A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems is a research paper published in mBio (2020). On theSindex it has a DataRank of 0.839. It has been cited 268 times.
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Base Score Contribution
0.839
From this paper's citation signal
Citation Network Contribution
0
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This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →NIDDK NIH HHS
Grant: P30 DK034933
HHS | National Institutes of Health
Grant: 1R01CA215574
NCI NIH HHS
Grant: R01 CA215574
National Institutes of Health
Grant: 5P30DK034933-05
GASTROINTESTINAL HORMONE RESEARCH CORE CENTER
National Institutes of Health
Grant: 5R01CA215574-04
Identification of Microbiome Based Markers to Improve Colorectal Cancer Detection
FWCI
9.59
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth
Additional file 1 of Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth
Additional file 3 of Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth
Additional file 3 of Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth
Additional file 4 of Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth
Additional file 4 of Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth
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 1 of Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions
Additional file 1 of Identification of robust and generalizable biomarkers for microbiome-based stratification in lifestyle interventions
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
Additional file 2 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 Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth
Additional file 2 of Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth