Name: Rohan Duhaney

Your magic request: "Write a code snippet to solve Investigating Protein-Protein Interactions in Disease Mechanisms In a molecular biology lab investigating disease mechanisms, scientists want to analyze protein-protein interaction networks. Let's generate fictitious protein interaction data and visualize it using Python. using Python. Please use fictitious data for a large scale company"

Email:Â smartduhaney@gmail.com

Dear Rohan Duhaney,

# Import necessary libraries

import numpy as np

import pandas as pd

import networkx as nx

import matplotlib.pyplot as plt

# Generate fictitious protein interaction data

# We will create a random adjacency matrix of size 100x100 to represent protein interaction network

# The values in the matrix will represent the strength of interaction between two proteins

adj_matrix = np.random.rand(100, 100)

# Convert the matrix into a dataframe for easier manipulation

df = pd.DataFrame(adj_matrix)

# Create a list of proteins for labeling the nodes in the network

proteins = [f"Protein_{i}" for i in range(100)]

# Create a networkx graph object

G = nx.Graph()

# Add nodes to the graph

G.add_nodes_from(proteins)

# Add edges to the graph based on the values in the adjacency matrix

# We will only add edges for interactions with strength greater than 0.5

for i in range(len(proteins)):

Â Â for j in range(i+1, len(proteins)):

Â Â Â Â if df.iloc[i, j] > 0.5:

Â Â Â Â Â Â G.add_edge(proteins[i], proteins[j], weight=df.iloc[i, j])

# Visualize the network using a spring layout

pos = nx.spring_layout(G)

# Plot the nodes and edges

nx.draw_networkx_nodes(G, pos, node_size=100, node_color='blue')

nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5)

# Add labels to the nodes

nx.draw_networkx_labels(G, pos, font_size=8)

# Display the plot

plt.show()

# Calculate the average degree of the network

avg_degree = sum([val for (node, val) in G.degree()])/len(G)

print(f"The average degree of the network is {avg_degree}")

# Calculate the degree distribution of the network

degree_dist = [val for (node, val) in G.degree()]

# Plot the degree distribution as a histogram

plt.hist(degree_dist)

plt.xlabel("Degree")

plt.ylabel("Frequency")

plt.title("Degree Distribution of Protein Interaction Network")

plt.show()

# Calculate the clustering coefficient of the network

clustering_coeff = nx.average_clustering(G)

print(f"The clustering coefficient of the network is {clustering_coeff}")

# Calculate the shortest path between two randomly selected nodes in the network

# We will select two random nodes from the list of proteins

node_1 = np.random.choice(proteins)

node_2 = np.random.choice(proteins)

# Calculate the shortest path between the two nodes

shortest_path = nx.shortest_path(G, node_1, node_2)

print(f"The shortest path between {node_1} and {node_2} is: {shortest_path}")

# Calculate the betweenness centrality of each node in the network

betweenness_centrality = nx.betweenness_centrality(G)

# Sort the nodes based on their betweenness centrality value in descending order

sorted_nodes = sorted(betweenness_centrality.

# Print the top 10 nodes with highest betweenness centrality

print("Top 10 nodes with highest betweenness centrality:")

for node, val in sorted_nodes[:10]:

Â Â print(f"{node}: {val}")