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
DeePNAP: A Deep Learning Method to Predict Protein–Nucleic ..:
Pandey, Uddeshya
;
Behara, Sasi M.
;
Sharma, Siddhant
...
Journal of Chemical Information and Modeling. 64 (2024) 6 - p. 1806-1815 , 2024
Link:
https://doi.org/10.1021/acs.jcim.3c01151
RT Journal T1 <Title>DeePNAP: A Deep Learning Method to Predict Protein–Nucleic Acid Binding Affinity from Their Sequences</Title> UL https://suche.suub.uni-bremen.de/peid=cr-10.1021_acs.jcim.3c01151&Exemplar=1&LAN=DE A1 Pandey, Uddeshya A1 Behara, Sasi M. A1 Sharma, Siddhant A1 Patil, Rachit S. A1 Nambiar, Souparnika A1 Koner, Debasish A1 Bhukya, Hussain PB American Chemical Society (ACS) YR 2024 SN 1549-9596 SN 1549-960X JF Journal of Chemical Information and Modeling VO 64 IS 6 SP 1806 OP 1815 LK http://dx.doi.org/https://doi.org/10.1021/acs.jcim.3c01151 DO https://doi.org/10.1021/acs.jcim.3c01151 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
