Configuration

2OMe-LM

2OMe-LM predicts whether the central nucleotide of an RNA window carries a 2′-O-methylation modification.

Sources

2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 3 Results/3.4 Motif analysis (paragraph 2); 2 Materials and methods/2.2 Model architecture/2.2.2 Word2vec embedding (paragraph 2)

1 evaluation · 7 results

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

2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 2 Materials and methods/2.2 Model architecture (paragraph 1); 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3)

Overview

Model type

Recurrent neural network; this record is the paper-specific evaluated configuration.

Sources

Biological inputs

RNA sequence windows centred on candidate modification sites

Sources

2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 3 Results/3.4 Motif analysis (paragraph 2); 4 Conclusion (paragraph 1)

Outputs

Probability that the central site is 2′-O-methylated

Sources

2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3); 3 Results/3.5 Case study (paragraph 1)

Access

Official study implementation and usage documentation: https://github.com/CSUBioGroup/2OMe-LM/blob/2e22439723777b5bacdce72cdcd7cfbde9e88cd1/README.md. This pinned documentation revision is not automatically the evaluated weight revision.

Sources

CSUBioGroup/2OMe-LM README.md · README.md; installation, model download and usage instructions

limited source coverage · Automated source review, 2026-09-16. All specifications and missing details

Evaluations and results

1 evaluation · 7 results. Different protocols are not a single leaderboard.

Filter evaluations

Tested model or methodProtocolDatasetEvaluation setupMetricEvidence origin

Applied filters: All linked evaluations

Exact evaluated configurations and original reported results
Tested configuration	Protocol and dataset	Finding	Evidence and details
Configuration: 2OMe-LM	Protocol: Human RNA 2OMe sites, five-fold cross-validation (human RNA 2-prime-O-methylation site prediction) Dataset: human RNA 2OMe sites	0.919 AUC fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source 2OMe-LM: human RNA 2-prime-O-methylation site prediction 41-nt centred RNA windows; balanced 8,037 positive and 8,037 negative samples after 80% identity filtering, then 8:2 train/test division. Average of five validation folds of the training set. Aggregation: Not reported 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · Table 1, 2OMe-LM row, AUC column
Configuration: 2OMe-LM	Protocol: Human RNA 2OMe sites, five-fold cross-validation (human RNA 2-prime-O-methylation site prediction) Dataset: human RNA 2OMe sites	0.929 AUPR fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source 2OMe-LM: human RNA 2-prime-O-methylation site prediction 41-nt centred RNA windows; balanced 8,037 positive and 8,037 negative samples after 80% identity filtering, then 8:2 train/test division. Average of five validation folds of the training set. Aggregation: Not reported 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · Table 1. (btaf417-T1), row 8 2OMe-LM, column 7: Human RNA 2OMe sites, five-fold cross-validation AUPR
Configuration: 2OMe-LM	Protocol: Human RNA 2OMe sites, five-fold cross-validation (human RNA 2-prime-O-methylation site prediction) Dataset: human RNA 2OMe sites	0.873 Precision fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source 2OMe-LM: human RNA 2-prime-O-methylation site prediction 41-nt centred RNA windows; balanced 8,037 positive and 8,037 negative samples after 80% identity filtering, then 8:2 train/test division. Average of five validation folds of the training set. Aggregation: Not reported 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · Table 1. (btaf417-T1), row 8 2OMe-LM, column 4: Human RNA 2OMe sites, five-fold cross-validation Precision
Configuration: 2OMe-LM	Protocol: Human RNA 2OMe sites, five-fold cross-validation (human RNA 2-prime-O-methylation site prediction) Dataset: human RNA 2OMe sites	0.846 ACC fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source 2OMe-LM: human RNA 2-prime-O-methylation site prediction 41-nt centred RNA windows; balanced 8,037 positive and 8,037 negative samples after 80% identity filtering, then 8:2 train/test division. Average of five validation folds of the training set. Aggregation: Not reported 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · Table 1. (btaf417-T1), row 8 2OMe-LM, column 2: Human RNA 2OMe sites, five-fold cross-validation ACC
Configuration: 2OMe-LM	Protocol: Human RNA 2OMe sites, five-fold cross-validation (human RNA 2-prime-O-methylation site prediction) Dataset: human RNA 2OMe sites	0.811 Recall fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source 2OMe-LM: human RNA 2-prime-O-methylation site prediction 41-nt centred RNA windows; balanced 8,037 positive and 8,037 negative samples after 80% identity filtering, then 8:2 train/test division. Average of five validation folds of the training set. Aggregation: Not reported 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · Table 1. (btaf417-T1), row 8 2OMe-LM, column 5: Human RNA 2OMe sites, five-fold cross-validation Recall
Configuration: 2OMe-LM	Protocol: Human RNA 2OMe sites, five-fold cross-validation (human RNA 2-prime-O-methylation site prediction) Dataset: human RNA 2OMe sites	0.695 MCC unitless · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source 2OMe-LM: human RNA 2-prime-O-methylation site prediction 41-nt centred RNA windows; balanced 8,037 positive and 8,037 negative samples after 80% identity filtering, then 8:2 train/test division. Average of five validation folds of the training set. Aggregation: Not reported 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · Table 1. (btaf417-T1), row 8 2OMe-LM, column 8: Human RNA 2OMe sites, five-fold cross-validation MCC
Configuration: 2OMe-LM	Protocol: Human RNA 2OMe sites, five-fold cross-validation (human RNA 2-prime-O-methylation site prediction) Dataset: human RNA 2OMe sites	0.841 F1-score fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible	Author-reported evaluation · Source checked Methods, coverage and source 2OMe-LM: human RNA 2-prime-O-methylation site prediction 41-nt centred RNA windows; balanced 8,037 positive and 8,037 negative samples after 80% identity filtering, then 8:2 train/test division. Average of five validation folds of the training set. Aggregation: Not reported 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · Table 1. (btaf417-T1), row 8 2OMe-LM, column 3: Human RNA 2OMe sites, five-fold cross-validation F1-score

Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.

Use this model

How it works, versions and access

How it works

How the evaluated method works

SpliceBERT embeddings pass through a dimensionality-reduction layer. A second branch encodes k-mers with word2vec and a bidirectional LSTM. Fused features enter an attention block and three fully connected layers for site classification.

Sources

What was evaluated

The linked evaluation record identifies 2OMe-LM: human RNA 2-prime-O-methylation site prediction. Its dataset, split, adaptation and evidence origin remain attached to the reported results.

Sources

2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · The named evaluation’s methods and comparison table; exact preserved evaluation IDs: evaluation-b2-2ome-lm-2025

Strengths, limitations and unresolved questions

Strengths and limitations

Strengths and considerations

Combines pretrained RNA representations with a separately learned k-mer branch and provides attention-based motif analysis.
Sources
2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · Abstract (paragraph 1); 3 Results/3.4 Motif analysis (paragraph 1)

Limitations and conditions

The reported human-RNA classification task and sampling scheme do not establish performance on every RNA species or modification type.
Sources
2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 3 Results/3.6 Cross-nucleotide evaluation (paragraph 1); 3 Results/3.4 Motif analysis (paragraph 2)

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-7f5b8234967c54

Specifications

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.

Inputs, outputs and configuration
Property	Description and evidence
Model type	Recurrent neural network; this record is the paper-specific evaluated configuration. Sources 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 2 Materials and methods/2.2 Model architecture (paragraph 1); 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3)
Architecture / procedure	SpliceBERT embeddings pass through a dimensionality-reduction layer. A second branch encodes k-mers with word2vec and a bidirectional LSTM. Fused features enter an attention block and three fully connected layers for site classification. Sources 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 2 Materials and methods/2.2 Model architecture (paragraph 1); 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3)
Biological inputs	RNA sequence windows centred on candidate modification sites Sources 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 3 Results/3.4 Motif analysis (paragraph 2); 4 Conclusion (paragraph 1)
Outputs	Probability that the central site is 2′-O-methylated Sources 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3); 3 Results/3.5 Case study (paragraph 1)
Parameters	An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. · Not reported in inspected sources Sources (2) 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model; CSUBioGroup/2OMe-LM README.md · 2 Materials and methods/2.1 Datasets; 2 Materials and methods/2.2 Model architecture; 2 Materials and methods/2.2 Model architecture/2.2.1 Pretrained RNA language model; 2 Materials and methods/2.2 Model architecture/2.2.2 Word2vec embedding; 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction; 2 Materials and methods/2.3 Deep learning baseline models; 2 Materials and methods/2.4 Evaluation metrics; 2 Materials and methods/2.5 Implementation details; inspected for aggregate parameter count (component sizes are not added without an exact configuration); README.md at pinned repository revision
Known versions / configuration	2OMe-LM is the comparison-table label; that label does not specify an immutable weight revision. · Not reported in inspected sources Sources 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · Model identification in the comparison table and corresponding Methods; immutable checkpoint revision is not supplied by the table label.
Training data / fitting	8,037 positive and 8,037 negative examples, with an 80:20 training/test partition; SpliceBERT provides separately pretrained sequence features. Sources 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 2 Materials and methods/2.1 Datasets (paragraph 2); 2 Materials and methods/2.1 Datasets (paragraph 1)
Context limits	SpliceBERT supports up to 1,024 nucleotides; that backbone limit is not an independently verified limit of the complete predictor. Sources 2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · 3 Results/3.3 Effectiveness of pre-trained RNA language model (paragraph 1); Table btaf417-T2 (paragraph 1)
Access	Official study implementation and usage documentation: https://github.com/CSUBioGroup/2OMe-LM/blob/2e22439723777b5bacdce72cdcd7cfbde9e88cd1/README.md. This pinned documentation revision is not automatically the evaluated weight revision. Sources CSUBioGroup/2OMe-LM 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 sources Sources CSUBioGroup/2OMe-LM 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 sources Sources CSUBioGroup/2OMe-LM README.md · README.md; checkpoint/access documentation and licence scope

Evidence

Source checking verifies the cited claim or transcription. It does not establish independent reproduction.

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

Claims, original sources and review scope · Release 2026-09-29-06401fd5b220
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	2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model Original source ↗ 2 Materials and methods/2.2 Model architecture (paragraph 1); 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.332Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.diagram.caption` Source artifact SHA-256: `54fe4db6f35c03d0d4f3ef4da720eb26a832199372c56d0956609ff07af750ee` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram steps RNA sequence windows centred on candidate modification sites 2OMe-LM Probability that the central site is 2′-O-methylated Individual claims	2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model Original source ↗ 2 Materials and methods/2.2 Model architecture (paragraph 1); 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.332Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.diagram.steps` Source artifact SHA-256: `54fe4db6f35c03d0d4f3ef4da720eb26a832199372c56d0956609ff07af750ee` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Diagram title Evaluated procedure (conceptual) Individual claims	2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model Original source ↗ 2 Materials and methods/2.2 Model architecture (paragraph 1); 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.332Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.diagram.title` Source artifact SHA-256: `54fe4db6f35c03d0d4f3ef4da720eb26a832199372c56d0956609ff07af750ee` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Model type Recurrent neural network; this record is the paper-specific evaluated configuration. Individual claims	2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model Original source ↗ 2 Materials and methods/2.2 Model architecture (paragraph 1); 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.332Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.0.value` Source artifact SHA-256: `54fe4db6f35c03d0d4f3ef4da720eb26a832199372c56d0956609ff07af750ee` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Architecture / procedure SpliceBERT embeddings pass through a dimensionality-reduction layer. A second branch encodes k-mers with word2vec and a bidirectional LSTM. Fused features enter an attention block and three fully connected layers for site classification. Individual claims	2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model Original source ↗ 2 Materials and methods/2.2 Model architecture (paragraph 1); 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.332Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.1.value` Source artifact SHA-256: `54fe4db6f35c03d0d4f3ef4da720eb26a832199372c56d0956609ff07af750ee` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
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	CSUBioGroup/2OMe-LM README.md Original source ↗ README.md; checkpoint/access documentation and licence scope Version: 2e22439723777b5bacdce72cdcd7cfbde9e88cd1 Retrieved: 2026-09-16T19:54:12.011441+00:00	unreported automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.10.value` Source artifact SHA-256: `fc90c421ffa9766c1981d6ea15eb73a9a2049f99858e1e3bef99f11263dee2bd` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Biological inputs RNA sequence windows centred on candidate modification sites Individual claims	2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model Original source ↗ 3 Results/3.4 Motif analysis (paragraph 2); 4 Conclusion (paragraph 1) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.332Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.2.value` Source artifact SHA-256: `54fe4db6f35c03d0d4f3ef4da720eb26a832199372c56d0956609ff07af750ee` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Outputs Probability that the central site is 2′-O-methylated Individual claims	2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model Original source ↗ 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3); 3 Results/3.5 Case study (paragraph 1) Version: journal full text in PMC Retrieved: 2026-09-16T10:33:38.332Z	source checked automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.3.value` Source artifact SHA-256: `54fe4db6f35c03d0d4f3ef4da720eb26a832199372c56d0956609ff07af750ee` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims	2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model Original source ↗ 2 Materials and methods/2.1 Datasets; 2 Materials and methods/2.2 Model architecture; 2 Materials and methods/2.2 Model architecture/2.2.1 Pretrained RNA language model; 2 Materials and methods/2.2 Model architecture/2.2.2 Word2vec embedding; 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction; 2 Materials and methods/2.3 Deep learning baseline models; 2 Materials and methods/2.4 Evaluation metrics; 2 Materials and methods/2.5 Implementation details; 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: journal full text in PMC Retrieved: 2026-09-16T10:33:38.332Z	unreported automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `54fe4db6f35c03d0d4f3ef4da720eb26a832199372c56d0956609ff07af750ee` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact
Parameters An aggregate parameter total for this exact evaluated configuration is not established by the inspected sources. Individual claims	CSUBioGroup/2OMe-LM README.md Original source ↗ 2 Materials and methods/2.1 Datasets; 2 Materials and methods/2.2 Model architecture; 2 Materials and methods/2.2 Model architecture/2.2.1 Pretrained RNA language model; 2 Materials and methods/2.2 Model architecture/2.2.2 Word2vec embedding; 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction; 2 Materials and methods/2.3 Deep learning baseline models; 2 Materials and methods/2.4 Evaluation metrics; 2 Materials and methods/2.5 Implementation details; 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: 2e22439723777b5bacdce72cdcd7cfbde9e88cd1 Retrieved: 2026-09-16T19:54:12.011441+00:00	unreported automated source review · 2026-09-16 Audit 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. Field: `attributes.profile.facts.4.value` Source artifact SHA-256: `fc90c421ffa9766c1981d6ea15eb73a9a2049f99858e1e3bef99f11263dee2bd` Hash scope: Hash scope not separately documented; inspect source record Inspected artifact

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Release 2026-09-29-06401fd5b220 · Record review: needs review

2 source records and release history

2OMe-LM: predicting 2′-O-methylation sites in human RNA using a pre-trained RNA language model · Original source · journal full text in PMC
CSUBioGroup/2OMe-LM README.md · Original source · 2e22439723777b5bacdce72cdcd7cfbde9e88cd1

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Technical metadata and extraction receipts

Stable ID: reported-model-7f5b8234967c54

areas: rna-transcriptomes
entity level: method
version: not stated in table
reported name: 2OMe-LM
historical missing metadata: 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: 2ome-lm-2025; source locator: 2 Materials and methods/2.2 Model architecture (paragraph 1); 2 Materials and methods/2.2 Model architecture/2.2.3 Feature fusion and prediction (paragraph 3) | 3 Results/3.4 Motif analysis (paragraph 2); 2 Materials and methods/2.2 Model architecture/2.2.2 Word2vec embedding (paragraph 2); 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.

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model: 2OMe-LM: human RNA 2-prime-O-methylation site prediction

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