The role of lectins in mediating cancer metastasis, apoptosis as well as various other signaling events has been well established in the past few years. Data on various aspects of the role of lectins in cancer is being accumulated at a rapid pace. The data on lectins available in the literature is so diverse, that it becomes difficult and time-consuming, if not impossible to comprehend the advances in various areas and obtain the maximum benefit. Not only do the lectins vary significantly in their individual functional roles, but they are also diverse in their sequences, structures, binding site architectures, quaternary structures, carbohydrate affinities and specificities as well as their potential applications. An organization of these seemingly independent data into a common framework is essential in order to achieve effective use of all the data towards understanding the roles of different lectins in different aspects of cancer and any resulting applications. An integrated knowledge base (CancerLectinDB) together with appropriate analytical tools has therefore been developed for lectins relevant for any aspect of cancer, by collating and integrating diverse data. This database is unique in terms of providing sequence, structural, and functional annotations for lectins from all known sources in cancer and is expected to be a useful addition to the number of glycan related resources now available to the community. The database has been implemented using MySQL on a Linux platform and web-enabled using Perl-CGI and Java tools. Data for individual lectins pertain to taxonomic, biochemical, domain architecture, molecular sequence and structural details as well as carbohydrate specificities. Extensive links have also been provided for relevant bioinformatics resources and analytical tools. Availability of diverse data integrated into a common framework is expected to be of high value for various studies on lectin cancer biology. CancerLectinDB can be accessed through http://proline.physics.iisc.ernet.in/cancerdb .

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In this paper we present an extension of existing Nearest-Neighbor heuristics to an algorithm called k-Repetitive-Nearest-Neighbor. The idea is to start with a tour of k nodes and then perform a Nearest-Neighbor search from there on. After doing this for all permutations of k nodes the result gets selected as the shortest tour found. Experimental results show that for 2-RNN the solutions quality remains relatively stable between about 10% to 40% above the optimum.

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Social network data analysis is an important problem due to proliferation of social network applications, amount of data these applications generate, and potential of insight based on this big data. The objective of the present work is to propose architecture for a semantic web application to facilitate meaningful social network data analytics as well as answer query about concerned ontology. Proposed technique, on the one hand, links tools based on semantic technology provided by social network applications with data analytics tools and, on the other hand, extends this link to ontology authoring tools for further inference. The results obtained from data analytics tool, results of query on generated ontology, and benchmarking of the performance of data analytics tool are shown. It has been observed that a semantic web application utilizing above-mentioned tools and technologies is more versatile and flexible and further improvements are possible by applying generic data mining algorithms to the above scenario.