Managing and Mining Graph Data part 59 pdf

570 MANAGING AND MINING GRAPH DATA
from the perspective mining of mining a single (large) network in the presence
of noise and uncertainty.
Both data mining and the field of bioinformatics are young and vibrant and
thus there are ample opportunities for interesting lines of future research at
their intersection. Sticking to the theme of this article – graph mining in
bioinformatics – below we list several such opportunities. This list is by no
means a comprehensive list but highlight some of the potential opportunities
researchers may avail of.
Scalable algorithms for analyzing time varying networks: A large ma-
jority of the work to date in this field has focused on the analysis of static
networks. While there have been some recent efforts to analyze dynamic
biological networks, research in this arena is at its infancy. With antici-
pated advances in technology where much more temporal data is likely
to become available temporal analysis of such networks is likely to be an
important arena of future research. Underpinning this effort, given the
size and dynamics of the data involved are the need to develop scalable
algorithms for processing and analyzing such data.
Discovering anomalous structures in graph data: Again while most of
the work to date has focused on the discovery of frequent or modular
structure within such data – the discovery of anomalous substructures
often has a crucial role to play in such domains. Defining what con-
stitutes an anomaly, how to compute it efficiently while leveraging the
ambient knowledge in the domain in question are some of the challenges
to be addressed.
Integrating data from multiple, possibly conflicting sources: A funda-
mental challenge in bioinformatics in general is that of data integration.
Data is available in many formats and often times are in conflict. For ex-
ample protein interaction data produced by various experimental meth-
ods (mass spectrometry, Yeast2Hybrid, in-silico) are often in conflict.
Research into methods that are capable of resolving such conflicts while

still discovering useful patterns are needed.
Incorporating domain information: It has been our observation that often
we as data mining researchers tend to under-utilize available domain
information. This may arise out of ignorance (the field of bioinformatics
is very vast) or simply omitted from the training phase as a means to
confirm the utility of the proposed methods (to maintain the sanctity
of the validation procedure). We believe a fresh look at how domain
knowledge can be embedded in existing approaches and better validation
methodologies in close conjunction with domain experts must be looked
into.
A Survey of Graph Mining Techniques for Biological Datasets 571
Uncertainty-aware and noise-tolerant methods: While this has certainly
been an active area of research in the bioinformatics community in gen-
eral, and in the field of graph mining in bioinformatics in particular,
there are still many open problems here. Incorporating uncertainty is
necessarily a domain-dependent issue and probabilistic approaches of-
fer exciting possibilities. Additionally leveraging topological, relational
and other semantic characteristics of the data effectively is an interesting
topic for future research. A related challenge here is to model trust and
provenance related information.
Ranking and summarizing patterns harvested: While ranking and sum-
marizing patterns has been the subject of much research in the data min-
ing and network science community the role of such methods in bioin-
formatics has been much less researched. We expect this to be a very
important and active area of research especially since often times evalu-
ating and validating patterns discovered can be an expensive and time
consuming process. In this context research into ranking algorithms
for bioinformatics that leverage domain knowledge and mechanisms for
summarizing patterns harvested is an exciting opportunity for future re-
search.

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