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m42-health/
BioFM-265M

Name: BioFM-265M
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BioFM: A Biologically-Informed Genomic Foundation Model

BioFM is a cutting-edge genomic foundation model that addresses critical limitations in existing genomic sequence modeling. By introducing BioToken, a novel tokenization framework, BioFM encodes genomic variants and structural annotations with unprecedented biological context, enabling more nuanced and accurate representation learning.

Model Highlights

With the introduction of BioToken, we achieved competitive genomic prediction results using only 265 million parameters, significantly reducing computational requirements and training costs.
Demonstrated superior performance compared to specialized models like Enformer and SpliceTransformer in critical genomic tasks, such as expression prediction and sQTL prediction, respectively.
BioFM excels at various genomic tasks (e.g., expression prediction, coding/non-coding pathogenicity prediction, and sQTL prediction) that require long-range genomic contexts, outperforming existing GFMs.

Model Details

Model developers: M42 Health AI Team
Base architecture: MistralForCausalLM
Context length:
- Training: 6k tokens
- Inference: 12k tokens
Training data: 1000 Genomes
Input format: Annotated DNA sequences using BioToken
Output options:
- DNA sequences only
- Embeddings
License: CC BY-NC 4.0
Publication: Paper link

Model Inference

We developed a BioFM-Eval Python package for inference and embedding extraction from genomic sequences. Refer to BioFM-Eval library for setup and installation instructions.

Creating Variant Embeddings with BioFM

This guide will help you quickly generate BioFM embeddings for the variants in your VCF file. These embeddings are created using the method described in our publication.

python

from biofm_eval import AnnotatedModel, AnnotationTokenizer, Embedder, VCFConverter
import torch

# Define paths to the pre-trained BioFM model and tokenizer
MODEL_PATH = "m42-health/BioFM-265M"
TOKENIZER_PATH = "m42-health/BioFM-265M"

# Load the pre-trained BioFM model and BioToken tokenizer
model = AnnotatedModel.from_pretrained(
    MODEL_PATH,
    torch_dtype=torch.bfloat16,
)
tokenizer = AnnotationTokenizer.from_pretrained(TOKENIZER_PATH)

# Initialize the embedder using the model and tokenizer
embedder = Embedder(model, tokenizer)

# Set up the VCF converter with paths to gene annotations and reference genome
vcf_converter = VCFConverter(
    gene_annotation_path="./gencode.v38.annotation.gff3",
    reference_genome_path="./GCA_000001405.15_GRCh38_no_alt_plus_hs38d1_analysis_set.fna"
)

# Convert a VCF file into an annotated dataset using BioTokens
annotated_dataset = vcf_converter.vcf_to_annotated_dataset(
    vcf_path = './HG01779_b.vcf.gz', 
    max_variants=200 # Set to None to process all variants in the VCF file
)

# Extract BioFM embeddings for all annotated variants
embeddings = embedder.get_dataset_embeddings(annotated_dataset)
print(embeddings)

# Example output (dict):
# {
#     'embeddings': array of shape (num_variants, 2*embedding_dim),  # Numeric embeddings for each variant
#     'labels': array of shape (num_variants,)  # Present only during supervised embedding extraction
# }

Sample reference genome fasta file: download link
Gene annotation file: download_link
Sample vcf file from 1000 Genomes data: download_link

Generation with BioFM

BioFM can generate genomic sequences based on input DNA prompts.

python

from biofm_eval import AnnotatedModel, AnnotationTokenizer, Generator
import torch

# Define paths to the pre-trained BioFM model and tokenizer
MODEL_PATH = "m42-health/BioFM-265M"
TOKENIZER_PATH = "m42-health/BioFM-265M"

# Load the pre-trained BioFM model and BioToken tokenizer
model = AnnotatedModel.from_pretrained(
    MODEL_PATH,
    torch_dtype=torch.bfloat16,
)
tokenizer = AnnotationTokenizer.from_pretrained(TOKENIZER_PATH)

# Initializing the generator using model and tokenizer
seq_generator = Generator(model, tokenizer)

# Generate DNA sequences
input_sequences = ['AGCT', 'GACTGCA']
output = seq_generator.generate(
    input_sequences, 
    max_new_tokens=10, 
    temperature=1.0, 
    do_sample=True, 
    top_k=4)

print(output)

# Example output: List[str] = ['AGCTACTCCCCTCC', 'GACTGCACCACTGTACT']

Training Setup

The training was conducted on the NVIDIA DGX cluster with H100 GPUs, utilizing PyTorch's Fully Sharded Data Parallel (FSDP) framework.

Evaluation Results

To demonstrate the effectiveness of BioToken, we evaluated BioFM against strong supervised baselines: Enformer for gene expression prediction and Splice Transformer for sQTL prediction.

Gene Expression Prediction: BioFM matches Enformer's performance when both models use a 12K context, making it the first-ever GFM to achieve this. Notably, Enformer fails to reach this performance level even with a 98K context.
sQTL Prediction: BioFM significantly outperforms Splice Transformer across all tissues, highlighting its robustness and generalizability.

sQTL prediction	Expression prediction
Alt1	Alt2

We further evaluated BioFM on the Variant Benchmark we curated and the Genomics Long-Range Benchmark.

Variant Benchmark: Across a broad spectrum of variant prediction tasks, BioFM outperforms other GFMs, showcasing its superior predictive capabilities.
Long-Range Genomic Dependencies: On the Genomics Long-Range Benchmark, BioFM sets new performance standards, surpassing previous GFMs that required extensive fine-tuning and longer genomic contexts. This highlights BioFM’s ability to effectively capture and utilize long-range genomic dependencies.

Variant benchmark	Genomics long-range benchmark
Alt1	Alt2

Please go through the paper for more resutls and ablations.

Citation

@article {Medvedev2025.03.27.645711,
	author = {Medvedev, Aleksandr and Viswanathan, Karthik and Kanithi, Praveenkumar and Vishniakov, Kirill and Munjal, Prateek and Christophe, Clement and Pimentel, Marco AF and Rajan, Ronnie and Khan, Shadab},
	title = {BioToken and BioFM - Biologically-Informed Tokenization Enables Accurate and Efficient Genomic Foundation Models},
	elocation-id = {2025.03.27.645711},
	year = {2025},
	doi = {10.1101/2025.03.27.645711},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2025/04/01/2025.03.27.645711},
	eprint = {https://www.biorxiv.org/content/early/2025/04/01/2025.03.27.645711.full.pdf},
	journal = {bioRxiv}
}