Model type
Study-specific predictive method; this record is the paper-specific evaluated configuration.
Pipeline
scaLR
scaLR is a low-resource single-cell annotation pipeline with feature selection and neural classification.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Abstract (paragraph 1); Results/Performance evaluation of scaLR using all genes as features (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
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Abstract (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1)At a glance
Biological inputs
Single-cell gene-expression data
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Introduction (paragraph 1); Introduction (paragraph 2)Outputs
Selected features, cell-type predictions and downstream analyses
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results/Comparison of top-K differential gene expression features between full and test set samples (paragraph 1); Abstract (paragraph 1)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 |
|---|---|---|
| scaLR: PBMC cell-type classification All features and samples from PBMCs-BS. Author-reported evaluation · Evaluation metadata: needs review | ||
| 0.942 Cell-type accuracy Unit: unitless · Direction: unknown | Uncertainty: not reported in legacy extract Scored: Not reported · Eligible: Not reported | source checkedscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Table 2, scaLR row, Cell type Accuracy column Source checking is not independent reproduction. |
How it works
How the evaluated method works
Models are trained on feature subsets to estimate feature importance, then the final model uses the top-K selected features. Batched samples and CPU-parallel training reduce memory requirements.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Abstract (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1)What was evaluated
The linked evaluation record identifies scaLR: PBMC cell-type classification. Its dataset, split, adaptation and evidence origin remain attached to the reported results.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-lit-b3-015Strengths and limitations
Strengths and considerations
- Makes feature selection and memory-aware processing part of the workflow.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Methods/The scaLR platform (paragraph 1); Abstract (paragraph 1)
Limitations and conditions
- The selected features and final training data are part of the fitted system; resource claims are tied to the study’s hardware and workloads.
Sources
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Abstract (paragraph 1); Methods/scaLR performance comparison with different pipelines (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-7cf2f9951e1dbaSpecifications
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.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Abstract (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1) |
| Architecture / procedure | Models are trained on feature subsets to estimate feature importance, then the final model uses the top-K selected features. Batched samples and CPU-parallel training reduce memory requirements.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Abstract (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1) |
| Biological inputs | Single-cell gene-expression dataSourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Introduction (paragraph 1); Introduction (paragraph 2) |
| Outputs | Selected features, cell-type predictions and downstream analysesSourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results/Comparison of top-K differential gene expression features between full and test set samples (paragraph 1); Abstract (paragraph 1) |
| Parameters | An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sourcesSources (2)scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery; infocusp/scaLR README.md · Methods/Single-cell RNA-Seq data selection and download; Methods/The scaLR platform; Methods/The scaLR platform/Data processing; Methods/The scaLR platform/Feature extraction; Methods/The scaLR platform/Training; Methods/The scaLR platform/Evaluation and downstream analysis; Methods/scaLR performance comparison with different pipelines; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision |
| Known versions / configuration | scaLR is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sourcesSourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label. |
| Training data / fitting | Feature-subset linear models rank genes using mean absolute class weights; a final DNN is fitted on the selected top-K genes, with checkpoint selection using validation data.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Methods/The scaLR platform/Feature extraction (paragraph 1); Methods/The scaLR platform/Training (paragraph 1) |
| Context limits | A selected gene-feature matrix rather than a sequence-token context window; the chosen top-K is part of the fitted configuration.SourcesscaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Results/Comparison of top-K differential gene expression features between full and test set samples (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1) |
| Access | Official study implementation and usage documentation: https://github.com/infocusp/scaLR/blob/b5f72ce8f9bd25cb90f4b6b3112a03288c788f2e/README.md. This pinned documentation revision is not automatically the evaluated weight revision.Sourcesinfocusp/scaLR README.md · README.md; installation, model download and usage instructions |
| Code licence | GNU GPL version 3 (study repository code at the cited revision; this does not establish every dependency or historical checkpoint licence).Sourcesinfocusp/scaLR LICENSE · LICENSE; complete licence text |
| 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 sourcesSourcesinfocusp/scaLR 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.
20 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 | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Abstract (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1) Version: version of record | 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 ["Single-cell gene-expression data","scaLR","Selected features, cell-type predictions and downstream analyses"] Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Abstract (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1) Version: version of record | 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 | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Abstract (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1) Version: version of record | 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 | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Abstract (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1) Version: version of record | 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 Models are trained on feature subsets to estimate feature importance, then the final model uses the top-K selected features. Batched samples and CPU-parallel training reduce memory requirements. Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Abstract (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1) Version: version of record | 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 | infocusp/scaLR README.md README.md; checkpoint/access documentation and licence scope Version: b5f72ce8f9bd25cb90f4b6b3112a03288c788f2e | 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 Single-cell gene-expression data Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Introduction (paragraph 1); Introduction (paragraph 2) Version: version of record | 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 Selected features, cell-type predictions and downstream analyses Individual claims | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Results/Comparison of top-K differential gene expression features between full and test set samples (paragraph 1); Abstract (paragraph 1) Version: version of record | 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 | infocusp/scaLR README.md Methods/Single-cell RNA-Seq data selection and download; Methods/The scaLR platform; Methods/The scaLR platform/Data processing; Methods/The scaLR platform/Feature extraction; Methods/The scaLR platform/Training; Methods/The scaLR platform/Evaluation and downstream analysis; Methods/scaLR performance comparison with different pipelines; 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: b5f72ce8f9bd25cb90f4b6b3112a03288c788f2e | 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 | scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery Methods/Single-cell RNA-Seq data selection and download; Methods/The scaLR platform; Methods/The scaLR platform/Data processing; Methods/The scaLR platform/Feature extraction; Methods/The scaLR platform/Training; Methods/The scaLR platform/Evaluation and downstream analysis; Methods/scaLR performance comparison with different pipelines; 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: version of record | 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
3 source records and release history
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery · Original source · version of record
- infocusp/scaLR README.md · Original source · b5f72ce8f9bd25cb90f4b6b3112a03288c788f2e
- infocusp/scaLR LICENSE · Original source · b5f72ce8f9bd25cb90f4b6b3112a03288c788f2e
Technical metadata and extraction receipts
Stable ID: reported-model-7cf2f9951e1dba
- areas
- cells-tissues
- entity level
- method
- version
- Not reported
- reported name
- scaLR
- 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: Feature-subset models supply importance scores for top-K feature selection; a separately fitted final classifier consumes the selected features. Preserve the exact source-scoped composition and its results; no additional checkpoint or family equivalence is inferred.; source ids: scalr-2025; source locator: Abstract (paragraph 1); Methods/The scaLR platform/Feature extraction (paragraph 1) | Abstract (paragraph 1); Results/Performance evaluation of scaLR using all genes as features (paragraph 1) | Methods: scaLR platform and Feature Extraction Algorithm 1; Results: platform overview; Figures 1 and 3; ambiguities: This is the paper-specific pipeline identity. Missing component versions or checkpoint hashes remain unknown; a shared upstream name does not establish equivalent pipelines.