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21ENGBIO - High-Throughput Design of Novel Sensors to Help Address the Impending Phosphate Crisis
Wood, Chris
(Principal Investigator)
Doerner, Peter
(Co-investigator)
Richardson, Annis
(Co-investigator)
School of Biological Sciences
Overview
Fingerprint
Research output
(1)
Project Details
Status
Finished
Effective start/end date
31/01/22
→
30/01/23
Funding
BBSRC:
£126,012.00
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Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
Convolutional Neural Network
Engineering
100%
Protein Engineering
Biochemistry, Genetics and Molecular Biology
100%
Peptide Sequence
Biochemistry, Genetics and Molecular Biology
100%
Optimal Sequence
Engineering
33%
Command Line
Engineering
33%
Computational Cost
Engineering
33%
User Interfaces
Engineering
33%
Design Constraint
Engineering
33%
Research output
Research output per year
2024
2024
2024
1
Article
Research output per year
Research output per year
TIMED-Design: Flexible and accessible protein sequence design with convolutional neural networks
Castorina, L. V.
,
Ünal, S. M.
,
Subr, K.
&
Wood, C. W.
,
30 Jan 2024
,
In:
Protein Engineering, Design & Selection (PEDS).
37
,
p. 1-11
11 p.
Research output
:
Contribution to journal
›
Article
›
peer-review
Open Access
File
Convolutional Neural Network
100%
Peptide Sequence
100%
Protein Engineering
100%
Main Disadvantage
33%
User Interfaces
33%