  The correlation and interactions among different biological entities comprise
the biological system. Although already revealed interactions contribute to the
understanding of different existing systems, researchers face many questions
everyday regarding inter-relationships among entities. Their queries have
potential role in exploring new relations which may open up a new area of
investigation. In this paper, we introduce a text mining based method for
answering the biological queries in terms of statistical computation such that
researchers can come up with new knowledge discovery. It facilitates user to
submit their query in natural linguistic form which can be treated as
hypothesis. Our proposed approach analyzes the hypothesis and measures the
p-value of the hypothesis with respect to the existing literature. Based on the
measured value, the system either accepts or rejects the hypothesis from
statistical point of view. Moreover, even it does not find any direct
relationship among the entities of the hypothesis, it presents a network to
give an integral overview of all the entities through which the entities might
be related. This is also congenial for the researchers to widen their view and
thus think of new hypothesis for further investigation. It assists researcher
to get a quantitative evaluation of their assumptions such that they can reach
a logical conclusion and thus aids in relevant re-searches of biological
knowledge discovery. The system also provides the researchers a graphical
interactive interface to submit their hypothesis for assessment in a more
convenient way.
