BioBridge-PrimeKG Loader¶

In this tutorial, we will explain how to load BioBridge-PrimeKG dataset, which is a dataset for cross-modality prediction using PrimeKG as the knowledge graph.

Prior information about the BioBridge-PrimeKG dataaset can be found in the following repositories:

• https://github.com/RyanWangZf/BioBridge
• https://arxiv.org/pdf/2310.03320

First of all, we need to import the necessary libraries as follows.

In [1]:

# Import necessary libraries
import sys
sys.path.append('../../..')
from aiagents4pharma.talk2knowledgegraphs.datasets.biobridge_primekg import BioBridgePrimeKG

# Import necessary libraries import sys sys.path.append('../../..') from aiagents4pharma.talk2knowledgegraphs.datasets.biobridge_primekg import BioBridgePrimeKG

/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.

In [2]:

# 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.

In [3]:

# 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...
Downloading data_config.json from https://raw.githubusercontent.com/RyanWangZf/BioBridge/refs/heads/main/data/BindData/data_config.json to ../../../../data/biobridge_primekg/...

1.03kiB [00:00, 3.78MiB/s]                

Building node embeddings...
Downloading protein.pkl from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/embeddings/esm2b_unimo_pubmedbert/protein.pkl to ../../../../data/biobridge_primekg/embeddings...

100%|██████████| 197M/197M [00:13<00:00, 14.7MiB/s] 

Downloading mf.pkl from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/embeddings/esm2b_unimo_pubmedbert/mf.pkl to ../../../../data/biobridge_primekg/embeddings...

100%|██████████| 34.3M/34.3M [00:02<00:00, 12.4MiB/s]

Downloading cc.pkl from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/embeddings/esm2b_unimo_pubmedbert/cc.pkl to ../../../../data/biobridge_primekg/embeddings...

100%|██████████| 12.5M/12.5M [00:01<00:00, 7.26MiB/s]

Downloading bp.pkl from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/embeddings/esm2b_unimo_pubmedbert/bp.pkl to ../../../../data/biobridge_primekg/embeddings...

100%|██████████| 85.9M/85.9M [00:10<00:00, 8.09MiB/s]

Downloading drug.pkl from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/embeddings/esm2b_unimo_pubmedbert/drug.pkl to ../../../../data/biobridge_primekg/embeddings...

100%|██████████| 28.7M/28.7M [00:04<00:00, 6.86MiB/s]

Downloading disease.pkl from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/embeddings/esm2b_unimo_pubmedbert/disease.pkl to ../../../../data/biobridge_primekg/embeddings...

100%|██████████| 53.1M/53.1M [00:02<00:00, 20.2MiB/s]

Building full triplets...
Downloading protein.csv from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/Processed/protein.csv to ../../../../data/biobridge_primekg/processed...

100%|██████████| 11.7M/11.7M [00:00<00:00, 17.2MiB/s]

Downloading mf.csv from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/Processed/molecular.csv to ../../../../data/biobridge_primekg/processed...

100%|██████████| 2.24M/2.24M [00:00<00:00, 13.9MiB/s]

Downloading cc.csv from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/Processed/cellular.csv to ../../../../data/biobridge_primekg/processed...

100%|██████████| 1.05M/1.05M [00:00<00:00, 4.17MiB/s]

Downloading bp.csv from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/Processed/biological.csv to ../../../../data/biobridge_primekg/processed...

100%|██████████| 6.79M/6.79M [00:00<00:00, 38.3MiB/s]

Downloading drug.csv from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/Processed/drug.csv to ../../../../data/biobridge_primekg/processed...

100%|██████████| 9.47M/9.47M [00:00<00:00, 15.6MiB/s]

Downloading disease.csv from https://media.githubusercontent.com/media/RyanWangZf/BioBridge/refs/heads/main/data/Processed/disease.csv to ../../../../data/biobridge_primekg/processed...

100%|██████████| 11.4M/11.4M [00:01<00:00, 7.92MiB/s]

Building train-test split...
Number of 1 nodes in train: 16918
Number of 1 nodes in test: 1879
Number of 6 nodes in train: 6084
Number of 6 nodes in test: 675
Number of 2 nodes in train: 15349
Number of 2 nodes in test: 1705
Number of 0 nodes in train: 24669
Number of 0 nodes in test: 2740
Number of 5 nodes in train: 9856
Number of 5 nodes in test: 1095
Number of 7 nodes in train: 3610
Number of 7 nodes in test: 401

As a result, we obtained several processed files in the local directory as explained in the subsequent sections.

Check initial PrimeKG Dataframes¶

Firstly, we can get the initial PrimeKG data by invoking the method get_primekg() and further check the dataframes of nodes and edges.

In [4]:

# Get the initial data of PrimeKG
primekg_nodes = biobridge_data.get_primekg().get_nodes()
primekg_edges = biobridge_data.get_primekg().get_edges()

# Get the initial data of PrimeKG primekg_nodes = biobridge_data.get_primekg().get_nodes() primekg_edges = biobridge_data.get_primekg().get_edges()

In [5]:

# Check PrimeKG nodes
primekg_nodes.head()

# Check PrimeKG nodes primekg_nodes.head()

Out[5]:

	node_index	node_name	node_source	node_id	node_type
0	0	PHYHIP	NCBI	9796	gene/protein
1	1	GPANK1	NCBI	7918	gene/protein
2	2	ZRSR2	NCBI	8233	gene/protein
3	3	NRF1	NCBI	4899	gene/protein
4	4	PI4KA	NCBI	5297	gene/protein

In [6]:

# Check the dimensions of the PrimeKG nodes
primekg_nodes.shape

# Check the dimensions of the PrimeKG nodes primekg_nodes.shape

Out[6]:

(129375, 5)

In [7]:

# Check PrimeKG edges
primekg_edges.head()

# Check PrimeKG edges primekg_edges.head()

Out[7]:

	head_index	head_name	head_source	head_id	head_type	tail_index	tail_name	tail_source	tail_id	tail_type	display_relation	relation
0	0	PHYHIP	NCBI	9796	gene/protein	8889	KIF15	NCBI	56992	gene/protein	ppi	protein_protein
1	1	GPANK1	NCBI	7918	gene/protein	2798	PNMA1	NCBI	9240	gene/protein	ppi	protein_protein
2	2	ZRSR2	NCBI	8233	gene/protein	5646	TTC33	NCBI	23548	gene/protein	ppi	protein_protein
3	3	NRF1	NCBI	4899	gene/protein	11592	MAN1B1	NCBI	11253	gene/protein	ppi	protein_protein
4	4	PI4KA	NCBI	5297	gene/protein	2122	RGS20	NCBI	8601	gene/protein	ppi	protein_protein

In [8]:

# Check the dimensions of the PrimeKG edges
primekg_edges.shape

# Check the dimensions of the PrimeKG edges primekg_edges.shape

Out[8]:

(8100498, 12)

Check BioBridge-PrimeKG Data Config¶

BioBridgePrimeKG class provides a method to load the dataconfig file which contains the following information:

• node_type : The node type mapping
• relation_type : The relation type mapping
• emb_dim : The embedding dimension (pre-loaded embeddings from the repository)

In [9]:

biobridge_data.get_data_config().keys()

biobridge_data.get_data_config().keys()

Out[9]:

dict_keys(['node_type', 'relation_type', 'emb_dim'])

In [10]:

# Check the node type within data config of the BioBridge PrimeKG
biobridge_data.get_data_config()['node_type']

# Check the node type within data config of the BioBridge PrimeKG biobridge_data.get_data_config()['node_type']

Out[10]:

{'biological_process': 0,
 'gene/protein': 1,
 'disease': 2,
 'effect/phenotype': 3,
 'anatomy': 4,
 'molecular_function': 5,
 'drug': 6,
 'cellular_component': 7,
 'pathway': 8,
 'exposure': 9}

In [11]:

# Check the relation type within data config of the BioBridge PrimeKG
biobridge_data.get_data_config()['relation_type']

# Check the relation type within data config of the BioBridge PrimeKG biobridge_data.get_data_config()['relation_type']

Out[11]:

{'expression present': 0,
 'synergistic interaction': 1,
 'interacts with': 2,
 'ppi': 3,
 'phenotype present': 4,
 'parent-child': 5,
 'associated with': 6,
 'side effect': 7,
 'contraindication': 8,
 'expression absent': 9,
 'target': 10,
 'indication': 11,
 'enzyme': 12,
 'transporter': 13,
 'off-label use': 14,
 'linked to': 15,
 'phenotype absent': 16,
 'carrier': 17}

In [12]:

# Check the embedding dimension within data config of the BioBridge PrimeKG
# Note that, not all of node types have embeddings
biobridge_data.get_data_config()['emb_dim']

# Check the embedding dimension within data config of the BioBridge PrimeKG # Note that, not all of node types have embeddings biobridge_data.get_data_config()['emb_dim']

Out[12]:

{'biological_process': 768,
 'cellular_component': 768,
 'disease': 768,
 'drug': 512,
 'molecular_function': 768,
 'gene/protein': 2560}

Check BioBridge-PrimeKG Node Information¶

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 [13]:

# 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[13]:

dict_keys(['gene/protein', 'molecular_function', 'cellular_component', 'biological_process', 'drug', 'disease'])

In [14]:

# Check a sample node information of gene/protein
biobridge_node_info['gene/protein']

# Check a sample node information of gene/protein biobridge_node_info['gene/protein']

Out[14]:

	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...
...	...	...	...	...	...	...
19157	83735	100133251	gene/protein	PRR23D2	NCBI	MYGYRRLRSPRDSQTEPQNDNEGETSLATTQMNPPKRRQVEQGPST...
19158	83735	100133251	gene/protein	PRR23D2	NCBI	MYGYRRLRSPRDSQTEPQNDNEGETSLATTQMNPPKRRQVEQGPST...
19159	83740	389649	gene/protein	C8orf86	NCBI	MRPLGKGLLPAEELIRSNLGVGRSLRDCLSQSGKLAEELGSKRLKP...
19160	83746	343990	gene/protein	CRACDL	NCBI	MISTRVMDIKLREAAEGLGEDSTGKKKSKFKTFKKFFGKKKRKESP...
19161	83747	340441	gene/protein	POTEA	NCBI	MVAEVSPKLAASPMKKPFGFRGKMGKWCCCCFPCCRGSGKNNMGAW...

19162 rows × 6 columns

In [15]:

# Check a sample node information of molecular_function
biobridge_node_info['molecular_function']

# Check a sample node information of molecular_function biobridge_node_info['molecular_function']

Out[15]:

	node_index	node_id	node_type	node_name	node_source	description
0	53517	8168	molecular_function	methyltransferase activity	GO	Catalysis of the transfer of a methyl group to...
1	53518	140101	molecular_function	catalytic activity, acting on a tRNA	GO	Catalytic activity that acts to modify a tRNA.
2	53519	140097	molecular_function	catalytic activity, acting on DNA	GO	Catalytic activity that acts to modify DNA.
3	53520	140096	molecular_function	catalytic activity, acting on a protein	GO	Catalytic activity that acts to modify a protein.
4	53521	140098	molecular_function	catalytic activity, acting on RNA	GO	Catalytic activity that acts to modify RNA, dr...
...	...	...	...	...	...	...
10961	124216	42880	molecular_function	D-glucuronate transmembrane transporter activity	GO	Enables the transfer of D-glucuronate, the D-e...
10962	124217	61922	molecular_function	histone propionyltransferase activity	GO	Catalysis of the reaction: propionyl-CoA + his...
10963	124218	61995	molecular_function	ATP-dependent protein-DNA complex displacement...	GO	An activity that displaces proteins or protein...
10964	124219	51266	molecular_function	sirohydrochlorin ferrochelatase activity	GO	Catalysis of the reaction: siroheme + 2 H+ = F...
10965	124220	51740	molecular_function	ethylene binding	GO	Binding to ethylene (C2-H4, ethene), a simple ...

10966 rows × 6 columns

In [16]:

# Check a sample node information of cellular_component
biobridge_node_info['cellular_component']

# Check a sample node information of cellular_component biobridge_node_info['cellular_component']

Out[16]:

	node_index	node_id	node_type	node_name	node_source	description
0	55515	110165	cellular_component	cellular anatomical entity	GO	A part of a cellular organism that is either a...
1	55516	30137	cellular_component	COPI-coated vesicle	GO	A vesicle with a coat formed of the COPI coat ...
2	55517	30133	cellular_component	transport vesicle	GO	Any of the vesicles of the constitutive secret...
3	55520	5777	cellular_component	peroxisome	GO	A small organelle enclosed by a single membran...
4	55524	99512	cellular_component	supramolecular fiber	GO	A polymer consisting of an indefinite number o...
...	...	...	...	...	...	...
4008	127430	44169	cellular_component	host cell rough endoplasmic reticulum membrane	GO	The lipid bilayer surrounding the host cell ro...
4009	127431	98652	cellular_component	collagen type VII anchoring fibril	GO	An antiparallel dimer of two collagen VII trim...
4010	127432	90732	cellular_component	cofilin-actin rod	GO	A cellular structure consisting of parallel, h...
4011	127433	779	cellular_component	condensed chromosome, centromeric region	GO	The region of a condensed chromosome that incl...
4012	127434	32282	cellular_component	plastid acetyl-CoA carboxylase complex	GO	An acetyl-CoA carboxylase complex located in t...

4013 rows × 6 columns

In [17]:

# Check a sample node information of biological_process
biobridge_node_info['biological_process']

# Check a sample node information of biological_process biobridge_node_info['biological_process']

Out[17]:

	node_index	node_id	node_type	node_name	node_source	description
0	39898	51581	biological_process	negative regulation of neurotransmitter uptake	GO	Any process that stops, prevents, or reduces t...
1	39899	43271	biological_process	negative regulation of ion transport	GO	Any process that stops, prevents, or reduces t...
2	39900	51611	biological_process	regulation of serotonin uptake	GO	Any process that modulates the frequency, rate...
3	39901	51616	biological_process	regulation of histamine uptake	GO	Any process that modulates the frequency, rate...
4	39902	51956	biological_process	negative regulation of amino acid transport	GO	Any process that stops, prevents, or reduces t...
...	...	...	...	...	...	...
27473	115046	60654	biological_process	mammary gland cord elongation	GO	The process in which the mammary gland sprout ...
27474	115047	1903696	biological_process	protein localization to horsetail-astral micro...	GO	A process in which a protein is transported to...
27475	115048	3372	biological_process	establishment or maintenance of cytoskeleton p...	GO	Any cellular process that results in the speci...
27476	115049	97624	biological_process	UDP-galactose transmembrane import into Golgi ...	GO	The directed movement of UDP-galactose into th...
27477	115050	48072	biological_process	compound eye pigmentation	GO	Establishment of a pattern of pigment in the c...

27478 rows × 6 columns

In [18]:

# Check a sample node information of drug
biobridge_node_info['drug']

# Check a sample node information of drug biobridge_node_info['drug']

Out[18]:

	node_index	description	half_life	indication	mechanism_of_action	protein_binding	pharmacodynamics	state	atc_1	atc_2	...	node_type	node_name	node_source	name	smiles	logP ALOGPS	logP ChemAxon	solubility ALOGPS	pKa (strongest acidic)	pKa (strongest basic)
0	14014	Flunisolide (marketed as AeroBid, Nasalide, Na...	The half-life is 1.8 hours	For the maintenance treatment of asthma as a p...	Flunisolide is a glucocorticoid receptor agoni...	Approximately 40% after oral inhalation	Flunisolide is a synthetic corticosteroid. It ...	Flunisolide is a solid.	Flunisolide is anatomically related to respira...	Flunisolide is in the therapeutic group of nas...	...	drug	Flunisolide	DrugBank	Flunisolide	[H][C@@]12C[C@@]3([H])[C@]4([H])C[C@H](F)C5=CC...	2.20	1.56	3.74e-02 g/l	13.73	-2.90
1	14015	Alclometasone is synthetic glucocorticoid ster...	NaN	For the relief of the inflammatory and pruriti...	The mechanism of the anti-inflammatory activit...	NaN	Alclometasone is a synthetic corticosteroid fo...	Alclometasone is a solid.	Alclometasone is anatomically related to derma...	Alclometasone is in the therapeutic group of c...	...	drug	Alclometasone	DrugBank	Alclometasone	[H][C@@]12C[C@@H](C)[C@](O)(C(=O)CO)[C@@]1(C)C...	2.11	1.68	1.37e-01 g/l	12.45	-2.90
2	14016	Medrysone is a corticosteroid used in ophthalm...	NaN	For the treatment of allergic conjunctivitis, ...	There is no generally accepted explanation for...	NaN	Medrysone is a topical anti-inflammatory corti...	Medrysone is a solid.	Medrysone is anatomically related to sensory o...	Medrysone is in the therapeutic group of ophth...	...	drug	Medrysone	DrugBank	Medrysone	[H][C@@]12CC[C@H](C(C)=O)[C@@]1(C)C[C@H](O)[C@...	3.06	3.13	3.37e-02 g/l	19.14	-0.26
3	14017	A glucocorticoid employed, usually as eye drop...	NaN	For the ophthalmic treatment of corticosteroid...	There is no generally accepted explanation for...	NaN	Corticosteroids such as fluorometholone inhibi...	Fluorometholone is a solid.	Fluorometholone is anatomically related to der...	Fluorometholone is in the therapeutic group of...	...	drug	Fluorometholone	DrugBank	Fluorometholone	[H][C@@]12CC[C@](O)(C(C)=O)[C@@]1(C)C[C@H](O)[...	2.34	2.42	1.66e-02 g/l	12.65	-3.40
4	14018	Beclomethasone dipropionate is a second-genera...	Following intravenous administration, the half...	Indicated for oral inhalation use in the maint...	Beclomethasone dipropionate is a corticosteroi...	Based on the findings of _in vitro_ studies, t...	Inflammatory conditions, including asthma, der...	Beclomethasone dipropionate is a solid.	Beclomethasone dipropionate is anatomically re...	Beclomethasone dipropionate is in the therapeu...	...	drug	Beclomethasone dipropionate	DrugBank	Beclomethasone dipropionate	[H][C@@]12C[C@H](C)[C@](OC(=O)CC)(C(=O)COC(=O)...	3.69	4.43	2.08e-03 g/l	13.85	-3.30
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6943	21955	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Metabutethamine is anatomically related to ner...	Metabutethamine is in the therapeutic group of...	...	drug	Metabutethamine	DrugBank	NaN	CC(C)CNCCOC(=O)C1=CC=CC(N)=C1	NaN	NaN	NaN	NaN	NaN
6944	21956	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Quinisocaine is anatomically related to dermat...	Quinisocaine is in the therapeutic group of an...	...	drug	Quinisocaine	DrugBank	NaN	CCCCC1=CC2=CC=CC=C2C(OCCN(C)C)=N1	NaN	NaN	NaN	NaN	NaN
6945	39888	Benzyl benzoate is one of the older preparatio...	NaN	Used to kill lice and the mites responsible fo...	Benzyl benzoate exerts toxic effects on the ne...	NaN	Benzyl benzoate is one of the older preparatio...	Benzyl benzoate is a liquid.	Benzyl benzoate is anatomically related to ant...	Benzyl benzoate is in the therapeutic group of...	...	drug	Benzyl benzoate	DrugBank	NaN	O=C(OCC1=CC=CC=C1)C1=CC=CC=C1	NaN	NaN	NaN	NaN	NaN
6946	39894	Sulfur hexafluoride is an ultrasound contrast ...	The terminal half-life of SF6 in blood was app...	Echocardiography: Sulfur hexafluoride is indic...	Within the blood, the acoustic impedance of Lu...	NaN	Sulfur hexafluoride provides useful echocardio...	NaN	Sulfur hexafluoride is anatomically related to...	Sulfur hexafluoride is in the therapeutic grou...	...	drug	Sulfur hexafluoride	DrugBank	NaN	FS(F)(F)(F)(F)F	NaN	NaN	NaN	NaN	NaN
6947	39895	Butoconazole is an imidazole antifungal used i...	NaN	For the local treatment of vulvovaginal candid...	The exact mechanism of the antifungal action o...	NaN	Butoconazole is an imidazole derivative that h...	Butoconazole is a solid.	Butoconazole is anatomically related to genito...	Butoconazole is in the therapeutic group of gy...	...	drug	Butoconazole	DrugBank	NaN	ClC1=CC=C(CCC(CN2C=CN=C2)SC2=C(Cl)C=CC=C2Cl)C=C1	NaN	NaN	NaN	NaN	NaN

6948 rows × 29 columns

In [19]:

# Check a sample node information of disease
biobridge_node_info['disease']

# Check a sample node information of disease biobridge_node_info['disease']

Out[19]:

	node_index	mondo_id	mondo_name	definition
0	27165	8019	mullerian aplasia and hyperandrogenism	Deficiency of the glycoprotein WNT4, associate...
1	27165	8019	mullerian aplasia and hyperandrogenism	Deficiency of the glycoprotein WNT4, associate...
2	27166	11043	myelodysplasia, immunodeficiency, facial dysmo...	NaN
3	27168	8878	bone dysplasia, lethal Holmgren type	Bone dysplasia lethal Holmgren type (BDLH) is ...
4	27169	8905	predisposition to invasive fungal disease due ...	A rare, genetic primary immunodeficiency chara...
...	...	...	...	...
44128	99866	44144	panic disorder with agoraphobia	A disorder in which an individual experiences ...
44129	99916	44797	desmoplastic nevus	A benign melanocytic nevus characterized by th...
44130	99916	44800	desmoplastic spitz nevus	A Spitz nevus associated with fibrous stroma f...
44131	99969	100023	self-limited familial and non-familial neonata...	A neonatal/infantile epilepsy sndrome that is ...
44132	99969	100024	self-limited familial and non-familial infanti...	This syndrome is characterized by the onset of...

44133 rows × 4 columns

Check BioBridge-PrimeKG Node Embeddings (Pre-Loaded)¶

BioBridge provides a dictionary of pre-loaded embeddings, which can be obtained as follows.

In [20]:

# Check node embeddings
emb_dict = biobridge_data.get_node_embeddings()
emb_dict[0]

# Check node embeddings emb_dict = biobridge_data.get_node_embeddings() emb_dict[0]

Out[20]:

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In [21]:

len(emb_dict)

len(emb_dict)

Out[21]:

85466

In [22]:

# Check embedding dimension
len(emb_dict[0])

# Check embedding dimension len(emb_dict[0])

Out[22]:

2560

In [23]:

# Cross-check the node embeddings with the node types
primekg_nodes[primekg_nodes.node_index == 0]

# Cross-check the node embeddings with the node types primekg_nodes[primekg_nodes.node_index == 0]

Out[23]:

	node_index	node_name	node_source	node_id	node_type
0	0	PHYHIP	NCBI	9796	gene/protein

Check Triplet Splits of BioBridge-PrimeKG¶

Lastly, BioBridge-PrimeKG splits contain train and test dataframes of triplets and nodes.

In [24]:

# Get all triplets of BioBridge PrimeKG
triplets = biobridge_data.get_primekg_triplets()
triplets.head()

# Get all triplets of BioBridge PrimeKG triplets = biobridge_data.get_primekg_triplets() triplets.head()

Out[24]:

	head_index	head_name	head_source	head_id	head_type	tail_index	tail_name	tail_source	tail_id	tail_type	display_relation	relation
0	0	PHYHIP	NCBI	9796	1	8889	KIF15	NCBI	56992	1	3	protein_protein
1	1	GPANK1	NCBI	7918	1	2798	PNMA1	NCBI	9240	1	3	protein_protein
2	2	ZRSR2	NCBI	8233	1	5646	TTC33	NCBI	23548	1	3	protein_protein
3	3	NRF1	NCBI	4899	1	11592	MAN1B1	NCBI	11253	1	3	protein_protein
4	4	PI4KA	NCBI	5297	1	2122	RGS20	NCBI	8601	1	3	protein_protein

In [25]:

triplets

triplets

Out[25]:

	head_index	head_name	head_source	head_id	head_type	tail_index	tail_name	tail_source	tail_id	tail_type	display_relation	relation
0	0	PHYHIP	NCBI	9796	1	8889	KIF15	NCBI	56992	1	3	protein_protein
1	1	GPANK1	NCBI	7918	1	2798	PNMA1	NCBI	9240	1	3	protein_protein
2	2	ZRSR2	NCBI	8233	1	5646	TTC33	NCBI	23548	1	3	protein_protein
3	3	NRF1	NCBI	4899	1	11592	MAN1B1	NCBI	11253	1	3	protein_protein
4	4	PI4KA	NCBI	5297	1	2122	RGS20	NCBI	8601	1	3	protein_protein
...	...	...	...	...	...	...	...	...	...	...	...	...
3904605	52855	B cell receptor transport into membrane raft	GO	32597	0	34572	CD24	NCBI	100133941	1	2	bioprocess_protein
3904606	113352	chemokine receptor transport out of membrane raft	GO	32600	0	34572	CD24	NCBI	100133941	1	2	bioprocess_protein
3904607	42264	negative regulation of cytoskeleton organization	GO	51494	0	57675	IQCJ-SCHIP1	NCBI	100505385	1	2	bioprocess_protein
3904608	109904	mesendoderm migration	GO	90133	0	58770	APELA	NCBI	100506013	1	2	bioprocess_protein
3904609	44810	regulation of endoplasmic reticulum unfolded p...	GO	1900101	0	57692	PIGBOS1	NCBI	101928527	1	2	bioprocess_protein

3904610 rows × 12 columns

Finally, we can check the number of each split as follows.

In [26]:

# Check the number of samples in each split of the biobridge primekg dataframes
biobridge_splits = biobridge_data.get_train_test_split()

# Check the number of samples in each split of the biobridge primekg dataframes biobridge_splits = biobridge_data.get_train_test_split()

In [27]:

list(biobridge_splits.keys())

list(biobridge_splits.keys())

Out[27]:

['train', 'node_train', 'test', 'node_test']

In [28]:

# Check dataframe of training triples
biobridge_splits["train"]

# Check dataframe of training triples biobridge_splits["train"]

Out[28]:

	head_index	head_name	head_source	head_id	head_type	tail_index	tail_name	tail_source	tail_id	tail_type	display_relation	relation
0	0	PHYHIP	NCBI	9796	1	8889	KIF15	NCBI	56992	1	3	protein_protein
1	1	GPANK1	NCBI	7918	1	2798	PNMA1	NCBI	9240	1	3	protein_protein
2	2	ZRSR2	NCBI	8233	1	5646	TTC33	NCBI	23548	1	3	protein_protein
3	3	NRF1	NCBI	4899	1	11592	MAN1B1	NCBI	11253	1	3	protein_protein
4	4	PI4KA	NCBI	5297	1	2122	RGS20	NCBI	8601	1	3	protein_protein
...	...	...	...	...	...	...	...	...	...	...	...	...
3768281	124473	longitudinal sarcoplasmic reticulum	GO	14801	7	58744	DHRS7C	NCBI	201140	1	2	cellcomp_protein
3768282	55747	myofilament	GO	36379	7	57367	MYBPHL	NCBI	343263	1	2	cellcomp_protein
3768285	126945	lateral wall of outer hair cell	GO	120249	7	22033	SLC26A5	NCBI	375611	1	2	cellcomp_protein
3768286	125456	Swi5-Swi2 complex	GO	34974	7	57415	SWI5	NCBI	375757	1	2	cellcomp_protein
3768287	55667	SUMO ligase complex	GO	106068	7	35398	SUMO4	NCBI	387082	1	2	cellcomp_protein

3510930 rows × 12 columns

In [29]:

# Check dataframe of training nodes
biobridge_splits["node_train"]

# Check dataframe of training nodes biobridge_splits["node_train"]

Out[29]:

	node_index	node_type
0	0	1
1	1	1
2	2	1
3	3	1
4	4	1
...	...	...
76481	127431	7
76482	127432	7
76483	127433	7
76484	127239	7
76485	127316	7

76486 rows × 2 columns

In [30]:

# Check dataframe of testing triples
biobridge_splits["test"]

# Check dataframe of testing triples biobridge_splits["test"]

Out[30]:

	head_index	head_name	head_source	head_id	head_type	tail_index	tail_name	tail_source	tail_id	tail_type	display_relation	relation
8	8	MT1A	NCBI	4489	1	1785	TP53	NCBI	7157	1	3	protein_protein
12	12	CD7	NCBI	924	1	7681	SFXN5	NCBI	94097	1	3	protein_protein
16	16	SNRPD2	NCBI	6633	1	3235	PRPF4	NCBI	9128	1	3	protein_protein
18	19	VAV3	NCBI	10451	1	3005	ZRANB1	NCBI	54764	1	3	protein_protein
29	16	SNRPD2	NCBI	6633	1	216	NCSTN	NCBI	23385	1	3	protein_protein
...	...	...	...	...	...	...	...	...	...	...	...	...
3768273	125342	myosin V complex	GO	31475	7	9639	DYNLL2	NCBI	140735	1	2	cellcomp_protein
3768277	55608	extracellular membrane-bounded organelle	GO	65010	7	57129	PHOSPHO1	NCBI	162466	1	2	cellcomp_protein
3768283	124243	axonemal outer doublet	GO	97545	7	59351	CFAP100	NCBI	348807	1	2	cellcomp_protein
3768284	124243	axonemal outer doublet	GO	97545	7	59352	CFAP73	NCBI	387885	1	2	cellcomp_protein
3768288	126258	uropod membrane	GO	31259	7	57434	SCIMP	NCBI	388325	1	2	cellcomp_protein

393680 rows × 12 columns

In [31]:

# Check dataframe of testing nodes
biobridge_splits["node_test"]

# Check dataframe of testing nodes biobridge_splits["node_test"]

Out[31]:

	node_index	node_type
0	8	1
1	12	1
2	16	1
3	19	1
4	34	1
...	...	...
8490	127404	7
8491	127415	7
8492	127421	7
8493	127425	7
8494	127434	7

8495 rows × 2 columns