I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
ChemicalX: A Deep Learning Library for Drug Pair Scoring:
, In:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
,
Rozemberczki, Benedek
;
Hoyt, Charles Tapley
;
Gogleva, Anna
... - p. 3819-3828 , 2022
Link:
https://dl.acm.org/doi/10.1145/3534678.3539023
RT T1 <cTitle>Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining</cTitle>: T1 <Title>ChemicalX: A Deep Learning Library for Drug Pair Scoring</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3539023&Exemplar=1&LAN=DE A1 Rozemberczki, Benedek A1 Hoyt, Charles Tapley A1 Gogleva, Anna A1 Grabowski, Piotr A1 Karis, Klas A1 Lamov, Andrej A1 Nikolov, Andriy A1 Nilsson, Sebastian A1 Ughetto, Michael A1 Wang, Yu A1 Derr, Tyler A1 Gyori, Benjamin M. PB ACM YR 2022 K1 chemistry K1 deep learning K1 neural networks K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks SP 3819 OP 3828 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3534678.3539023 DO https://dl.acm.org/doi/10.1145/3534678.3539023 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
