<｜begin▁of▁sentence｜>### Instruction:
As a computational biologist, imagine you have a large dataset representing a network of genetic interactions. You are interested in identifying the smallest set of genetic interactions to reverse in order to transform the network into a directed acyclic graph. In this scenario, you want to apply the 'eades' method provided by the `feedback_arc_set` function in igraph to compute the feedback arc set and make the network acyclic.
### Response:
import igraph as ig

# Create a directed graph
g = ig.Graph(directed=True)
g.add_vertices(5)
g.add_edges([(0, 1), (1, 2), (2, 3), (3, 4), (4, 0)])

# Calculate feedback arc set using Eades et al. method
eades_fas = g.feedback_arc_set(method='eades')
print(f'Feedback Arc Set (Eades et al.): {eades_fas}')
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