  A system level view of cellular processes for human and several organisms can
be cap- tured by analyzing molecular interaction networks. A molecular
interaction network formed of differentially expressed genes and their
interactions helps to understand key players behind disease development. So, if
the functions of these genes are blocked by altering their interactions, it
would have a great impact in controlling the disease. Due to this promising
consequence, the problem of inferring disease causing genes and their pathways
has attained a crucial position in computational biology research. However,
considering the huge size of interaction networks, executing computations can
be costly. Review of literatures shows that the methods proposed for finding
the set of disease causing genes could be assessed in terms of their accuracy
which a perfect algorithm would find. Along with accuracy, the time complexity
of the method is also important, as high time complexities would limit the
number of pathways that could be found within a pragmatic time interval.
