Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Jannis Born"'
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-14 (2024)
Abstract Machine learning algorithms have shown great accuracy in predicting chemical reaction outcomes and retrosyntheses. However, designing synthesis pathways remains challenging for existing machine learning models which are trained for single-st
Externí odkaz:
https://doaj.org/article/645eb6f591f346859cf2e0c92b6a3894
Autor:
Katja Ovchinnikova, Jannis Born, Panagiotis Chouvardas, Marianna Rapsomaniki, Marianna Kruithof-de Julio
Publikováno v:
npj Precision Oncology, Vol 8, Iss 1, Pp 1-7 (2024)
Abstract Machine learning (ML) models of drug sensitivity prediction are becoming increasingly popular in precision oncology. Here, we identify a fundamental limitation in standard measures of drug sensitivity that hinders the development of personal
Externí odkaz:
https://doaj.org/article/0b70ce5cd8e84908af41f9aac145c56b
Autor:
Nathaniel H. Park, Matteo Manica, Jannis Born, James L. Hedrick, Tim Erdmann, Dmitry Yu. Zubarev, Nil Adell-Mill, Pedro L. Arrechea
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-15 (2023)
Abstract Advances in machine learning (ML) and automated experimentation are poised to vastly accelerate research in polymer science. Data representation is a critical aspect for enabling ML integration in research workflows, yet many data models imp
Externí odkaz:
https://doaj.org/article/701a0247664540b78e39c2b39912603c
Autor:
Matteo Manica, Jannis Born, Joris Cadow, Dimitrios Christofidellis, Ashish Dave, Dean Clarke, Yves Gaetan Nana Teukam, Giorgio Giannone, Samuel C. Hoffman, Matthew Buchan, Vijil Chenthamarakshan, Timothy Donovan, Hsiang Han Hsu, Federico Zipoli, Oliver Schilter, Akihiro Kishimoto, Lisa Hamada, Inkit Padhi, Karl Wehden, Lauren McHugh, Alexy Khrabrov, Payel Das, Seiji Takeda, John R. Smith
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-6 (2023)
Abstract With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery. They harness powerful representations learned from datasets to speed up the formulation of
Externí odkaz:
https://doaj.org/article/d563c21247234f54b23d37c88d05e3d1
Autor:
Nathaniel H. Park, Matteo Manica, Jannis Born, James L. Hedrick, Tim Erdmann, Dmitry Yu. Zubarev, Nil Adell-Mill, Pedro L. Arrechea
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-1 (2023)
Externí odkaz:
https://doaj.org/article/8a1f0c7a37a8468b9d873a5a194d79dc
Autor:
Anwaar Ulhaq, Jannis Born, Asim Khan, Douglas Pinto Sampaio Gomes, Subrata Chakraborty, Manoranjan Paul
Publikováno v:
IEEE Access, Vol 8, Pp 179437-179456 (2020)
The COVID-19 pandemic has triggered an urgent call to contribute to the fight against an immense threat to the human population. Computer Vision, as a subfield of artificial intelligence, has enjoyed recent success in solving various complex problems
Externí odkaz:
https://doaj.org/article/f4014aa50e414b7d90a4873d2741178c
Publikováno v:
Computers and Education: Artificial Intelligence, Vol 3, Iss , Pp 100063- (2022)
In primary schools, Lesen durch Schreiben (LdS; “reading through writing”, known internationally as inventive spelling) is a prevalent didactic method of reading and spelling instruction. In LdS, pupils learn writing through prolonged inventive s
Externí odkaz:
https://doaj.org/article/ee68d60e38b14f7b96359cb134a84951
Autor:
Jannis Born, David Beymer, Deepta Rajan, Adam Coy, Vandana V. Mukherjee, Matteo Manica, Prasanth Prasanna, Deddeh Ballah, Michal Guindy, Dorith Shaham, Pallav L. Shah, Emmanouil Karteris, Jan L. Robertus, Maria Gabrani, Michal Rosen-Zvi
Publikováno v:
Patterns, Vol 2, Iss 6, Pp 100269- (2021)
Summary: Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for
Externí odkaz:
https://doaj.org/article/96dfaf7a0d8346f788dd6f9f30ca030c
Autor:
Jannis Born, Matteo Manica, Ali Oskooei, Joris Cadow, Greta Markert, María Rodríguez Martínez
Publikováno v:
iScience, Vol 24, Iss 4, Pp 102269- (2021)
Summary: With the advent of deep generative models in computational chemistry, in-silico drug design is undergoing an unprecedented transformation. Although deep learning approaches have shown potential in generating compounds with desired chemical p
Externí odkaz:
https://doaj.org/article/2878649a15784acc80e2b1f5cc274279
Autor:
Jannis Born, Nina Wiedemann, Manuel Cossio, Charlotte Buhre, Gabriel Brändle, Konstantin Leidermann, Julie Goulet, Avinash Aujayeb, Michael Moor, Bastian Rieck, Karsten Borgwardt
Publikováno v:
Applied Sciences, Vol 12, Iss 8, p 3869 (2022)
The authors wish to make the following corrections to this paper [...]
Externí odkaz:
https://doaj.org/article/f4d2e7293c4a439aa77a11a85eff1656