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Adapting machine-learning algorithms to design gene circuits
Tom W. Hiscock
*
*
Corresponding author for this work
Cancer Research UK
Wellcome Trust/ Cancer Research UK Gurdon Institute
Research output
:
Contribution to journal
›
Article
›
peer-review
22
Citations (Scopus)
2
Downloads (Pure)
Overview
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Dive into the research topics of 'Adapting machine-learning algorithms to design gene circuits'. Together they form a unique fingerprint.
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Weight
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Mathematics
Learning Algorithm
100%
Machine Learning
92%
Gene
83%
Design
52%
Synthetic Biology
31%
Circuit Design
14%
Python
13%
Open Source
12%
Biological Networks
12%
Cell Cycle
12%
Descent Algorithm
11%
Differentiate
11%
Gradient Descent
11%
Range of data
11%
Turing
10%
Accelerate
10%
Optimization Algorithm
10%
Complex Systems
9%
Biology
9%
Background
8%
Counting
8%
Cell
6%
Module
6%
Model
6%
Simulation
5%
Context
5%
Medicine & Life Sciences
Machine Learning
96%
Gene Regulatory Networks
92%
Synthetic Biology
58%
Boidae
30%
Genetic Association Studies
22%
Cell Cycle
16%
Engineering & Materials Science
Genes
81%
Learning algorithms
70%
Machine learning
62%
Networks (circuits)
46%
Synthetic biology
25%
Cells
8%
Large scale systems
7%
Tissue
7%
Pipelines
6%
Chemical Compounds
Pipeline
98%
Simulation
53%