Instructions to use multimolecule/aparent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/aparent with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/aparent") model = AutoModel.from_pretrained("multimolecule/aparent") inputs = tokenizer("UAGCUUAUCAGACUGAUGUUGA", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_state - Notebooks
- Google Colab
- Kaggle
File size: 16,643 Bytes
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library_name: multimolecule
license: agpl-3.0
pipeline: polyadenylation
pipeline_tag: other
tags:
- Biology
- RNA
- 3' UTR
- rna
widget:
- example_title: microRNA 21
pipeline_tag: polyadenylation
sequence_type: ncRNA
task: polyadenylation
text: UAGCUUAUCAGACUGAUGUUGA
- example_title: microRNA 146a
pipeline_tag: polyadenylation
sequence_type: ncRNA
task: polyadenylation
text: UGAGAACUGAAUUCCAUGGGUU
- example_title: microRNA 155
pipeline_tag: polyadenylation
sequence_type: ncRNA
task: polyadenylation
text: UUAAUGCUAAUCGUGAUAGGGGUU
- example_title: RNA component of mitochondrial RNA processing endoribonuclease
pipeline_tag: polyadenylation
sequence_type: ncRNA
task: polyadenylation
text: GGUUCGUGCUGAAGGCCUGUAUCCUAGGCUACACACUGAGGACUCUGUUCCUCCCCUUUCCGCCUAGGGGAAAGUCCCCGGACCUCGGGCAGAGAGUGCCACGUGCAUACGCACGUAGACAUUCCCCGCUUCCCACUCCAAAGUCCGCCAAGAAGCGUAUCCCGCUGAGCGGCGUGGCGCGGGGGCGUCAUCCGUCAGCUCCCUCUAGUUACGCAGGCAGUGCGUGUCCGCGCACCAACCACACGGGGCUCAUUCUCAGCGCGGCUGUAAAAAAAAA
- example_title: 7SK small nuclear RNA
pipeline_tag: polyadenylation
sequence_type: ncRNA
task: polyadenylation
text: GGAUGUGAGGGCGAUCUGGCUGCGACAUCUGUCACCCCAUUGAUCGCCAGGGUUGAUUCGGCUGAUCUGGCUGGCUAGGCGGGUGUCCCCUUCCUCCCUCACCGCUCCAUGUGCGUCCCUCCCGAAGCUGCGCGCUCGGUCGAAGAGGACGACCAUCCCCGAUAGAGGAGGACCGGUCUUCGGUCAAGGGUAUACGAGUAGCUGCGCUCCCCUGCUAGAACCUCCAAACAAGCUCUCAAGGUCCAUUUGUAGGAGAACGUAGGGUAGUCAAGCUUCCAAGACUCCAGACACAUCCAAAUGAGGCGCUGCAUGUGGCAGUCUGCCUUUCUUUU
- example_title: telomerase RNA component
pipeline_tag: polyadenylation
sequence_type: ncRNA
task: polyadenylation
text: GGGUUGCGGAGGGUGGGCCUGGGAGGGGUGGUGGCCAUUUUUUGUCUAACCCUAACUGAGAAGGGCGUAGGCGCCGUGCUUUUGCUCCCCGCGCGCUGUUUUUCUCGCUGACUUUCAGCGGGCGGAAAAGCCUCGGCCUGCCGCCUUCCACCGUUCAUUCUAGAGCAAACAAAAAAUGUCAGCUGCUGGCCCGUUCGCCCCUCCCGGGGACCUGCGGCGGGUCGCCUGCCCAGCCCCCGAACCCCGCCUGGAGGCCGCGGUCGGCCCGGGGCUUCUCCGGAGGCACCCACUGCCACCGCGAAGAGUUGGGCUCUGUCAGCCGCGGGUCUCUCGGGGGCGAGGGCGAGGUUCAGGCCUUUCAGGCCGCAGGAAGAGGAACGGAGCGAGUCCCCGCGCGCGGCGCGAUUCCCUGAGCUGUGGGACGUGCACCCAGGACUCGGCUCACACAUGC
- example_title: vault RNA 2-1
pipeline_tag: polyadenylation
sequence_type: ncRNA
task: polyadenylation
text: CGGGUCGGAGUUAGCUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA
- example_title: brain cytoplasmic RNA 1
pipeline_tag: polyadenylation
sequence_type: ncRNA
task: polyadenylation
text: GGCCGGGCGCGGUGGCUCACGCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCUUU
- example_title: HIV-1 TAR-WT
pipeline_tag: polyadenylation
sequence_type: ncRNA
task: polyadenylation
text: GGUCUCUCUGGUUAGACCAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC
- example_title: prion protein (Kanno blood group)
pipeline_tag: polyadenylation
sequence_type: mRNA
task: polyadenylation
text: AUGGCGAACCUUGGCUGCUGGAUGCUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC
- example_title: interleukin 10
pipeline_tag: polyadenylation
sequence_type: mRNA
task: polyadenylation
text: AUGCACAGCUCAGCACUGCUCUGUUGCCUGGUCCUCCUGACUGGGGUGAGGGCC
- example_title: Zaire ebolavirus
pipeline_tag: polyadenylation
sequence_type: mRNA
task: polyadenylation
text: AAUGUUCAAACACUUUGUGAAGCUCUGUUAGCUGAUGGUCUUGCUAAAGCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU
- example_title: SARS coronavirus
pipeline_tag: polyadenylation
sequence_type: mRNA
task: polyadenylation
text: AUGUUUAUUUUCUUAUUAUUUCUUACUCUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU
- example_title: insulin
pipeline_tag: polyadenylation
sequence_type: mRNA
task: polyadenylation
text: AUGGCCCUGUGGAUGCGCCUCCUGCCCCUGCUGGCGCUGCUGGCCCUCUGGGGACCUGACCCAGCCGCAGCCUUUGUGAACCAACACCUGUGCGGCUCACACCUGGUGGAAGCUCUCUACCUAGUGUGCGGGGAACGAGGCUUCUUCUACACACCCAAGACCCGCCGGGAGGCAGAGGACCUGCAGGUGGGGCAGGUGGAGCUGGGCGGGGGCCCUGGUGCAGGCAGCCUGCAGCCCUUGGCCCUGGAGGGGUCCCUGCAGAAGCGUGGCAUUGUGGAACAAUGCUGUACCAGCAUCUGCUCCCUCUACCAGCUGGAGAACUACUGCAACUAG
- example_title: cyclin dependent kinase inhibitor 2A
pipeline_tag: polyadenylation
sequence_type: mRNA
task: polyadenylation
text: AUGGAGCCGGCGGCGGGGAGCAGCAUGGAGCCUUCGGCUGACUGGCUGGCCACGGCCGCGGCCCGGGGUCGGGUAGAGGAGGUGCGGGCGCUGCUGGAGGCGGGGGCGCUGCCCAACGCACCGAAUAGUUACGGUCGGAGGCCGAUCCAGGUCAUGAUGAUGGGCAGCGCCCGAGUGGCGGAGCUGCUGCUGCUCCACGGCGCGGAGCCCAACUGCGCCGACCCCGCCACUCUCACCCGACCCGUGCACGACGCUGCCCGGGAGGGCUUCCUGGACACGCUGGUGGUGCUGCACCGGGCCGGGGCGCGGCUGGACGUGCGCGAUGCCUGGGGCCGUCUGCCCGUGGACCUGGCUGAGGAGCUGGGCCAUCGCGAUGUCGCACGGUACCUGCGCGCGGCUGCGGGGGGCACCAGAGGCAGUAACCAUGCCCGCAUAGAUGCCGCGGAAGGUCCCUCAGACAUCCCCGAUUGA
- example_title: human papillomavirus type 16 E6
pipeline_tag: polyadenylation
sequence_type: mRNA
task: polyadenylation
text: AUGCACCAAAAGAGAACUGCAAUGUUUCAGGACCCACAGGAGCGACCCAGAAAGUUACCACAGUUAUGCACAGAGCUGCAAACAACUAUACAUGAUAUAAUAUUAGAAUGUGUGUACUGCAAGCAACAGUUACUGCGACGUGAGGUAUAUGACUUUGCUUUUCGGGAUUUAUGCAUAGUAUAUAGAGAUGGGAAUCCAUAUGCUGUAUGUGAUAAAUGUUUAAAGUUUUAUUCUAAAAUUAGUGAGUAUAGACAUUAUUGUUAUAGUUUGUAUGGAACAACAUUAGAACAGCAAUACAACAAACCGUUGUGUGAUUUGUUAAUUAGGUGUAUUAACUGUCAAAAGCCACUGUGUCCUGAAGAAAAGCAAAGACAUCUGGACAAAAAGCAAAGAUUCCAUAAUAUAAGGGGUCGGUGGACCGGUCGAUGUAUGUCUUGUUGCAGAUCAUCAAGAACACGUAGAGAAACCCAGCUGUAA
- example_title: NRAS proto-oncogene
pipeline_tag: polyadenylation
sequence_type: 5' UTR
task: polyadenylation
text: GGGGCCGGAAGUGCCGCUCCUUGGUGGGGGCUGUUCAUGGCGGUUCCGGGGUCUCCAACAUUUUUCCCGGCUGUGGUCCUAAAUCUGUCCAAAGCAGAGGCAGUGGAGCUUGAGGUUCUUGCUGGUGUGAA
- example_title: amyloid beta precursor protein
pipeline_tag: polyadenylation
sequence_type: 5' UTR
task: polyadenylation
text: GUCAGUUUCCUCGGCAGCGGUAGGCGAGAGCACGCGGAGGAGCGUGCGCGGGGGCCCCGGGAGACGGCGGCGGUGGCGGCGCGGGCAGAGCAAGGACGCGGCGGAUCCCACUCGCACAGCAGCGCACUCGGUGCCCCGCGCAGGGUCGCG
- example_title: RUNX family transcription factor 1
pipeline_tag: polyadenylation
sequence_type: 5' UTR
task: polyadenylation
text: ACUUCUUUGGGCCUCAUAAACAACCACAGAACCACAAGUUGGGUAGCCUGGCAGUGUCAGAAGUCUGAACCCAGCAUAGUGGUCAGCAGGCAGGACGAAUCACACUGAAUGCAAACCACAGGGUUUCGCAGCGUGGUAAAAGAAAUCAUUGAGUCCCCCGCCUUCAGAAGAGGGUGCAUUUUCAGGAGGAAGCG
- example_title: fragile X messenger ribonucleoprotein 1
pipeline_tag: polyadenylation
sequence_type: 5' UTR
task: polyadenylation
text: CUCAGUCAGGCGCUCAGCUCCGUUUCGGUUUCACUUCCGGUGGAGGGCCGCCUCUGAGCGGGCGGCGGGCCGACGGCGAGCGCGGGCGGCGGCGGUGACGGAGGCGCCGCUGCCAGGGGGCGUGCGGCAGCGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGAGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCUGGGCCUCGAGCGCCCGCAGCCCACCUCUCGGGGGCGGGCUCCCGGCGCUAGCAGGGCUGAAGAGAAG
- example_title: MYC proto-oncogene
pipeline_tag: polyadenylation
sequence_type: 5' UTR
task: polyadenylation
text: AACUCGCUGUAGUAAUUCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCUAGGGUGGAAGAGCCGGGCGAGCAGAGCUGCGCUGCGGGCGUCCUGGGAAGGGAGAUCCGGAGCGAAUAGGGGGCUUCGCCUCUGGCCCAGCCCUCCCGCUGAUCCCCCAGCCAGCGGUCCGCAACCCUUGCCGCAUCCACGAAACUUUGCCCAUAGCAGCGGGCGGGCACUUUGCACUGGAACUUACAACACCCGAGCAAGGACGCGACUCUCCCGACGCGGGGAGGCUAUUCUGCCCAUUUGGGGACACUUCCCCGCCGCUGCCAGGACCCGCUUCUCUGAAAGGCUCUCCUUGCAGCUGCUUAGACG
- example_title: activating transcription factor 4
pipeline_tag: polyadenylation
sequence_type: 5' UTR
task: polyadenylation
text: CAUUUCUACUUUGCCCGCCCACAGAUGUAGUUUUCUCUGCGCGUGUGCGUUUUCCCUCCUCCCCGCCCUCAGGGUCCACGGCCACCAUGGCGUAUUAGGGGCAGCAGUGCCUGCGGCAGCAUUGGCCUUUGCAGCGGCGGCAGCAGCACCAGGCUCUGCAGCGGCAACCCCCAGCGGCUUAAGCCAUGGCGCUUCUCACGGCAUUCAGCAGCAGCGUUGCUGUAACCGACAAAGACACCUUCGAAUUAAGCACAUUCCUCGAUUCCAGCAAAGCACCGCAAC
- example_title: Human GPI protein p137
pipeline_tag: polyadenylation
sequence_type: 3' UTR
task: polyadenylation
text: UUUUUAAAAGGAAAAGAUACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUUUGAAAGGCACCAGUAUGUGUUUUAGAUUGAUUUAAAUGUUUCAUUUAAAUCACGGACAGUAGUUUCAGUUCUGAUGGUAUAAGCAAAACAAAUAAAACGUUUAUAAAAGUUGUAUCUUGAAACACUGGUGUUCAACAGCUAGCAGCUUAUGUGAUUCACCCCAUGCCACGUUAGUGUCACAAAUUUUAUGGUUUAUCUCCAGCAACAUUUCUCUAGUACUUGCACUUAUUAUCUGAAUUC
- example_title: nucleophosmin 1
pipeline_tag: polyadenylation
sequence_type: 3' UTR
task: polyadenylation
text: GAAAAUAGUUUAAACAAUUUGUUAAAAAAUUUUCCGUCUUAUUUCAUUUCUGUAACAGUUGAUAUCUGGCUGUCCUUUUUAUAAUGCAGAGUGAGAACUUUCCCUACCGUGUUUGAUAAAUGUUGUCCAGGUUCUAUUGCCAAGAAUGUGUUGUCCAAAAUGCCUGUUUAGUUUUUAAAGAUGGAACUCCACCCUUUGCUUGGUUUUAAGUAUGUAUGGAAUGUUAUGAUAGGACAUAGUAGUAGCGGUGGUCAGACAUGGAAAUGGUGGGGAGACAAAAAUAUACAUGUGAAAUAAAACUCAGUAUUUUAAUAAAGUAGCACGGUUUCUAUUGA
- example_title: superoxide dismutase 1
pipeline_tag: polyadenylation
sequence_type: 3' UTR
task: polyadenylation
text: ACAUUCCCUUGGAUGUAGUCUGAGGCCCCUUAACUCAUCUGUUAUCCUGCUAGCUGUAGAAAUGUAUCCUGAUAAACAUUAAACACUGUAAUCUUAAAAGUGUAAUUGUGUGACUUUUUCAGAGUUGCUUUAAAGUACCUGUAGUGAGAAACUGAUUUAUGAUCACUUGGAAGAUUUGUAUAGUUUUAUAAAACUCAGUUAAAAUGUCUGUUUCAAUGACCUGUAUUUUGCCAGACUUAAAUCACAGAUGGGUAUUAAACUUGUCAGAAUUUCUUUGUCAUUCAAGCCUGUGAAUAAAAACCCUGUAUGGCACUUAUUAUGAGGCUAUUAAAAGAAUCCAAAUUCAAACUAAA
- example_title: hemoglobin subunit alpha 2
pipeline_tag: polyadenylation
sequence_type: 3' UTR
task: polyadenylation
text: CUGGAGCCUCGGUAGCCGUUCCUCCUGCCCGCUGGGCCUCCCAACGGGCCCUCCUCCCCUCCUUGCACCGGCCCUUCCUGGUCUUUGAAUAAAGUCUGAGUGGGCAGCA
- example_title: BRAF proto-oncogene
pipeline_tag: polyadenylation
sequence_type: 3' UTR
task: polyadenylation
text: AACAAAUGAGUGAGAGAGUUCAGGAGAGUAGCAACAAAAGGAAAAUAAAUGAACAUAUGUUUGCUUAUAUGUUAAAUUGAAUAAAAUACUCUCUUUUUUUUUAAGGUGAACCAAAGAACACUUGUGUGGUUAAAGACUAGAUAUAAUUUUUCCCCAAACUAAAAUUUAUACUUAACAUUGGAUUUUUAACAUCCAAGGGUUAAAAUACAUAGACAUUGCUAAAAAUUGGCAGAGCCUCUUCUAGAGGCUUUACUUUCUGUUCCGGGUUUGUAUCAUUCACUUGGUUAUUUUAAGUAGUAAACUUCAGUUUCUCAUGCAACUUUUGUUGCCAGCUAUCACAUGUCCACUAGGGACUCCAGAAGAAGACCCUACCUAUGCCUGUGUUUGCAGGUGAGAAGUUGGCAGUCGGUUAGCCUGGG
- example_title: H3 clustered histone 1
pipeline_tag: polyadenylation
sequence_type: 3' UTR
task: polyadenylation
text: UUACUGUGGUCUCUCUGACGGUCCAAGCAAAGGCUCUUUUCAGAGCCACCACCUUUUC
---
# APARENT
Convolutional neural network for predicting human 3'UTR Alternative Polyadenylation (APA) from sequence.
## Disclaimer
This is an UNOFFICIAL implementation of [A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation](https://doi.org/10.1016/j.cell.2019.04.046) by Nicholas Bogard, Johannes Linder, et al.
The OFFICIAL repository of APARENT is at [johli/aparent](https://github.com/johli/aparent).
> [!TIP]
> The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.
**The team releasing APARENT did not write this model card for this model so this model card has been written by the MultiMolecule team.**
## Model Details
APARENT (APA REgression NeT) is a convolutional neural network trained on more than 3.5 million randomized 3'UTR poly-A signals expressed on mini-gene reporters in HEK293. Given a fixed-length 205 nt 3'UTR/polyA sequence, APARENT predicts the alternative-polyadenylation isoform proportion (a scalar) and a positional cleavage distribution. The model is primarily used to score the impact of genetic variants on APA regulation and to engineer new polyadenylation signals. Please refer to the [Training Details](#training-details) section for more information on the training process.
The base, non-normalised APARENT model is recommended by the original authors for isoform and variant-effect prediction.
### Model Specification
| Num Layers | Hidden Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens |
| ---------- | ----------- | ------------------ | --------- | -------- | -------------- |
| 4 | 256 | 6.43 | 0.03 | 0.01 | 205 |
### Links
- **Code**: [multimolecule.aparent](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/aparent)
- **Data**: Massively-parallel polyadenylation MPRA, GEO [GSE113849](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113849)
- **Paper**: [A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation](https://doi.org/10.1016/j.cell.2019.04.046)
- **Developed by**: Nicholas Bogard, Johannes Linder, Alexander B. Rosenberg, Georg Seelig
- **Model type**: 1D CNN for alternative polyadenylation isoform and cleavage prediction from 3'UTR sequence
- **Original Repository**: [johli/aparent](https://github.com/johli/aparent)
## Usage
The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip:
```bash
pip install multimolecule
```
### Direct Use
#### APA Isoform Prediction
You can use this model directly to predict the APA isoform proportion of a 3'UTR/polyA sequence:
```python
>>> from multimolecule import RnaTokenizer, AparentForSequencePrediction
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/aparent")
>>> model = AparentForSequencePrediction.from_pretrained("multimolecule/aparent")
>>> output = model(**tokenizer("ACGUACGUACGU", return_tensors="pt"))
>>> output.keys()
odict_keys(['logits'])
```
The full APARENT isoform and cleavage outputs are available on the backbone:
```python
>>> from multimolecule import RnaTokenizer, AparentModel
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/aparent")
>>> model = AparentModel.from_pretrained("multimolecule/aparent")
>>> output = model(**tokenizer("ACGUACGUACGU", return_tensors="pt"))
>>> output.keys()
odict_keys(['pooler_output', 'isoform_logits', 'cleavage_logits'])
```
### Interface
- **Input length**: fixed 205 nt 3'UTR / polyA sequence
- **Output (`AparentModel`)**: `isoform_logits` (scalar APA proportion) + `cleavage_logits` (206-dim positional cleavage distribution)
- **Output (`AparentForSequencePrediction`)**: APA isoform scalar only (`logits`)
## Training Details
APARENT was trained to jointly predict the APA isoform proportion and the positional cleavage distribution of randomized 3'UTR poly-A signals.
### Training Data
APARENT was trained on more than 3.5 million randomized 3'UTR poly-A signal sequences expressed on mini-gene reporters in HEK293 cells (a massively parallel reporter assay, MPRA). The raw sequencing data for the 3'UTR MPRA libraries are available at GEO accession GSE113849.
This APARENT model was trained on all MPRA libraries (no libraries held out) to produce the best general-purpose APA predictor; it differs from the per-library held-out model evaluated in the paper.
### Training Procedure
#### Pre-training
The model was trained to minimize a combined objective: a sigmoid KL-divergence on the isoform proportion and a KL-divergence on the positional cleavage distribution, weighted equally.
## Citation
```bibtex
@article{bogard2019adeep,
author = {Bogard, Nicholas and Linder, Johannes and Rosenberg, Alexander B. and Seelig, Georg},
title = {A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation},
journal = {Cell},
volume = {178},
number = {1},
pages = {91--106.e23},
year = {2019},
publisher = {Elsevier BV},
doi = {10.1016/j.cell.2019.04.046}
}
```
> [!NOTE]
> The artifacts distributed in this repository are part of the MultiMolecule project.
> If MultiMolecule supports your research, please cite the MultiMolecule project as follows:
```bibtex
@software{chen_2024_12638419,
author = {Chen, Zhiyuan and Zhu, Sophia Y.},
title = {MultiMolecule},
doi = {10.5281/zenodo.12638419},
publisher = {Zenodo},
url = {https://doi.org/10.5281/zenodo.12638419},
year = 2024,
month = may,
day = 4
}
```
## Contact
Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card.
Please contact the authors of the [APARENT paper](https://doi.org/10.1016/j.cell.2019.04.046) for questions or comments on the paper/model.
## License
This model implementation is licensed under the [GNU Affero General Public License](license.md).
For additional terms and clarifications, please refer to our [License FAQ](license-faq.md).
```spdx
SPDX-License-Identifier: AGPL-3.0-or-later
``` |