Protein Embeddings for BioBridge-PrimeKG¶
In this tutorial, we will perform protein embeddings for BioBridge-PrimeKG.
First of all, we need to import necessary libraries as follows:
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# Import necessary libraries
import os
import time
import numpy as np
from tqdm import tqdm
import torch
import sys
sys.path.append('../../..')
from aiagents4pharma.talk2knowledgegraphs.datasets.biobridge_primekg import BioBridgePrimeKG
from aiagents4pharma.talk2knowledgegraphs.utils.embeddings.huggingface import EmbeddingWithHuggingFace
# Import necessary libraries
import os
import time
import numpy as np
from tqdm import tqdm
import torch
import sys
sys.path.append('../../..')
from aiagents4pharma.talk2knowledgegraphs.datasets.biobridge_primekg import BioBridgePrimeKG
from aiagents4pharma.talk2knowledgegraphs.utils.embeddings.huggingface import EmbeddingWithHuggingFace
/home/awmulyadi/Repositories/office/AIAgents4Pharma/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html from .autonotebook import tqdm as notebook_tqdm /home/awmulyadi/Repositories/office/AIAgents4Pharma/venv/lib/python3.12/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_id" in SysBioModel has conflict with protected namespace "model_". You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`. warnings.warn( /home/awmulyadi/Repositories/office/AIAgents4Pharma/venv/lib/python3.12/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_id" in BasicoModel has conflict with protected namespace "model_". You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`. warnings.warn( /home/awmulyadi/Repositories/office/AIAgents4Pharma/venv/lib/python3.12/site-packages/pydantic/_internal/_fields.py:132: UserWarning: Field "model_data" in SimulateModelInput has conflict with protected namespace "model_". You may be able to resolve this warning by setting `model_config['protected_namespaces'] = ()`. warnings.warn(
Load BioBridge-PrimeKG¶
The BioBridgePrimeKG allows to load the data from related Github repository if the data is not available locally.
Otherwise, the data is loaded from the local directory as defined in the local_dir and primekg_dir.
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# Define biobridge primekg data by providing a local directory where the data is stored
biobridge_data = BioBridgePrimeKG(primekg_dir="../../../../data/primekg/",
local_dir="../../../../data/biobridge_primekg/")
# Define biobridge primekg data by providing a local directory where the data is stored
biobridge_data = BioBridgePrimeKG(primekg_dir="../../../../data/primekg/",
local_dir="../../../../data/biobridge_primekg/")
To load the dataframes of BioBridge and its split, we just need to call a method as follows.
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# Invoke a method to load the data
biobridge_data.load_data()
# Invoke a method to load the data
biobridge_data.load_data()
Loading PrimeKG dataset... Loading nodes of PrimeKG dataset ... ../../../../data/primekg/primekg_nodes.tsv.gz already exists. Loading the data from the local directory. Loading edges of PrimeKG dataset ... ../../../../data/primekg/primekg_edges.tsv.gz already exists. Loading the data from the local directory. Loading data config file of BioBridgePrimeKG... File data_config.json already exists in ../../../../data/biobridge_primekg/. Building node embeddings... Building full triplets... Building train-test split...
BioBridge has provided us with node information for each modalities of PrimeKG.
In particular, they included the information for the following modalities:
- protein (sequence)
- molecular function (texts)
- cellular component (texts)
- biological process (texts)
- drug (SMILES strings)
- disease (texts)
In this tutorial, we just focus our embeddings on the protein sequence.
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# Get the node information of the BioBridge PrimeKG
biobridge_node_info = biobridge_data.get_node_info_dict()
biobridge_node_info.keys()
# Get the node information of the BioBridge PrimeKG
biobridge_node_info = biobridge_data.get_node_info_dict()
biobridge_node_info.keys()
Out[4]:
dict_keys(['gene/protein', 'molecular_function', 'cellular_component', 'biological_process', 'drug', 'disease'])
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# Check a sample node information of gene/protein
biobridge_protein_df = biobridge_node_info['gene/protein']
biobridge_protein_df.head()
# Check a sample node information of gene/protein
biobridge_protein_df = biobridge_node_info['gene/protein']
biobridge_protein_df.head()
Out[5]:
| node_index | node_id | node_type | node_name | node_source | sequence | |
|---|---|---|---|---|---|---|
| 0 | 0 | 9796 | gene/protein | PHYHIP | NCBI | MELLSTPHSIEINNITCDSFRISWAMEDSDLERVTHYFIDLNKKEN... |
| 1 | 1 | 7918 | gene/protein | GPANK1 | NCBI | MSRPLLITFTPATDPSDLWKDGQQQPQPEKPESTLDGAAARAFYEA... |
| 2 | 2 | 8233 | gene/protein | ZRSR2 | NCBI | MAAPEKMTFPEKPSHKKYRAALKKEKRKKRRQELARLRDSGLSQKE... |
| 3 | 3 | 4899 | gene/protein | NRF1 | NCBI | MEEHGVTQTEHMATIEAHAVAQQVQQVHVATYTEHSMLSADEDSPS... |
| 4 | 4 | 5297 | gene/protein | PI4KA | NCBI | MAAAPARGGGGGGGGGGGCSGSGSSASRGFYFNTVLSLARSLAVQR... |
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# Check dataframe records
biobridge_protein_df.shape
# Check dataframe records
biobridge_protein_df.shape
Out[6]:
(19162, 6)
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# Checking the distribution of protein sequence lengths
prot_seq_len = np.array(biobridge_protein_df['sequence'].str.len().tolist())
prot_seq_len.mean(), prot_seq_len.std(), prot_seq_len.max(), prot_seq_len.min()
# Checking the distribution of protein sequence lengths
prot_seq_len = np.array(biobridge_protein_df['sequence'].str.len().tolist())
prot_seq_len.mean(), prot_seq_len.std(), prot_seq_len.max(), prot_seq_len.min()
Out[37]:
(576.2223671850537, 605.2005749412913, 34350, 24)
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# To make sure that the embedding models can perform the task properly (i.e., depends on GPU memory availability)
# We can filter the data to only embed the protein sequence length less than 2048
biobridge_protein_df_filtered = biobridge_protein_df[biobridge_protein_df['sequence'].str.len() < 2048]
biobridge_protein_df_filtered.shape
# To make sure that the embedding models can perform the task properly (i.e., depends on GPU memory availability)
# We can filter the data to only embed the protein sequence length less than 2048
biobridge_protein_df_filtered = biobridge_protein_df[biobridge_protein_df['sequence'].str.len() < 2048]
biobridge_protein_df_filtered.shape
Out[38]:
(18744, 6)
Protein Embedding¶
We can perform protein embedding using the above dataframe by using the EmbeddingWithHuggingFace class.
Here we will use facebook/esm2_t6_8M_UR50D as the smallest model of ESM2 protein modeling.
Note that facebook/esm2_t6_8M_UR50D produces 320-dimensional embeddings.
For more detailed information about this model, please refer to:
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# Check device availability
device = "cuda:0" if torch.cuda.is_available() else "cpu"
device
# Check device availability
device = "cuda:0" if torch.cuda.is_available() else "cpu"
device
Out[7]:
'cuda:0'
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# Using facebook/esm2_t6_8M_UR50D
emb_model = EmbeddingWithHuggingFace(model_name='facebook/esm2_t6_8M_UR50D',
model_cache_dir="../../../../data/facebook/esm2_t6_8M_UR50D/",
truncation=False,
device=device)
# Using facebook/esm2_t6_8M_UR50D
emb_model = EmbeddingWithHuggingFace(model_name='facebook/esm2_t6_8M_UR50D',
model_cache_dir="../../../../data/facebook/esm2_t6_8M_UR50D/",
truncation=False,
device=device)
Some weights of EsmModel were not initialized from the model checkpoint at facebook/esm2_t6_8M_UR50D and are newly initialized: ['esm.pooler.dense.bias', 'esm.pooler.dense.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
We can perform embeddings over all records in the dataframe as follows:
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# Embeddings using one sample at a time
mini_batch_size = 3
protein_embeddings = []
for i in tqdm(range(0, biobridge_protein_df_filtered.shape[0], mini_batch_size)):
outputs = emb_model.embed_documents(biobridge_protein_df_filtered.sequence.values.tolist()[i:i+mini_batch_size])
protein_embeddings.extend(outputs)
torch.cuda.synchronize()
torch.cuda.empty_cache()
protein_embeddings
# Embeddings using one sample at a time
mini_batch_size = 3
protein_embeddings = []
for i in tqdm(range(0, biobridge_protein_df_filtered.shape[0], mini_batch_size)):
outputs = emb_model.embed_documents(biobridge_protein_df_filtered.sequence.values.tolist()[i:i+mini_batch_size])
protein_embeddings.extend(outputs)
torch.cuda.synchronize()
torch.cuda.empty_cache()
protein_embeddings
100%|██████████| 6248/6248 [04:38<00:00, 22.43it/s]
Out[31]:
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...]
In [39]:
Copied!
# Check embedding dimension
len(protein_embeddings)
# Check embedding dimension
len(protein_embeddings)
Out[39]:
18744
In [52]:
Copied!
# Add them as features to the dataframe
biobridge_protein_df_filtered['sequence_embedded'] = np.array(protein_embeddings).tolist()
# Check dataframe of nodes
biobridge_protein_df_filtered.head()
# Add them as features to the dataframe
biobridge_protein_df_filtered['sequence_embedded'] = np.array(protein_embeddings).tolist()
# Check dataframe of nodes
biobridge_protein_df_filtered.head()
Out[52]:
| node_index | node_id | node_type | node_name | node_source | sequence | sequence_embedded | |
|---|---|---|---|---|---|---|---|
| 0 | 0 | 9796 | gene/protein | PHYHIP | NCBI | MELLSTPHSIEINNITCDSFRISWAMEDSDLERVTHYFIDLNKKEN... | [-0.0907055139541626, -0.07978754490613937, 0.... |
| 1 | 1 | 7918 | gene/protein | GPANK1 | NCBI | MSRPLLITFTPATDPSDLWKDGQQQPQPEKPESTLDGAAARAFYEA... | [-0.11766866594552994, -0.18336597084999084, -... |
| 2 | 2 | 8233 | gene/protein | ZRSR2 | NCBI | MAAPEKMTFPEKPSHKKYRAALKKEKRKKRRQELARLRDSGLSQKE... | [-0.24953687191009521, -0.10010954737663269, 0... |
| 3 | 3 | 4899 | gene/protein | NRF1 | NCBI | MEEHGVTQTEHMATIEAHAVAQQVQQVHVATYTEHSMLSADEDSPS... | [0.008047989569604397, 0.08891301602125168, 0.... |
| 5 | 5 | 6564 | gene/protein | SLC15A1 | NCBI | MGMSKSHSFFGYPLSIFFIVVNEFCERFSYYGMRAILILYFTNFIS... | [0.06465577334165573, 0.12712723016738892, 0.2... |
In [56]:
Copied!
# Save the embedded dataframes to parquet file
biobridge_protein_df_filtered.to_parquet(os.path.join(biobridge_data.local_dir, 'biobridgeprimekg_filtered_sequence_embedded.parquet'), compression='gzip', index=False)
# Save the embedded dataframes to parquet file
biobridge_protein_df_filtered.to_parquet(os.path.join(biobridge_data.local_dir, 'biobridgeprimekg_filtered_sequence_embedded.parquet'), compression='gzip', index=False)