🔴 Biomedical Knowledge Graphs: Constructing and Analyzing Protein-Protein Interaction Networks

AIVIA Project Engine AI in HealthCare & Medicine
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Biomedical Knowledge Graphs: Constructing and Analyzing Protein-Protein Interaction Networks

Objective

The primary objective of this project is to develop a comprehensive understanding of protein-protein interactions (PPIs) by constructing and analyzing a Knowledge Graph using the Stanford BIOSNAP Datasets. You will focus on performing network analysis to identify strongly connected components (SCCs) and significant clusters within the network. This exploration is fundamental for uncovering hidden patterns and insights in the domain of bioinformatics, particularly in disease modeling and drug discovery.


Learning Outcomes

By completing this project, you will:

  • Master the construction and analysis of Knowledge Graphs with a focus on biological data.
  • Develop advanced data visualization skills to effectively represent complex interaction networks.
  • Gain a thorough understanding of protein-protein interaction dynamics and their implications in disease mechanisms and therapeutic target identification.
  • Apply sophisticated network analysis techniques to real-world biological datasets.
  • Understand the application of Knowledge Graphs in bioinformatics, including drug discovery and disease pathway analysis.

Prerequisites and Theoretical Foundations

1. Programming Skills

  • Python Programming (Intermediate Level):
    • Data structures: lists, dictionaries, sets, tuples.
    • Control flow: loops, conditionals, functions.
    • Object-oriented programming: classes, inheritance.
    • Familiarity with libraries: Pandas, NumPy, Matplotlib.
Click to view Python code examples 
# Example: Basic data manipulation with Pandas
import pandas as pd

# Creating a DataFrame
data = {'protein1': ['P1', 'P2'], 'protein2': ['P3', 'P4']}
df = pd.DataFrame(data)

# Displaying the DataFrame
print(df.head())

2. Understanding of Graph Theory

  • Basic Concepts:
    • Nodes (vertices), edges (links), adjacency matrices.
    • Types of graphs: directed, undirected, weighted, unweighted.
  • Graph Properties:
    • Degree, centrality measures, clusters, components.
  • Network Analysis:
    • Strongly connected components, community detection.
Click to view graph theory concepts
  • Node Degree: The number of connections (edges) a node has.
  • Centrality Measures: Quantitative measures to identify the most important nodes in a network (e.g., degree centrality, betweenness centrality).
  • Strongly Connected Component (SCC): A subset of nodes where every node is reachable from every other node via directed edges.

3. Biological Foundations

  • College-Level Biology:
    • Proteins and Their Functions: Enzymes, structural proteins, signaling molecules.
    • Protein-Protein Interactions: How proteins interact to carry out biological functions.
    • Disease Mechanisms: Role of proteins and PPIs in diseases.
    • Drug Discovery Process: Target identification, validation, and therapeutic intervention.
Click to view biological concepts
  • Protein Function: Understanding how proteins contribute to cellular processes.
  • Interaction Networks: How networks of PPIs can influence biological pathways.
  • Disease Pathways: How alterations in PPIs can lead to disease states.

Skills Gained

  • Construction of Knowledge Graphs: Building graphs from biological datasets.
  • Network Analysis Techniques: Applying algorithms to analyze graph properties.
  • Advanced Data Visualization: Representing complex networks visually.
  • Integration of External Biological Data: Incorporating annotations and external datasets.
  • Application in Bioinformatics: Using knowledge graphs for disease modeling and drug discovery.

Tools Required

  • Programming Language: Python 3.7+
  • Libraries:
    • NetworkX: Graph creation and analysis (pip install networkx)
    • Pandas: Data manipulation (pip install pandas)
    • NumPy: Numerical computations (pip install numpy)
    • Matplotlib and Seaborn: Data visualization (pip install matplotlib seaborn)
    • Plotly or Bokeh: Interactive visualizations (pip install plotly bokeh)
    • igraph: Advanced graph analysis (pip install python-igraph)
  • Datasets:
    • Stanford BIOSNAP Datasets: Download link
    • External Annotations (optional): Databases like DrugBank, UniProt, ChEMBL, OMIM, GeneCards, DisGeNET.
  • Integrated Development Environment (IDE):
    • Jupyter Notebook, VSCode, or PyCharm.

Steps and Tasks

At any point during your project, if you need assistance, several resources are available to support you:

  • Code Snippets: Provided for each step to guide your implementation.
  • Discussion Forums: Join discussions to exchange ideas and resolve issues.
  • Mentorship Programs: Access live mentorship, including forum support and webinars.

1. Data Acquisition and Setup

Tasks:

  • Download the Protein-Protein Interaction Dataset:
    • Access the Stanford BIOSNAP website and download the required dataset.
  • Install Necessary Libraries:
    • Ensure all required Python libraries are installed.

Implementation:

# Install required libraries
!pip install networkx pandas numpy matplotlib seaborn python-igraph plotly
Data Download
  • Visit the Stanford BIOSNAP Datasets page.
  • Download the Protein-Protein Interaction dataset.
  • Unzip and place the dataset in your working directory.

2. Loading and Preparing the Data

Tasks:

  • Load the Dataset into Pandas DataFrame:
    • Read the interaction data file using Pandas.
  • Explore the Data:
    • View the first few rows and summary statistics.
  • Data Cleaning:
    • Handle missing values, duplicates, and data types.

Implementation:

import pandas as pd

# Load the dataset
data = pd.read_csv('protein_interactions.csv')

# Explore the data
print(data.head())
print(data.info())

# Data cleaning (if necessary)
data.dropna(inplace=True)
data.drop_duplicates(inplace=True)
Explanation
  • data.head(): Displays the first five rows.
  • data.info(): Provides information about data types and missing values.

3. Constructing the Knowledge Graph

Tasks:

  • Create a Graph Object Using NetworkX:
    • Build the graph from the edge list.
  • Visualize the Graph:
    • Use Matplotlib or Seaborn for basic visualization.
  • Assign Attributes (Optional):
    • Add node and edge attributes if available.

Implementation:

import networkx as nx

# Create the graph
G = nx.from_pandas_edgelist(data, source='protein1', target='protein2')

# Basic graph information
print(nx.info(G))

# Simple visualization
import matplotlib.pyplot as plt

plt.figure(figsize=(12, 8))
nx.draw(G, node_size=20, node_color='blue')
plt.title('Protein-Protein Interaction Network')
plt.show()
Explanation
  • nx.from_pandas_edgelist: Creates a graph from a DataFrame.
  • Visualization: For large graphs, consider sampling or filtering to visualize subsets.

4. Basic Network Analysis

Tasks:

  • Compute Basic Network Metrics:
    • Number of nodes and edges.
    • Density, degree distribution.
  • Identify Connected Components:
    • Find the number of connected components.
  • Degree Distribution Analysis:
    • Plot the degree distribution to understand network connectivity.

Implementation:

# Number of nodes and edges
num_nodes = G.number_of_nodes()
num_edges = G.number_of_edges()
print(f'Number of nodes: {num_nodes}')
print(f'Number of edges: {num_edges}')

# Network density
density = nx.density(G)
print(f'Network density: {density:.4f}')

# Degree distribution
degrees = [G.degree(n) for n in G.nodes()]
plt.hist(degrees, bins=50)
plt.title('Degree Distribution')
plt.xlabel('Degree')
plt.ylabel('Frequency')
plt.show()

# Connected components
components = nx.connected_components(G)
num_components = nx.number_connected_components(G)
print(f'Number of connected components: {num_components}')
Explanation
  • Degree Distribution: Helps identify if the network follows a power-law distribution, common in biological networks.
  • Connected Components: Large numbers may indicate fragmented networks; SCCs are important for understanding network robustness.

5. Advanced Network Analysis

Tasks:

  • Compute Centrality Measures:
    • Degree centrality, betweenness centrality, closeness centrality.
  • Identify Key Proteins:
    • Proteins with high centrality scores.
  • Community Detection:
    • Use algorithms to find clusters or communities.

Implementation:

# Degree Centrality
degree_centrality = nx.degree_centrality(G)
sorted_degree = sorted(degree_centrality.items(), key=lambda x: x[1], reverse=True)

# Betweenness Centrality
betweenness_centrality = nx.betweenness_centrality(G)
sorted_betweenness = sorted(betweenness_centrality.items(), key=lambda x: x[1], reverse=True)

# Display top 5 proteins by centrality measures
print("Top 5 proteins by degree centrality:")
for protein, centrality in sorted_degree[:5]:
    print(f'{protein}: {centrality:.4f}')

print("\nTop 5 proteins by betweenness centrality:")
for protein, centrality in sorted_betweenness[:5]:
    print(f'{protein}: {centrality:.4f}')
Explanation
  • Centrality Measures: Identify proteins that are highly connected or serve as bridges in the network.
  • Community Detection: Can be performed using NetworkX or igraph’s algorithms.

6. Visualizing the Network

Tasks:

  • Create Advanced Visualizations:
    • Use interactive visualization libraries like Plotly or Bokeh.
  • Color Nodes by Attributes:
    • For example, color nodes based on centrality scores.
  • Visualize Subgraphs:
    • Focus on important clusters or communities.

Implementation:

import plotly.graph_objs as go

# Position nodes using a layout algorithm
pos = nx.spring_layout(G, k=0.15, iterations=20)

# Create edge trace
edge_trace = go.Scatter(
    x=[],
    y=[],
    line=dict(width=0.5, color='#888'),
    hoverinfo='none',
    mode='lines'
)

for edge in G.edges():
    x0, y0 = pos[edge[0]]
    x1, y1 = pos[edge[1]]
    edge_trace['x'] += [x0, x1, None]
    edge_trace['y'] += [y0, y1, None]

# Create node trace
node_trace = go.Scatter(
    x=[],
    y=[],
    text=[],
    mode='markers',
    hoverinfo='text',
    marker=dict(
        showscale=True,
        colorscale='YlGnBu',
        reversescale=True,
        color=[],
        size=10,
        colorbar=dict(
            thickness=15,
            title='Node Centrality',
            xanchor='left',
            titleside='right'
        ),
        line_width=2)
)

for node in G.nodes():
    x, y = pos[node]
    node_trace['x'].append(x)
    node_trace['y'].append(y)
    node_trace['marker']['color'].append(degree_centrality[node])
    node_info = f'Protein: {node}<br>Degree Centrality: {degree_centrality[node]:.4f}'
    node_trace['text'].append(node_info)

# Create figure
fig = go.Figure(data=[edge_trace, node_trace],
             layout=go.Layout(
                title='Protein-Protein Interaction Network',
                titlefont_size=16,
                showlegend=False,
                hovermode='closest',
                margin=dict(b=20,l=5,r=5,t=40),
                annotations=[dict(
                    text="Protein-Protein Interaction Network Visualization",
                    showarrow=False,
                    xref="paper", yref="paper",
                    x=0.005, y=-0.002 )],
                xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
                yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))
                )

fig.show()
Explanation
  • Interactive Visualization: Allows for better exploration of large networks.
  • Color Mapping: Nodes colored based on centrality measures to highlight important proteins.

7. Application in Drug Discovery

Tasks:

  • Incorporate External Drug Target Information:
    • Use databases like DrugBank, UniProt, or ChEMBL to identify known drug targets.
  • Annotate the Graph:
    • Mark nodes (proteins) that are known drug targets.
  • Identify Potential Drug Targets:
    • Analyze centrality measures to find proteins that could be potential targets.
  • Interaction Analysis:
    • Examine interactions between drug targets and other proteins.

Implementation:

# Load drug target data
drug_targets = pd.read_csv('drug_targets.csv')  # Contains 'protein_id' column

# Create a set of drug target proteins
drug_target_set = set(drug_targets['protein_id'])

# Add 'drug_target' attribute to nodes
for node in G.nodes():
    G.nodes[node]['drug_target'] = node in drug_target_set

# Identify proteins interacting with multiple drug targets
potential_targets = []
for node in G.nodes():
    if not G.nodes[node]['drug_target']:
        neighbors = list(G.neighbors(node))
        num_drug_target_neighbors = sum([1 for n in neighbors if G.nodes[n].get('drug_target', False)])
        if num_drug_target_neighbors >= 2:
            potential_targets.append((node, num_drug_target_neighbors))

# Display potential targets
print("Proteins interacting with multiple drug targets:")
for protein, count in potential_targets:
    print(f'{protein}: Interacts with {count} drug targets')
Explanation
  • Annotation: Enhances the graph with additional biological information.
  • Potential Targets: Proteins interacting with multiple drug targets may be of therapeutic interest.

8. Disease Pathway Analysis Using External Annotations

Tasks:

  • Gather Disease-Associated Protein Annotations:
    • Use databases like OMIM, GeneCards, or DisGeNET.
  • Construct Disease-Specific Subgraphs:
    • Extract subgraphs for specific diseases.
  • Analyze Disease Pathways:
    • Identify key proteins and interactions in disease mechanisms.

Implementation:

# Load disease association data
disease_data = pd.read_csv('disease_associations.csv')  # Contains 'protein_id' and 'disease' columns

# Group proteins by disease
disease_groups = disease_data.groupby('disease')['protein_id'].apply(list)

# Analyze a specific disease (e.g., Alzheimer's Disease)
disease = 'Alzheimer’s Disease'
proteins_in_disease = disease_groups[disease]

# Create a subgraph
disease_subgraph = G.subgraph(proteins_in_disease)

# Analyze the subgraph
print(f'Number of proteins in {disease} pathway: {disease_subgraph.number_of_nodes()}')
print(f'Number of interactions: {disease_subgraph.number_of_edges()}')

# Visualize the disease subgraph
plt.figure(figsize=(8,6))
nx.draw_networkx(disease_subgraph, node_color='red', with_labels=True, node_size=500, font_size=8)
plt.title(f'{disease} Protein Interaction Network')
plt.show()
Explanation
  • Subgraph Analysis: Focuses on a specific disease to understand its molecular interactions.
  • Visualization: Helps in identifying key proteins within disease pathways.

9. Advanced Analysis with igraph (Optional)

Tasks:

  • Convert NetworkX Graph to igraph Object:
    • Utilize igraph for advanced algorithms.
  • Perform Community Detection:
    • Use algorithms like Louvain or Infomap.
  • Analyze Network Motifs:
    • Identify recurring, significant patterns.

Implementation:

from igraph import Graph

# Convert NetworkX graph to igraph
edges = [e for e in G.edges()]
ig = Graph(edges=edges)

# Community detection using Louvain algorithm
communities = ig.community_multilevel()
print(f'Number of communities detected: {len(communities)}')

# Assign community membership to nodes
membership = communities.membership
for idx, node in enumerate(G.nodes()):
    G.nodes[node]['community'] = membership[idx]

# Analyze largest community
largest_community_nodes = [v['name'] for v in ig.vs if v['membership'] == max(set(membership), key=membership.count)]
largest_community_subgraph = G.subgraph(largest_community_nodes)

print(f'Number of proteins in largest community: {largest_community_subgraph.number_of_nodes()}')
Explanation
  • igraph: Offers efficient implementations of graph algorithms.
  • Community Detection: Reveals modular structures within the network.

10. Next Steps and Enhancements

Suggestions:

  • Integrate Additional Data Sources:
    • Include gene expression data, mutations, or post-translational modifications.
  • Temporal Analysis:
    • Study how PPIs change over time or under different conditions.
  • Machine Learning Applications:
    • Use graph embeddings for classification or prediction tasks.
  • Database Integration:
    • Store the knowledge graph in graph databases like Neo4j for efficient querying.