0.97 accuracy
MDL4Microbiome · accuracy · CRC microbiome cohort
- Tested model
- MDL4Microbiome
- Task or benchmark
- microbiome disease-state classification
- Dataset
- CRC microbiome cohort
- Procedure
- Multimodal deep learning model on colorectal-cancer versus healthy microbiome samples
- Evaluation
- MDL4Microbiome: microbiome disease-state classification
- Evidence
- Author-reported evaluation · source checkedMultimodal deep learning applied to classify healthy and disease states of human microbiome · Table 3, CRC row, MDL4Microbiome column
A source-checked result verifies the numerical transcription, not every model or protocol detail. Evaluation metadata: needs review. Source checked does not mean independently reproduced.
Evaluation results
Release 2026-09-16-d74d282221a9 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| MDL4Microbiome: microbiome disease-state classification Model: MDL4Microbiome · Benchmark: microbiome disease-state classification · Dataset: CRC microbiome cohort Multimodal deep learning model on colorectal-cancer versus healthy microbiome samples Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.97 accuracy Unit: fraction · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedMultimodal deep learning applied to classify healthy and disease states of human microbiome · Table 3, CRC row, MDL4Microbiome column Source checking is not independent reproduction. |
Sources and history
Release 2026-09-16-d74d282221a9 · Record review: source checked
- Multimodal deep learning applied to classify healthy and disease states of human microbiome · Original source · PMC archival version PMC8763943.1
Technical metadata and extraction receipts
Stable ID: lit-b4-020
- areas
- microbes-communities
- tasks
- microbiome disease-state classification
- printed value
- 0.97
- numeric value
- 0.97
- metric
- accuracy
- metric direction
- unknown
- unit
- fraction
- uncertainty
- Not reported
- source locator
- Table 3, CRC row, MDL4Microbiome column
- review
- method: primary_xml_exact_label_cell_check; reviewer: rewire deterministic table checker v1; reviewed at: 2026-09-16T10:33:58.492Z; notes: Exact row/header labels and numeric cell matched. Check verifies transcription, not experimental correctness.; evidence: Table 3, CRC row, MDL4Microbiome column; cell: 0.97; artifact sha256: 72330eda245ac97c5de5d47a491bb86d9b8f527a03ae25fa41cae5bfb637143b; retrieval url: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC8763943/fullTextXML
- legacy id
- lit-b4-020
- legacy row
- id: lit-b4-020; paper id: mdl4microbiome-2022; domain id: microbes-communities; task: microbiome disease-state classification; model: MDL4Microbiome; model version: Not reported; dataset: CRC microbiome cohort; dataset version: Not reported; split: Not reported; metric: accuracy; value: 0.97; unit: fraction; uncertainty: Not reported; protocol: Multimodal deep learning model on colorectal-cancer versus healthy microbiome samples; source locator: Table 3, CRC row, MDL4Microbiome column; source url: https://pmc.ncbi.nlm.nih.gov/articles/PMC8763943/; evaluation origin: author_reported; reviewed utc: 2026-09-15T23:37:05Z
- missing metadata
- model version: not_reported_in_legacy_extract; dataset version: not_reported_in_legacy_extract; split: not_reported_in_legacy_extract; uncertainty: not_reported_in_legacy_extract