Instructions to use multimolecule/deltasplice-human with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MultiMolecule
How to use multimolecule/deltasplice-human with MultiMolecule:
pip install multimolecule
from multimolecule import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("multimolecule/deltasplice-human") model = AutoModel.from_pretrained("multimolecule/deltasplice-human") inputs = tokenizer("UAGCUUAUCAGACUGAUGUUGA", return_tensors="pt") outputs = model(**inputs) embeddings = outputs.last_hidden_state - Notebooks
- Google Colab
- Kaggle
datasets:
- multimolecule/deltasplice-human
library_name: multimolecule
license: agpl-3.0
pipeline: splice-site
pipeline_tag: other
tags:
- Biology
- RNA
- Splicing
- rna
widget:
- example_title: microRNA 21
pipeline_tag: splice-site
sequence_type: ncRNA
task: splice-site
text: UAGCUUAUCAGACUGAUGUUGA
- example_title: microRNA 146a
pipeline_tag: splice-site
sequence_type: ncRNA
task: splice-site
text: UGAGAACUGAAUUCCAUGGGUU
- example_title: microRNA 155
pipeline_tag: splice-site
sequence_type: ncRNA
task: splice-site
text: UUAAUGCUAAUCGUGAUAGGGGUU
- example_title: RNA component of mitochondrial RNA processing endoribonuclease
pipeline_tag: splice-site
sequence_type: ncRNA
task: splice-site
text: >-
GGUUCGUGCUGAAGGCCUGUAUCCUAGGCUACACACUGAGGACUCUGUUCCUCCCCUUUCCGCCUAGGGGAAAGUCCCCGGACCUCGGGCAGAGAGUGCCACGUGCAUACGCACGUAGACAUUCCCCGCUUCCCACUCCAAAGUCCGCCAAGAAGCGUAUCCCGCUGAGCGGCGUGGCGCGGGGGCGUCAUCCGUCAGCUCCCUCUAGUUACGCAGGCAGUGCGUGUCCGCGCACCAACCACACGGGGCUCAUUCUCAGCGCGGCUGUAAAAAAAAA
- example_title: 7SK small nuclear RNA
pipeline_tag: splice-site
sequence_type: ncRNA
task: splice-site
text: >-
GGAUGUGAGGGCGAUCUGGCUGCGACAUCUGUCACCCCAUUGAUCGCCAGGGUUGAUUCGGCUGAUCUGGCUGGCUAGGCGGGUGUCCCCUUCCUCCCUCACCGCUCCAUGUGCGUCCCUCCCGAAGCUGCGCGCUCGGUCGAAGAGGACGACCAUCCCCGAUAGAGGAGGACCGGUCUUCGGUCAAGGGUAUACGAGUAGCUGCGCUCCCCUGCUAGAACCUCCAAACAAGCUCUCAAGGUCCAUUUGUAGGAGAACGUAGGGUAGUCAAGCUUCCAAGACUCCAGACACAUCCAAAUGAGGCGCUGCAUGUGGCAGUCUGCCUUUCUUUU
- example_title: telomerase RNA component
pipeline_tag: splice-site
sequence_type: ncRNA
task: splice-site
text: >-
GGGUUGCGGAGGGUGGGCCUGGGAGGGGUGGUGGCCAUUUUUUGUCUAACCCUAACUGAGAAGGGCGUAGGCGCCGUGCUUUUGCUCCCCGCGCGCUGUUUUUCUCGCUGACUUUCAGCGGGCGGAAAAGCCUCGGCCUGCCGCCUUCCACCGUUCAUUCUAGAGCAAACAAAAAAUGUCAGCUGCUGGCCCGUUCGCCCCUCCCGGGGACCUGCGGCGGGUCGCCUGCCCAGCCCCCGAACCCCGCCUGGAGGCCGCGGUCGGCCCGGGGCUUCUCCGGAGGCACCCACUGCCACCGCGAAGAGUUGGGCUCUGUCAGCCGCGGGUCUCUCGGGGGCGAGGGCGAGGUUCAGGCCUUUCAGGCCGCAGGAAGAGGAACGGAGCGAGUCCCCGCGCGCGGCGCGAUUCCCUGAGCUGUGGGACGUGCACCCAGGACUCGGCUCACACAUGC
- example_title: vault RNA 2-1
pipeline_tag: splice-site
sequence_type: ncRNA
task: splice-site
text: >-
CGGGUCGGAGUUAGCUCAAGCGGUUACCUCCUCAUGCCGGACUUUCUAUCUGUCCAUCUCUGUGCUGGGGUUCGAGACCCGCGGGUGCUUACUGACCCUUUUAUGCAA
- example_title: brain cytoplasmic RNA 1
pipeline_tag: splice-site
sequence_type: ncRNA
task: splice-site
text: >-
GGCCGGGCGCGGUGGCUCACGCCUGUAAUCCCAGCUCUCAGGGAGGCUAAGAGGCGGGAGGAUAGCUUGAGCCCAGGAGUUCGAGACCUGCCUGGGCAAUAUAGCGAGACCCCGUUCUCCAGAAAAAGGAAAAAAAAAAACAAAAGACAAAAAAAAAAUAAGCGUAACUUCCCUCAAAGCAACAACCCCCCCCCCCCUUU
- example_title: HIV-1 TAR-WT
pipeline_tag: splice-site
sequence_type: ncRNA
task: splice-site
text: GGUCUCUCUGGUUAGACCAGAUCUGAGCCUGGGAGCUCUCUGGCUAACUAGGGAACC
- example_title: prion protein (Kanno blood group)
pipeline_tag: splice-site
sequence_type: mRNA
task: splice-site
text: AUGGCGAACCUUGGCUGCUGGAUGCUGGUUCUCUUUGUGGCCACAUGGAGUGACCUGGGCCUCUGC
- example_title: interleukin 10
pipeline_tag: splice-site
sequence_type: mRNA
task: splice-site
text: AUGCACAGCUCAGCACUGCUCUGUUGCCUGGUCCUCCUGACUGGGGUGAGGGCC
- example_title: Zaire ebolavirus
pipeline_tag: splice-site
sequence_type: mRNA
task: splice-site
text: >-
AAUGUUCAAACACUUUGUGAAGCUCUGUUAGCUGAUGGUCUUGCUAAAGCAUUUCCUAGCAAUAUGAUGGUAGUCACAGAGCGUGAGCAAAAAGAAAGCUUAUUGCAUCAAGCAUCAUGGCACCACACAAGUGAUGAUUUUGGUGAGCAUGCCACAGUUAGAGGGAGUAGCUUUGUAACUGAUUUAGAGAAAUACAAUCUUGCAUUUAGAUAUGAGUUUACAGCACCUUUUAUAGAAUAUUGUAACCGUUGCUAUGGUGUUAAGAAUGUUUUUAAUUGGAUGCAUUAUACAAUCCCACAGUGUUAU
- example_title: SARS coronavirus
pipeline_tag: splice-site
sequence_type: mRNA
task: splice-site
text: >-
AUGUUUAUUUUCUUAUUAUUUCUUACUCUCACUAGUGGUAGUGACCUUGACCGGUGCACCACUUUUGAUGAUGUUCAAGCUCCUAAUUACACUCAACAUACUUCAUCUAUGAGGGGGGUUUACUAUCCUGAUGAAAUUUUUAGAUCAGACACUCUUUAUUUAACUCAGGAUUUAUUUCUUCCAUUUUAUUCUAAUGUUACAGGGUUUCAUACUAUUAAUCAUACGUUUGACAACCCUGUCAUACCUUUUAAGGAUGGUAUUUAUUUUGCUGCCACAGAGAAAUCAAAUGUUGUCCGUGGUUGGGUUUUUGGUUCUACCAUGAACAACAAGUCACAGUCGGUGAUUAUUAUUAACAAUUCUACUAAUGUUGUUAUACGAGCAUGUAACUUUGAAUUGUGUGACAACCCUUUCUUUGCUGUUUCUAAACCCAUGGGUACACAGACACAUACUAUGAUAUUCGAUAAUGCAUUUAAAUGCACUUUCGAGUACAUAUCU
- example_title: insulin
pipeline_tag: splice-site
sequence_type: mRNA
task: splice-site
text: >-
AUGGCCCUGUGGAUGCGCCUCCUGCCCCUGCUGGCGCUGCUGGCCCUCUGGGGACCUGACCCAGCCGCAGCCUUUGUGAACCAACACCUGUGCGGCUCACACCUGGUGGAAGCUCUCUACCUAGUGUGCGGGGAACGAGGCUUCUUCUACACACCCAAGACCCGCCGGGAGGCAGAGGACCUGCAGGUGGGGCAGGUGGAGCUGGGCGGGGGCCCUGGUGCAGGCAGCCUGCAGCCCUUGGCCCUGGAGGGGUCCCUGCAGAAGCGUGGCAUUGUGGAACAAUGCUGUACCAGCAUCUGCUCCCUCUACCAGCUGGAGAACUACUGCAACUAG
- example_title: cyclin dependent kinase inhibitor 2A
pipeline_tag: splice-site
sequence_type: mRNA
task: splice-site
text: >-
AUGGAGCCGGCGGCGGGGAGCAGCAUGGAGCCUUCGGCUGACUGGCUGGCCACGGCCGCGGCCCGGGGUCGGGUAGAGGAGGUGCGGGCGCUGCUGGAGGCGGGGGCGCUGCCCAACGCACCGAAUAGUUACGGUCGGAGGCCGAUCCAGGUCAUGAUGAUGGGCAGCGCCCGAGUGGCGGAGCUGCUGCUGCUCCACGGCGCGGAGCCCAACUGCGCCGACCCCGCCACUCUCACCCGACCCGUGCACGACGCUGCCCGGGAGGGCUUCCUGGACACGCUGGUGGUGCUGCACCGGGCCGGGGCGCGGCUGGACGUGCGCGAUGCCUGGGGCCGUCUGCCCGUGGACCUGGCUGAGGAGCUGGGCCAUCGCGAUGUCGCACGGUACCUGCGCGCGGCUGCGGGGGGCACCAGAGGCAGUAACCAUGCCCGCAUAGAUGCCGCGGAAGGUCCCUCAGACAUCCCCGAUUGA
- example_title: human papillomavirus type 16 E6
pipeline_tag: splice-site
sequence_type: mRNA
task: splice-site
text: >-
AUGCACCAAAAGAGAACUGCAAUGUUUCAGGACCCACAGGAGCGACCCAGAAAGUUACCACAGUUAUGCACAGAGCUGCAAACAACUAUACAUGAUAUAAUAUUAGAAUGUGUGUACUGCAAGCAACAGUUACUGCGACGUGAGGUAUAUGACUUUGCUUUUCGGGAUUUAUGCAUAGUAUAUAGAGAUGGGAAUCCAUAUGCUGUAUGUGAUAAAUGUUUAAAGUUUUAUUCUAAAAUUAGUGAGUAUAGACAUUAUUGUUAUAGUUUGUAUGGAACAACAUUAGAACAGCAAUACAACAAACCGUUGUGUGAUUUGUUAAUUAGGUGUAUUAACUGUCAAAAGCCACUGUGUCCUGAAGAAAAGCAAAGACAUCUGGACAAAAAGCAAAGAUUCCAUAAUAUAAGGGGUCGGUGGACCGGUCGAUGUAUGUCUUGUUGCAGAUCAUCAAGAACACGUAGAGAAACCCAGCUGUAA
- example_title: NRAS proto-oncogene
pipeline_tag: splice-site
sequence_type: 5' UTR
task: splice-site
text: >-
GGGGCCGGAAGUGCCGCUCCUUGGUGGGGGCUGUUCAUGGCGGUUCCGGGGUCUCCAACAUUUUUCCCGGCUGUGGUCCUAAAUCUGUCCAAAGCAGAGGCAGUGGAGCUUGAGGUUCUUGCUGGUGUGAA
- example_title: amyloid beta precursor protein
pipeline_tag: splice-site
sequence_type: 5' UTR
task: splice-site
text: >-
GUCAGUUUCCUCGGCAGCGGUAGGCGAGAGCACGCGGAGGAGCGUGCGCGGGGGCCCCGGGAGACGGCGGCGGUGGCGGCGCGGGCAGAGCAAGGACGCGGCGGAUCCCACUCGCACAGCAGCGCACUCGGUGCCCCGCGCAGGGUCGCG
- example_title: RUNX family transcription factor 1
pipeline_tag: splice-site
sequence_type: 5' UTR
task: splice-site
text: >-
ACUUCUUUGGGCCUCAUAAACAACCACAGAACCACAAGUUGGGUAGCCUGGCAGUGUCAGAAGUCUGAACCCAGCAUAGUGGUCAGCAGGCAGGACGAAUCACACUGAAUGCAAACCACAGGGUUUCGCAGCGUGGUAAAAGAAAUCAUUGAGUCCCCCGCCUUCAGAAGAGGGUGCAUUUUCAGGAGGAAGCG
- example_title: fragile X messenger ribonucleoprotein 1
pipeline_tag: splice-site
sequence_type: 5' UTR
task: splice-site
text: >-
CUCAGUCAGGCGCUCAGCUCCGUUUCGGUUUCACUUCCGGUGGAGGGCCGCCUCUGAGCGGGCGGCGGGCCGACGGCGAGCGCGGGCGGCGGCGGUGACGGAGGCGCCGCUGCCAGGGGGCGUGCGGCAGCGCGGCGGCGGCGGCGGCGGCGGCGGCGGCGGAGGCGGCGGCGGCGGCGGCGGCGGCGGCGGCUGGGCCUCGAGCGCCCGCAGCCCACCUCUCGGGGGCGGGCUCCCGGCGCUAGCAGGGCUGAAGAGAAG
- example_title: MYC proto-oncogene
pipeline_tag: splice-site
sequence_type: 5' UTR
task: splice-site
text: >-
AACUCGCUGUAGUAAUUCCAGCGAGAGGCAGAGGGAGCGAGCGGGCGGCCGGCUAGGGUGGAAGAGCCGGGCGAGCAGAGCUGCGCUGCGGGCGUCCUGGGAAGGGAGAUCCGGAGCGAAUAGGGGGCUUCGCCUCUGGCCCAGCCCUCCCGCUGAUCCCCCAGCCAGCGGUCCGCAACCCUUGCCGCAUCCACGAAACUUUGCCCAUAGCAGCGGGCGGGCACUUUGCACUGGAACUUACAACACCCGAGCAAGGACGCGACUCUCCCGACGCGGGGAGGCUAUUCUGCCCAUUUGGGGACACUUCCCCGCCGCUGCCAGGACCCGCUUCUCUGAAAGGCUCUCCUUGCAGCUGCUUAGACG
- example_title: activating transcription factor 4
pipeline_tag: splice-site
sequence_type: 5' UTR
task: splice-site
text: >-
CAUUUCUACUUUGCCCGCCCACAGAUGUAGUUUUCUCUGCGCGUGUGCGUUUUCCCUCCUCCCCGCCCUCAGGGUCCACGGCCACCAUGGCGUAUUAGGGGCAGCAGUGCCUGCGGCAGCAUUGGCCUUUGCAGCGGCGGCAGCAGCACCAGGCUCUGCAGCGGCAACCCCCAGCGGCUUAAGCCAUGGCGCUUCUCACGGCAUUCAGCAGCAGCGUUGCUGUAACCGACAAAGACACCUUCGAAUUAAGCACAUUCCUCGAUUCCAGCAAAGCACCGCAAC
- example_title: Human GPI protein p137
pipeline_tag: splice-site
sequence_type: 3' UTR
task: splice-site
text: >-
UUUUUAAAAGGAAAAGAUACCAAAUGCCUGCUGCUACCACCCUUUUCAAUUGCUAUGUUUUGAAAGGCACCAGUAUGUGUUUUAGAUUGAUUUAAAUGUUUCAUUUAAAUCACGGACAGUAGUUUCAGUUCUGAUGGUAUAAGCAAAACAAAUAAAACGUUUAUAAAAGUUGUAUCUUGAAACACUGGUGUUCAACAGCUAGCAGCUUAUGUGAUUCACCCCAUGCCACGUUAGUGUCACAAAUUUUAUGGUUUAUCUCCAGCAACAUUUCUCUAGUACUUGCACUUAUUAUCUGAAUUC
- example_title: nucleophosmin 1
pipeline_tag: splice-site
sequence_type: 3' UTR
task: splice-site
text: >-
GAAAAUAGUUUAAACAAUUUGUUAAAAAAUUUUCCGUCUUAUUUCAUUUCUGUAACAGUUGAUAUCUGGCUGUCCUUUUUAUAAUGCAGAGUGAGAACUUUCCCUACCGUGUUUGAUAAAUGUUGUCCAGGUUCUAUUGCCAAGAAUGUGUUGUCCAAAAUGCCUGUUUAGUUUUUAAAGAUGGAACUCCACCCUUUGCUUGGUUUUAAGUAUGUAUGGAAUGUUAUGAUAGGACAUAGUAGUAGCGGUGGUCAGACAUGGAAAUGGUGGGGAGACAAAAAUAUACAUGUGAAAUAAAACUCAGUAUUUUAAUAAAGUAGCACGGUUUCUAUUGA
- example_title: superoxide dismutase 1
pipeline_tag: splice-site
sequence_type: 3' UTR
task: splice-site
text: >-
ACAUUCCCUUGGAUGUAGUCUGAGGCCCCUUAACUCAUCUGUUAUCCUGCUAGCUGUAGAAAUGUAUCCUGAUAAACAUUAAACACUGUAAUCUUAAAAGUGUAAUUGUGUGACUUUUUCAGAGUUGCUUUAAAGUACCUGUAGUGAGAAACUGAUUUAUGAUCACUUGGAAGAUUUGUAUAGUUUUAUAAAACUCAGUUAAAAUGUCUGUUUCAAUGACCUGUAUUUUGCCAGACUUAAAUCACAGAUGGGUAUUAAACUUGUCAGAAUUUCUUUGUCAUUCAAGCCUGUGAAUAAAAACCCUGUAUGGCACUUAUUAUGAGGCUAUUAAAAGAAUCCAAAUUCAAACUAAA
- example_title: hemoglobin subunit alpha 2
pipeline_tag: splice-site
sequence_type: 3' UTR
task: splice-site
text: >-
CUGGAGCCUCGGUAGCCGUUCCUCCUGCCCGCUGGGCCUCCCAACGGGCCCUCCUCCCCUCCUUGCACCGGCCCUUCCUGGUCUUUGAAUAAAGUCUGAGUGGGCAGCA
- example_title: BRAF proto-oncogene
pipeline_tag: splice-site
sequence_type: 3' UTR
task: splice-site
text: >-
AACAAAUGAGUGAGAGAGUUCAGGAGAGUAGCAACAAAAGGAAAAUAAAUGAACAUAUGUUUGCUUAUAUGUUAAAUUGAAUAAAAUACUCUCUUUUUUUUUAAGGUGAACCAAAGAACACUUGUGUGGUUAAAGACUAGAUAUAAUUUUUCCCCAAACUAAAAUUUAUACUUAACAUUGGAUUUUUAACAUCCAAGGGUUAAAAUACAUAGACAUUGCUAAAAAUUGGCAGAGCCUCUUCUAGAGGCUUUACUUUCUGUUCCGGGUUUGUAUCAUUCACUUGGUUAUUUUAAGUAGUAAACUUCAGUUUCUCAUGCAACUUUUGUUGCCAGCUAUCACAUGUCCACUAGGGACUCCAGAAGAAGACCCUACCUAUGCCUGUGUUUGCAGGUGAGAAGUUGGCAGUCGGUUAGCCUGGG
- example_title: H3 clustered histone 1
pipeline_tag: splice-site
sequence_type: 3' UTR
task: splice-site
text: UUACUGUGGUCUCUCUGACGGUCCAAGCAAAGGCUCUUUUCAGAGCCACCACCUUUUC
DeltaSplice
Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.
Disclaimer
This is an UNOFFICIAL implementation of Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences by Chencheng Xu, Suying Bao, et al.
The OFFICIAL repository of DeltaSplice is at chaolinzhanglab/DeltaSplice.
The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.
The team releasing DeltaSplice did not write this model card for this model so this model card has been written by the MultiMolecule team.
Model Details
DeltaSplice predicts splice-site usage (SSU) and splicing-altering mutation effects from sequence. The model uses a valid-convolution dilated residual encoder and three prediction modules: splice-site usage, reference-informed delta-SSU, and an auxiliary splice-site head. The official package uses the average prediction of five checkpoints for SSU and delta-SSU prediction; MultiMolecule stores the five seed checkpoints of each released data variant as internal ensemble members and returns their average prediction.
Variants
- multimolecule/deltasplice: DeltaSplice trained on the multi-species training set used by the upstream default checkpoints.
- multimolecule/deltasplice-human: DeltaSplice human-only comparison checkpoint set released by the upstream project.
Model Specification
| Variant | Num Layers | Hidden Size | Context | Ensemble Members | Num Parameters (M) | FLOPs (M) | MACs (M) |
|---|---|---|---|---|---|---|---|
| DeltaSplice | 24 | 64 | 30000 | 5 | 40.376 | 1642965.72 | 820284.36 |
| DeltaSplice-Human | 24 | 64 | 30000 | 5 | 40.376 | 1642965.72 | 820284.36 |
(FLOPs and MACs measured on one requested output nucleotide with the default 30 kb padded context.)
Links
- Code: multimolecule.deltasplice
- Paper: Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences
- Developed by: Chencheng Xu, Suying Bao, Ye Wang, Wenxing Li, Hao Chen, Yufeng Shen, Tao Jiang, Chaolin Zhang
- Model type: Dilated residual 1D CNN ensemble for splice-site usage and delta-SSU prediction
- Original Repository: chaolinzhanglab/DeltaSplice
Usage
The model file depends on the multimolecule library. You can install it using pip:
pip install multimolecule
Direct Use
Splice-Site Usage
>>> from multimolecule import RnaTokenizer
>>> from multimolecule.models.deltasplice import DeltaSpliceModel
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/deltasplice-human")
>>> model = DeltaSpliceModel.from_pretrained("multimolecule/deltasplice-human")
>>> inputs = tokenizer("AGCAGUCAUUAUGGCGAAUCUGGCAAGUA", return_tensors="pt")
>>> output = model(**inputs)
>>> output["probabilities"].shape
torch.Size([1, 30, 3])
Variant Effect
>>> from multimolecule import RnaTokenizer
>>> from multimolecule.models.deltasplice import DeltaSpliceModel
>>> tokenizer = RnaTokenizer.from_pretrained("multimolecule/deltasplice-human")
>>> model = DeltaSpliceModel.from_pretrained("multimolecule/deltasplice-human")
>>> reference = tokenizer("AGCAGUCAUUAUGGCGAAUCUGGCAAGUA", return_tensors="pt")
>>> alternative = tokenizer("AGCAGUCAUUAUGGCUAAUCUGGCAAGUA", return_tensors="pt")
>>> output = model(reference["input_ids"], alternative_input_ids=alternative["input_ids"], use_reference=True)
>>> output["delta"].shape
torch.Size([1, 30, 3])
Interface
- Input: RNA sequence tokenized with
RnaTokenizer;Nis encoded as zero nucleotide channels - Output channels:
no_splice,acceptor,donor - Reference-only call: returns per-position splice-site usage probabilities in
probabilities - Reference + alternative call: pass the reference sequence as
input_idsand the alternate sequence asalternative_input_ids - Reference usage: pass
reference_usagewith shape(batch_size, sequence_length, 3)or omit it to use the model's own reference usage as the reference signal
Training Details
DeltaSplice was trained to predict splice-site usage from gene sequence and to improve mutation-effect prediction by incorporating reference splice-site usage.
Training Data
The upstream repository describes training from gene_dataset.tsu.txt, which contains splice-site usage in adult brains of eight mammalian species.
Training Procedure
The official release provides five seed checkpoints with the same architecture and data split. MultiMolecule represents these seed checkpoints as internal ensemble members rather than public model variants.
Citation
@article{xu2024deltasplice,
title = {Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences},
author = {Xu, Chencheng and Bao, Suying and Wang, Ye and Li, Wenxing and Chen, Hao and Shen, Yufeng and Jiang, Tao and Zhang, Chaolin},
journal = {Genome Research},
volume = {34},
number = {7},
pages = {1052--1065},
year = {2024},
doi = {10.1101/gr.279044.124}
}
The artifacts distributed in this repository are part of the MultiMolecule project. If MultiMolecule supports your research, please cite the MultiMolecule project as follows:
@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 for any questions or comments on the model card.
Please contact the authors of the DeltaSplice paper for questions or comments on the paper/model.
License
This model implementation is licensed under the GNU Affero General Public License.
For additional terms and clarifications, please refer to our License FAQ.
SPDX-License-Identifier: AGPL-3.0-or-later