A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer is a research paper published in mBio (2022). On theSindex it has a DataRank of 0. It has been cited 39 times.
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HHS | National Institutes of Health
Grant: R01CA215574
National Institutes of Health
Grant: 5R01CA215574-04
Identification of Microbiome Based Markers to Improve Colorectal Cancer Detection
FWCI
3.25
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 10 of Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin
Additional file 10 of Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin
Additional file 1 of Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin
Additional file 1 of Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin
Additional file 2 of Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin
Additional file 2 of Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin
Additional file 1 of Proportion-based normalizations outperform compositional data transformations in machine learning applications
Additional file 1 of Proportion-based normalizations outperform compositional data transformations in machine learning applications
Additional file 2 of Proportion-based normalizations outperform compositional data transformations in machine learning applications
Additional file 3 of Proportion-based normalizations outperform compositional data transformations in machine learning applications
Additional file 2 of Proportion-based normalizations outperform compositional data transformations in machine learning applications
Additional file 4 of Proportion-based normalizations outperform compositional data transformations in machine learning applications
Additional file 4 of Proportion-based normalizations outperform compositional data transformations in machine learning applications
Additional file 3 of Proportion-based normalizations outperform compositional data transformations in machine learning applications