https://github.com/annettaqi/optimal-sampling-for-edna-dataset-

Machine learning involved methods to determine survey sampling parameters for environmental DNA
https://github.com/annettaqi/optimal-sampling-for-edna-dataset-

dna machine-learning

Last synced: about 2 months ago
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Machine learning involved methods to determine survey sampling parameters for environmental DNA

• Host: GitHub
• URL: https://github.com/annettaqi/optimal-sampling-for-edna-dataset-
• Owner: AnnettaQi
• Created: 2024-11-19T19:24:05.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-19T19:47:33.000Z (2 months ago)
• Last Synced: 2024-11-19T20:33:17.069Z (2 months ago)
• Topics: dna, machine-learning
• Homepage:
• Size: 1.5 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Optimal-sampling-tools-for-environmental DNA detection

Machine learning involved methods to determine survey sampling parameters for environmental DNA

Description:

A computer implemented method for determining survey

sampling parameters for environmental DNA ( eDNA )

detection comprises receiving a species selection identifying

selected species and receiving environmental specifications

for an environment to be tested for presence of the species .

A sampling plan is generated using the environmental speci

fications and the species selection , and detectability predic

tion ( s ) are generated using the environmental specifications ,

the species selection and the current sampling plan to predict

whether the selected species is detectable in the environment

according to the current sampling plan . Where at least one

respective selected species is undetectable according to the

current sampling plan , the process iterates , with each sub

sequent iteration incorporating an increase in the total vol

ume to be sampled , until either every respective selected

species is detectable according to the then - current sampling

plan or an iteration stop limit is reached . The sampling

plan ( s ) and detection prediction ( s ) are generated using dif

ferent algorithms

Code is not given due to data privacy rule