakos, Celine Vens
Publikováno v:
BMC Bioinformatics, Vol 21, Iss 1, Pp 1-11 (2020)
Abstract Background Computational prediction of drug-target interactions (DTI) is vital for drug discovery. The experimental identification of interactions between drugs and target proteins is very onerous. Modern technologies have mitigated the prob
Externí odkaz:
https://doaj.org/article/3eefdcc981d6442c9bf6edc581fb7186
Autor:
Konstantinos Pliakos, Celine Vens
Publikováno v:
BMC Bioinformatics, Vol 20, Iss 1, Pp 1-12 (2019)
Abstract Background Network inference is crucial for biomedicine and systems biology. Biological entities and their associations are often modeled as interaction networks. Examples include drug protein interaction or gene regulatory networks. Studyin
Externí odkaz:
https://doaj.org/article/177d1cfb310842fa82bf2139f70dc4a2
Publikováno v:
Applied Sciences, Vol 11, Iss 16, p 7502 (2021)
The shift to e-commerce has changed many business areas. Real estate is one of the applications that has been affected by this modern technological wave. Recommender systems are intelligent models that assist users of real estate platforms in finding
Externí odkaz:
https://doaj.org/article/0172be4495924925baaccd581a34aa4d
Autor:
Charles Auffray, Rudi Balling, Inês Barroso, László Bencze, Mikael Benson, Jay Bergeron, Enrique Bernal-Delgado, Niklas Blomberg, Christoph Bock, Ana Conesa, Susanna Del Signore, Christophe Delogne, Peter Devilee, Alberto Di Meglio, Marinus Eijkemans, Paul Flicek, Norbert Graf, Vera Grimm, Henk-Jan Guchelaar, Yi-Ke Guo, Ivo Glynne Gut, Allan Hanbury, Shahid Hanif, Ralf-Dieter Hilgers, Ángel Honrado, D. Rod Hose, Jeanine Houwing-Duistermaat, Tim Hubbard, Sophie Helen Janacek, Haralampos Karanikas, Tim Kievits, Manfred Kohler, Andreas Kremer, Jerry Lanfear, Thomas Lengauer, Edith Maes, Theo Meert, Werner Müller, Dörthe Nickel, Peter Oledzki, Bertrand Pedersen, Milan Petkovic, Konstantinos Pliakos, Magnus Rattray, Josep Redón i Màs, Reinhard Schneider, Thierry Sengstag, Xavier Serra-Picamal, Wouter Spek, Lea A. I. Vaas, Okker van Batenburg, Marc Vandelaer, Peter Varnai, Pablo Villoslada, Juan Antonio Vizcaíno, John Peter Mary Wubbe, Gianluigi Zanetti
Publikováno v:
Genome Medicine, Vol 8, Iss 1, Pp 1-2 (2016)
Externí odkaz:
https://doaj.org/article/6d3f0b60a69d4155a2631338b0688e16
Publikováno v:
Applied Intelligence. 52:3705-3727
Predicting drug-target interactions (DTI) via reliable computational methods is an effective and efficient way to mitigate the enormous costs and time of the drug discovery process. Structure-based drug similarities and sequence-based target protein
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18:1596-1607
Identifying drug-target interactions is crucial for drug discovery. Despite modern technologies used in drug screening, experimental identification of drug-target interactions is an extremely demanding task. Predicting drug-target interactions in sil
Autor:
Jung Yeon Park, Klest Dedja, Konstantinos Pliakos, Jinho Kim, Sean Joo, Frederik Cornillie, Celine Vens, Wim Van den Noortgate
Publikováno v:
Behavior research methods.
To obtain more accurate and robust feedback information from the students' assessment outcomes and to communicate it to students and optimize teaching and learning strategies, educational researchers and practitioners must critically reflect on wheth
Tree-ensemble algorithms, such as random forest, are effective machine learning methods popular for their flexibility, high performance, and robustness to overfitting. However, since multiple learners are combined, they are not as interpretable as a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0fbb031e4350c37797188e5a0dfea5bf