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Interpolation of microbiome composition in longitudinal data sets
Omri Peleg,
Elhanan Borenstein
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Corresponding author for this work
COMPUTER SCIENCE
School of Computer Science
Santa Fe Institute
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Keyphrases
Microbial Composition
100%
Longitudinal Data
100%
Longitudinal Microbiome
100%
Interpolation Method
83%
Interpolation Accuracy
83%
Microbiome
50%
Predictive Models
33%
K-nearest
33%
Microbiome Data
33%
Best Practice Guidelines
16%
Multiple Time Points
16%
Large Array
16%
Leave-one-out
16%
Irregular Sampling
16%
Comprehensive Assessment
16%
Human Gut Microbiota
16%
Microbiome Research
16%
Health Impact
16%
Data Accuracy
16%
Data Reliability
16%
Downstream Analysis
16%
Incomplete Sampling
16%
Data Interpolation
16%
Analytical Challenges
16%
Missing Samples
16%
Collection Practices
16%
Microbial Stability
16%
Engineering
Phase Composition
100%
Nearest Neighbor
66%
Periodic Time
33%
Sampling Frequency
33%
Irregular Sampling
33%
Adjacent Sample
33%
Mathematics
Longitudinal Data
100%
Microbiome
100%
time point η
31%
Nearest Neighbor
12%
Predictive Model
12%
Periodic Time
6%
Longitudinal Study
6%
Immunology and Microbiology
Microbiome
100%
Sample Size
13%
K Nearest Neighbor
13%
Gut Microbiome
6%