Implementation of old, descriptive codes, and visualize graphic graphs using Pybel
In this lesson, we check how to get it Pybel Ecosystem to build and analyze rich graphs in rich information within Google Colab. We start by entering all the required packages, including Pybel, Network, Matplotlib, Seasbor, and Panda. Then we show how we can describe protein, processes, and conversion using Pybel DSL. From there, we guide you in the construction of alzheimer's related diseases, shows how equal causal relationships, protein interaction, and phospherylation events. Next to the creation of the graph, we launch an advanced network, including hundreds of Centrality, Node separation, and subgraph, and the production strategies for the cain and priep. At the end of this phase, you will have a full-defined Graph of the Downsmrright look and enrichment, losing a solid basis for testing the biological knowledge.
!pip install pybel pybel-tools networkx matplotlib seaborn pandas -q
import pybel
import pybel.dsl as dsl
from pybel import BELGraph
from pybel.io import to_pickle, from_pickle
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
print("PyBEL Advanced Tutorial: Biological Expression Language Ecosystem")
print("=" * 65)We start by installing the pybel and its direct dependency to Colob, to ensure that all the required libraries, network, matplotlib, is available in our analysis. Once installed, we import important modules and press for storage warnings for our writing clean and focused on the results.
print("n1. Building a Biological Knowledge Graph")
print("-" * 40)
graph = BELGraph(
name="Alzheimer's Disease Pathway",
version="1.0.0",
description="Example pathway showing protein interactions in AD",
authors="PyBEL Tutorial"
)
app = dsl.Protein(name="APP", namespace="HGNC")
abeta = dsl.Protein(name="Abeta", namespace="CHEBI")
tau = dsl.Protein(name="MAPT", namespace="HGNC")
gsk3b = dsl.Protein(name="GSK3B", namespace="HGNC")
inflammation = dsl.BiologicalProcess(name="inflammatory response", namespace="GO")
apoptosis = dsl.BiologicalProcess(name="apoptotic process", namespace="GO")
graph.add_increases(app, abeta, citation="PMID:12345678", evidence="APP cleavage produces Abeta")
graph.add_increases(abeta, inflammation, citation="PMID:87654321", evidence="Abeta triggers neuroinflammation")
tau_phosphorylated = dsl.Protein(name="MAPT", namespace="HGNC",
variants=[dsl.ProteinModification("Ph")])
graph.add_increases(gsk3b, tau_phosphorylated, citation="PMID:11111111", evidence="GSK3B phosphorylates tau")
graph.add_increases(tau_phosphorylated, apoptosis, citation="PMID:22222222", evidence="Hyperphosphorylated tau causes cell death")
graph.add_increases(inflammation, apoptosis, citation="PMID:33333333", evidence="Inflammation promotes apoptosis")
graph.add_association(abeta, tau, citation="PMID:44444444", evidence="Abeta and tau interact synergistically")
print(f"Created BEL graph with {graph.number_of_nodes()} nodes and {graph.number_of_edges()} edges")We begin the belgram with a metadata of alzheimer's infections and explain proteins and procedures that use Pybel DSL. By adding causal relationship, protein transformation and organizations, we create a broad network that attracts good molecular cooperation.
print("n2. Advanced Network Analysis")
print("-" * 30)
degree_centrality = nx.degree_centrality(graph)
betweenness_centrality = nx.betweenness_centrality(graph)
closeness_centrality = nx.closeness_centrality(graph)
most_central = max(degree_centrality, key=degree_centrality.get)
print(f"Most connected node: {most_central}")
print(f"Degree centrality: {degree_centrality[most_central]:.3f}")It includes a degree, between Fitness, as well as the placement of intensity prices to measure the importance of each area within the graph. By identifying the most connected areas, we gain insight into hubs that can run the ways of disease.
print("n3. Biological Entity Classification")
print("-" * 35)
node_types = Counter()
for node in graph.nodes():
node_types[node.function] += 1
print("Node distribution:")
for func, count in node_types.items():
print(f" {func}: {count}")We separate each place for its work, such as protein or biolodinproSprecess, and their offenses. This breakfast helps us to understand the formation of our network.
print("n4. Pathway Analysis")
print("-" * 20)
proteins = [node for node in graph.nodes() if node.function == 'Protein']
processes = [node for node in graph.nodes() if node.function == 'BiologicalProcess']
print(f"Proteins in pathway: {len(proteins)}")
print(f"Biological processes: {len(processes)}")
edge_types = Counter()
for u, v, data in graph.edges(data=True):
edge_types[data.get('relation')] += 1
print("nRelationship types:")
for rel, count in edge_types.items():
print(f" {rel}: {count}")We separate all proteins and processes to measure the size of the method and the difficulty of the way. Counting various relationship types reflects any relationships, such as the rise or organizations, contribute our model.
print("n5. Literature Evidence Analysis")
print("-" * 32)
citations = []
evidences = []
for _, _, data in graph.edges(data=True):
if 'citation' in data:
citations.append(data['citation'])
if 'evidence' in data:
evidences.append(data['evidence'])
print(f"Total citations: {len(citations)}")
print(f"Unique citations: {len(set(citations))}")
print(f"Evidence statements: {len(evidences)}")We produce citation identifiers and testimonial structures from each edge to view our graph introduction to published research. To summarize complete and unique quotations allow us to test the range of book support.
print("n6. Subgraph Analysis")
print("-" * 22)
inflammation_nodes = [inflammation]
inflammation_neighbors = list(graph.predecessors(inflammation)) + list(graph.successors(inflammation))
inflammation_subgraph = graph.subgraph(inflammation_nodes + inflammation_neighbors)
print(f"Inflammation subgraph: {inflammation_subgraph.number_of_nodes()} nodes, {inflammation_subgraph.number_of_edges()} edges")We separate the Inflammation Subgram by collecting their neighbors directly, stirring up the Crostalk focus. This target function highlights how swelling affects other processes of disease.
print("n7. Advanced Graph Querying")
print("-" * 28)
try:
paths = list(nx.all_simple_paths(graph, app, apoptosis, cutoff=3))
print(f"Paths from APP to apoptosis: {len(paths)}")
if paths:
print(f"Shortest path length: {len(paths[0])-1}")
except nx.NetworkXNoPath:
print("No paths found between APP and apoptosis")
apoptosis_inducers = list(graph.predecessors(apoptosis))
print(f"Factors that increase apoptosis: {len(apoptosis_inducers)}")We put simple ways between app and apoptosis to test mechanical methods and point to key internetidates. Listening All predictors that apoptosis shows what things can cause cell death.
print("n8. Data Export and Visualization")
print("-" * 35)
adj_matrix = nx.adjacency_matrix(graph)
node_labels = [str(node) for node in graph.nodes()]
plt.figure(figsize=(12, 8))
plt.subplot(2, 2, 1)
pos = nx.spring_layout(graph, k=2, iterations=50)
nx.draw(graph, pos, with_labels=False, node_color="lightblue",
node_size=1000, font_size=8, font_weight="bold")
plt.title("BEL Network Graph")
plt.subplot(2, 2, 2)
centralities = list(degree_centrality.values())
plt.hist(centralities, bins=10, alpha=0.7, color="green")
plt.title("Degree Centrality Distribution")
plt.xlabel("Centrality")
plt.ylabel("Frequency")
plt.subplot(2, 2, 3)
functions = list(node_types.keys())
counts = list(node_types.values())
plt.pie(counts, labels=functions, autopct="%1.1f%%", startangle=90)
plt.title("Node Type Distribution")
plt.subplot(2, 2, 4)
relations = list(edge_types.keys())
rel_counts = list(edge_types.values())
plt.bar(relations, rel_counts, color="orange", alpha=0.7)
plt.title("Relationship Types")
plt.xlabel("Relation")
plt.ylabel("Count")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()We prepare the matrics with node labels with lower river use and generate a multi-panel implantation of the network type, and the form of the door of the door, and the species of the door, and the calculation of the door. of the Odage, as well as the form of the form of the Odod, and the formation of the Odod form, and the form of the Odod form, as well as the form of the Odod, and the form of the Odod, and the form of the Odage, as well as the species of the Odage, and the calculation of the Odod Of the door type, as well as the calculation of the Odage, as well as the calculation of the Odage, and the species of the door. This looks at the bee of our Bel in life, supporting the deepest interpreter.
In this lesson, we have shown the power and flexibility of Pybel to measure biological system. We have shown that a person can build a selected white-box graph of the disease, analyze the network levels to find main hub areas, and releases logical subgraters in reality. We have also included important practices for proof of proof of mining and resolved data to ask. Like the next step, we encourage you to expand your draft, add additional OMICS data, effective financial training, or combine the graph of the travel service.
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Sana Hassan, a contact in MarktechPost with a student of the Dual-degree student in the IIit Madras, loves to use technology and ai to deal with the real challenges of the world. I'm very interested in solving practical problems, brings a new view of ai solution to AI and real solutions.
