Model type
Study-specific predictive method; this record is the paper-specific evaluated configuration.
Configuration
MDL4Microbiome
MDL4Microbiome predicts study-specific phenotype labels from several molecular summaries of metagenomic samples.
Sources
Multimodal deep learning applied to classify healthy and disease states of human microbiome · Introduction (paragraph 2); Methods/Generation of feature sets (paragraph 1)How it worksEvaluated procedure (conceptual)
Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings.
Sources
Multimodal deep learning applied to classify healthy and disease states of human microbiome · Methods/Generation of feature sets (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2)At a glance
Biological inputs
Taxonomic, genome-abundance and metabolic-functional features derived from metagenomes
Sources
Multimodal deep learning applied to classify healthy and disease states of human microbiome · Conclusion (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2)Outputs
Study phenotype classifications
Sources
Multimodal deep learning applied to classify healthy and disease states of human microbiome · Supplementary Information (paragraph 1); Conclusion (paragraph 2)limited source coverage · Automated source review, 2026-09-16. All specifications and missing details
Evaluations and results
Release 2026-09-17-d277315f7d76 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.
| Metric and finding | Coverage and uncertainty | Evidence |
|---|---|---|
| MDL4Microbiome: microbiome disease-state classification Configuration: MDL4MicrobiomeTask: microbiome disease-state classificationDataset: 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. |
How it works
How the evaluated method works
A multimodal neural classifier combines conventional taxonomic profiles, genome-level relative abundance and metabolic-functional features.
Sources
Multimodal deep learning applied to classify healthy and disease states of human microbiome · Methods/Generation of feature sets (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2)What was evaluated
The linked evaluation record identifies MDL4Microbiome: microbiome disease-state classification. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
Multimodal deep learning applied to classify healthy and disease states of human microbiome · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b4-020Strengths and limitations
Strengths and considerations
- Combines complementary composition and functional measurements rather than relying on a single abundance table.
Sources
Multimodal deep learning applied to classify healthy and disease states of human microbiome · Conclusion (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 1)
Limitations and conditions
- Retrospective disease-cohort classification is not prospective clinical validation; population, assay and batch effects can affect transfer.
Sources
Multimodal deep learning applied to classify healthy and disease states of human microbiome · Methods/Performance evaluation (paragraph 1); Introduction (paragraph 4)
Profile review details
Primary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction.
Stable record: reported-model-e6ba198c2ac996Specifications
Inputs, training, access and other details
Explanatory profile: limited source coverage · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.
| Property | Description and evidence |
|---|---|
| Model type | Study-specific predictive method; this record is the paper-specific evaluated configuration.SourcesMultimodal deep learning applied to classify healthy and disease states of human microbiome · Methods/Generation of feature sets (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2) |
| Architecture / procedure | A multimodal neural classifier combines conventional taxonomic profiles, genome-level relative abundance and metabolic-functional features.SourcesMultimodal deep learning applied to classify healthy and disease states of human microbiome · Methods/Generation of feature sets (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2) |
| Biological inputs | Taxonomic, genome-abundance and metabolic-functional features derived from metagenomesSourcesMultimodal deep learning applied to classify healthy and disease states of human microbiome · Conclusion (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2) |
| Outputs | Study phenotype classificationsSourcesMultimodal deep learning applied to classify healthy and disease states of human microbiome · Supplementary Information (paragraph 1); Conclusion (paragraph 2) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Multimodal deep learning applied to classify healthy and disease states of human microbiome; DMnBI/MDL4Microbiome README.md · Methods/Data preparation and preprocessing; Methods/Generation of feature sets; Methods/Construction of multimodal deep learning model; Methods/Performance evaluation; Results/Performance evaluation with various model architectures and parameters; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | MDL4Microbiome is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesMultimodal deep learning applied to classify healthy and disease states of human microbiome · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | Disease-associated microbiome cohorts described in the paper, evaluated with leave-one-out cross-validation.SourcesMultimodal deep learning applied to classify healthy and disease states of human microbiome · Methods/Generation of feature sets (paragraph 1); Introduction (paragraph 3) |
| Context limits | A maximum input/context length for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)Multimodal deep learning applied to classify healthy and disease states of human microbiome; DMnBI/MDL4Microbiome README.md · Methods/Data preparation and preprocessing; Methods/Generation of feature sets; Methods/Construction of multimodal deep learning model; Methods/Performance evaluation; Results/Performance evaluation with various model architectures and parameters; inspected for explicit maximum input length (dataset lengths and family-wide limits are not substituted); README.md at pinned repository revision |
| Access | Official study implementation and usage documentation: https://github.com/DMnBI/MDL4Microbiome/blob/0b2076cbd31af62224bd7a90ef0130e4cac50017/README.md. This pinned documentation revision is not automatically the evaluated weight revision.SourcesDMnBI/MDL4Microbiome README.md · README.md; installation, model download and usage instructions |
| Code licence | No explicit code licence was established from the paper’s availability statement and inspected repository-root documentation. · Not reported in inspected sourcesSourcesDMnBI/MDL4Microbiome README.md · README.md and repository-root licence-file search |
| Weights licence | The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. · Not reported in inspected sourcesSourcesDMnBI/MDL4Microbiome README.md · README.md; checkpoint/access documentation and licence scope |
Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
21 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Diagram caption Conceptual input–method–output guide. Check the procedure text and linked evaluation for fitted components, additional inputs and exact settings. Individual claims | Multimodal deep learning applied to classify healthy and disease states of human microbiome Methods/Generation of feature sets (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2) Version: PMC archival version PMC8763943.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram steps ["Taxonomic, genome-abundance and metabolic-functional features derived from metagenomes","MDL4Microbiome","Study phenotype classifications"] Individual claims | Multimodal deep learning applied to classify healthy and disease states of human microbiome Methods/Generation of feature sets (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2) Version: PMC archival version PMC8763943.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Diagram title Evaluated procedure (conceptual) Individual claims | Multimodal deep learning applied to classify healthy and disease states of human microbiome Methods/Generation of feature sets (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2) Version: PMC archival version PMC8763943.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Model type Study-specific predictive method; this record is the paper-specific evaluated configuration. Individual claims | Multimodal deep learning applied to classify healthy and disease states of human microbiome Methods/Generation of feature sets (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2) Version: PMC archival version PMC8763943.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Architecture / procedure A multimodal neural classifier combines conventional taxonomic profiles, genome-level relative abundance and metabolic-functional features. Individual claims | Multimodal deep learning applied to classify healthy and disease states of human microbiome Methods/Generation of feature sets (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2) Version: PMC archival version PMC8763943.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Weights licence The inspected model-access documentation does not explicitly identify terms for this exact evaluated checkpoint or fitted head; repository code terms are shown separately. Individual claims | DMnBI/MDL4Microbiome README.md README.md; checkpoint/access documentation and licence scope Version: 0b2076cbd31af62224bd7a90ef0130e4cac50017 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Biological inputs Taxonomic, genome-abundance and metabolic-functional features derived from metagenomes Individual claims | Multimodal deep learning applied to classify healthy and disease states of human microbiome Conclusion (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2) Version: PMC archival version PMC8763943.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Outputs Study phenotype classifications Individual claims | Multimodal deep learning applied to classify healthy and disease states of human microbiome Supplementary Information (paragraph 1); Conclusion (paragraph 2) Version: PMC archival version PMC8763943.1 | source checked automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | DMnBI/MDL4Microbiome README.md Methods/Data preparation and preprocessing; Methods/Generation of feature sets; Methods/Construction of multimodal deep learning model; Methods/Performance evaluation; Results/Performance evaluation with various model architectures and parameters; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 0b2076cbd31af62224bd7a90ef0130e4cac50017 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
| Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims | Multimodal deep learning applied to classify healthy and disease states of human microbiome Methods/Data preparation and preprocessing; Methods/Generation of feature sets; Methods/Construction of multimodal deep learning model; Methods/Performance evaluation; Results/Performance evaluation with various model architectures and parameters; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: PMC archival version PMC8763943.1 | unreported automated source review · 2026-09-16 Audit detailsPrimary full text and the available official implementation/model documentation were inspected. Explanatory claims are source-backed; unresolved exact-configuration metadata is labelled explicitly. This is automated review, not a human review or independent benchmark reproduction. Field: Source artifact SHA-256: Hash scope: Hash scope not separately documented; inspect source record |
Sources and history
Release 2026-09-17-d277315f7d76 · Record review: needs review
2 source records and release history
- Multimodal deep learning applied to classify healthy and disease states of human microbiome · Original source · PMC archival version PMC8763943.1
- DMnBI/MDL4Microbiome README.md · Original source · 0b2076cbd31af62224bd7a90ef0130e4cac50017
Technical metadata and extraction receipts
Stable ID: reported-model-e6ba198c2ac996
- areas
- microbes-communities
- entity level
- method
- version
- Not reported
- reported name
- MDL4Microbiome
- historical missing metadata
- version: not_reported_in_legacy_extract; checkpoint revision: not_reported_in_legacy_extract; training data: not_reported_in_legacy_extract; licence: not_reported_in_legacy_extract
- metadata review scope
- historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
- legacy kinds
- model
- entity classification
- review date: 2026-09-17; rationale: This source-scoped entry preserves the method/configuration actually named in an evaluation. It is neither a global family identity nor proof of an immutable checkpoint; the linked evaluation retains adaptation, fitting and scoring details.; source ids: mdl4microbiome-2022; source locator: Methods/Generation of feature sets (paragraph 1); Results/Performance evaluation with various model architectures and parameters (paragraph 2) | Introduction (paragraph 2); Methods/Generation of feature sets (paragraph 1); ambiguities: Configuration means the source-labelled evaluated identity. It does not establish missing checkpoint hashes, default settings or equivalence to same-named records in other papers.