---
tags:
- dna
- human_genome
---

# WARNING

This readme should be changed according to current model. Num steps: 810000

# GENA-LM

GENA-LM is a transformer masked language model trained on human DNA sequence.

Differences between GENA-LM and DNABERT:
- BPE tokenization instead of k-mers;
- input sequence size is about 3000 nucleotides (512 BPE tokens) compared to 510 nucleotides of DNABERT
- pre-training on T2T vs. GRCh38.p13 human genome assembly.

Source code and data: https://github.com/AIRI-Institute/GENA_LM

## Examples
### How to load the model to fine-tune it on classification task
```python
from src.gena_lm.modeling_bert import BertForSequenceClassification
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('AIRI-Institute/gena-lm-bert-base')
model = BertForSequenceClassification.from_pretrained('AIRI-Institute/gena-lm-bert-base')
```

## Model description
GENA-LM model is trained in a masked language model (MLM) fashion, following the methods proposed in the BigBird paper by masking 85% of tokens. Model config for `gena-lm-bert-base` is similar to the bert-base:

- 512 Maximum sequence length
- 12 Layers, 12 Attention heads
- 768 Hidden size
- 32k Vocabulary size

We pre-trained `gena-lm-bert-base` using the latest T2T human genome assembly (https://www.ncbi.nlm.nih.gov/assembly/GCA_009914755.3/). Pre-training was performed for 500,000 iterations with the same parameters as in BigBird, except sequence length was equal to 512 tokens and we used pre-layer normalization in Transformer.

## Downstream tasks
Currently, gena-lm-bert-base model has been finetuned  and tested on promoter prediction task.  Its' performance is comparable to previous SOTA results. We plan to fine-tune and make available models for other downstream tasks in the near future.

### Fine-tuning GENA-LM on our data and scoring
After fine-tuning gena-lm-bert-base on promoter prediction dataset, following results were achieved: 

| model                    | seq_len (bp) | F1    |
|--------------------------|--------------|-------|
| DeePromoter              | 300          | 95.60 |
| GENA-LM bert-base (ours) | 2000         | 95.72 |
| BigBird                  | 16000        | 99.90 |

We can conclude that our model achieves comparable performance to the previously published results for promoter prediction task.