Characteristics of Gut Microbiota in Patients With Diabetes Determined by Data Mining Analysis of Terminal Restriction Fragment Length Polymorphisms
Bibliographic record
Abstract
Background: This study was performed to clarify whether gut microbiota obtained from fecal samples could identify the type of diabetes in patients of each gender by using a combination of terminal restriction fragment length polymorphism (T-RFLP) analysis and data mining. Methods: A cross-sectional study was performed at three centers. Fecal samples were collected from 12 Japanese patients with type 1 diabetes mellitus (T1D), 18 patients with type 2 diabetes mellitus (T2D), and 31 subjects without diabetes mellitus (non-DM). Amplification of fecal 16S rRNA was carried out. After digestion of the amplification products with restriction enzymes ( Alu I, Bsl I, Hae III, and Msp I), terminal restriction fragments (T-RFs) of DNA were detected. A data mining algorithm (classification and regression tree (CART) modeling system) provides a decision tree that classifies subjects into various groups according to pre-assigned characteristics. Results: Among men, the error rate was 2.4% with Msp I, while error rates were 0.0% with other restriction enzymes. Among women, the error rate was 0.0% with all restriction enzymes. The operational taxonomic units (OTUs) incorporated into the decision tree differed between men and women. Conclusions: We were able to classify the 16SrRNA gene amplification products obtained from fecal samples of T1D patients, T2D patients, and non-DM subjects with a high level of precision by combining T-RFLP analysis and data mining. Specific gut microbiota patterns were found for T1D and T2D patients, as well as a sex difference of the patterns. J Clin Med Res. 2019;11(6):401-406 doi: https://doi.org/10.14740/jocmr3791
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".