SVMrejection — Abdelaal et al. 2019 Table 1
Same underlying SVM (linear kernel) classifier as SVM, with a rejection option enabled.
Overview
Same underlying SVM (linear kernel) classifier as SVM, with a rejection option enabled.
Consult the linked sources for architecture or protocol details. Missing evidence is not evidence of a missing capability.
Evaluations and results
1 evaluation · 2 results. Different protocols are not a single leaderboard.
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| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: SVMrejection — Abdelaal et al. 2019 Table 1 | Protocol: Abdelaal et al. 2019 intra-dataset 5-fold CV: Baron Human pancreatic dataset Dataset: Baron Human pancreatic scRNA-seq dataset (Abdelaal et al. 2019, Table 2) | 0.991 median-f1 fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceabdelaal baron human svm rejection Not reported Aggregation: Not reported A comparison of automatic cell identification methods for single-cell RNA sequencing data · Quoted verbatim, Results, "All classifiers perform well in intra-dataset experiments": "for the Baron Human dataset, the median F1-score for SVM rejection, scmapcell, scPred, and SVM is 0.991, 0.984, 0.981, and 0.980, respectively (Fig. 1a)." Fig. 1a, SVM rejection row. |
| Configuration: SVMrejection — Abdelaal et al. 2019 Table 1 | Protocol: Abdelaal et al. 2019 intra-dataset 5-fold CV: Baron Human pancreatic dataset Dataset: Baron Human pancreatic scRNA-seq dataset (Abdelaal et al. 2019, Table 2) | 1.5% pct-unlabeled percent · unknown Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceabdelaal baron human svm rejection Not reported Aggregation: Not reported A comparison of automatic cell identification methods for single-cell RNA sequencing data · Quoted verbatim, same location: "SVM rejection, scmapcell, and scPred assigned 1.5%, 4.2%, and 10.8% of the cells, respectively, as unlabeled while SVM (without rejection) classified 100% of the cells with a median F1-score of 0.98 (Fig. 1b)." Fig. 1b, SVM rejection row. |
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Release 2026-10-06-161b59a1d02c · Record review: source checked
1 source records and release history
- A comparison of automatic cell identification methods for single-cell RNA sequencing data · Original source · 10.1186/s13059-019-1795-z; published article XML
Technical metadata and extraction receipts
Stable ID: ucc-research-config-abdelaal-svm-rejection
- review
- method: automated_source_review; actor: Claude Sonnet cell-type-annotation-transfer evidence-research worker; reviewed at: 2026-10-06T23:28:54Z; note: Source-backed primary-text transcription of Abdelaal et al. 2019 (Genome Biology). Re-fetched directly from Europe PMC at 2026-10-06T23:28:54Z (this review pass; not the earlier bounded-window estimate), byte-identical to the already-cached copy (SHA-256 unchanged), and archived as a committed artifact at data/omics/use-case-coverage-20261006/research/artifacts/abdelaal-2019-pmc6734286-fulltext.xml.gz. No new model execution, independent experimental replication, or qualified human scientific review.
- training setting
- scikit-learn 0.19.2, Python, SVM (linear kernel); Table 1 columns: Prior knowledge = No, Rejection option = Yes
- source locator
- Table 1, row "SVM rejection"
Related records
- variant of: SVM (linear kernel), scikit-learn
- configuration: abdelaal baron human svm rejection