dsfsi/simcse-dna

Project Description

This repository contains the trained model for our paper: Fine-tuning a Sentence Transformer for DNA & Protein tasks that is currently under review at BMC Bioinformatics. This model, called simcse-dna; is based on the original implementation of SimCSE [1]. The original model was adapted for DNA downstream tasks by training it on a small sample size k-mer tokens generated from the human reference genome, and can be used to generate sentence embeddings for DNA tasks.

Prerequisites

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Please see the original SimCSE for installation details. The model will als be hosted on Zenodo (DOI: 10.5281/zenodo.11046580).

Usage

Run the following code to get the sentence embeddings:

import torch
from transformers import AutoModel, AutoTokenizer

# Import trained model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("dsfsi/simcse-dna")
model = AutoModel.from_pretrained("dsfsi/simcse-dna")


#sentences is your list of n DNA tokens of size 6 
inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt")

# Get the embeddings
with torch.no_grad():
    embeddings = model(**inputs, output_hidden_states=True, return_dict=True).pooler_output

The retrieved embeddings can be utilized as input for a machine learning classifier to perform classification.

Performance on evaluation tasks

Find out more about the datasets and access in the paper (TBA)

Task 1: Detection of colorectal cancer cases (after oversampling)

	5-fold Cross Validation accuracy	Test accuracy
LightGBM	91	63
Random Forest	94	71
XGBoost	93	66
CNN	42	52

	5-fold Cross Validation F1	Test F1
LightGBM	91	66
Random Forest	94	72
XGBoost	93	66
CNN	41	60

Task 2: Prediction of the Gleason grade group (after oversampling)

	5-fold Cross Validation accuracy	Test accuracy
LightGBM	97	68
Random Forest	98	78
XGBoost	97	70
CNN	35	50

	5-fold Cross Validation F1	Test F1
LightGBM	97	70
Random Forest	98	80
XGBoost	97	70
CNN	33	59

Task 3: Detection of human TATA sequences (after oversampling)

	5-fold Cross Validation accuracy	Test accuracy
LightGBM	98	93
Random Forest	99	96
XGBoost	99	95
CNN	38	59

	5-fold Cross Validation F1	Test F1
LightGBM	98	92
Random Forest	99	95
XGBoost	99	92
CNN	58	10

Authors

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• Mpho Mokoatle, Vukosi Marivate, Darlington Mapiye, Riana Bornman, Vanessa M. Hayes
• Contact details : [email protected]

Citation

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Bibtex Reference TBA

References

[1] Gao, Tianyu, Xingcheng Yao, and Danqi Chen. "Simcse: Simple contrastive learning of sentence embeddings." arXiv preprint arXiv:2104.08821 (2021).