Knowledge-based clustering

In the knowledge-based clustering approaches reported in the literature, explicit know ledge, typically in the form of a set of concepts, is used in computing similarity or conceptual cohesiveness between objects and in grouping them. We propose a knowledge-based clustering approach in which the domain knowledge is also used in the pattern representation phase of clustering. We argue that such a knowledge-based pattern representation scheme reduces the complexity of similarity computation and grouping phases. We present a knowledge-based clustering algorithm for grouping hooks in a library.

Techniques for realistic visualization of fluids: A survey

Visualization of fluids has wide applications in science, engineering and entertainment. Various methodologies Of visualizing fluids have evolved which emphasize on capturing different aspects of the fluids accurately. In this survey the existing methods for realistic visualization of fluids are reviewed. The approaches are classified based on the key concept they rely on for fluid modeling. This classification allows for easy selection of the method to be adopted for visualization given an application. It also enables identification of alternative techniques for fluid modeling.

PocketAlign A Novel Algorithm for Aligning Binding Sites in Protein Structures

A fundamental task in bioinformatics involves a transfer of knowledge from one protein molecule onto another by way of recognizing similarities. Such similarities are obtained at different levels, that of sequence, whole fold, or important substructures. Comparison of binding sites is important to understand functional similarities among the proteins and also to understand drug cross-reactivities. Current methods in literature have their own merits and demerits, warranting exploration of newer concepts and algorithms, especially for large-scale comparisons and for obtaining accurate residue-wise mappings. Here, we report the development of a new algorithm, PocketAlign, for obtaining structural superpositions of binding sites. The software is available as a web-service at http://proline.physicslisc.emetin/pocketalign/. The algorithm encodes shape descriptors in the form of geometric perspectives, supplemented by chemical group classification. The shape descriptor considers several perspectives with each residue as the focus and captures relative distribution of residues around it in a given site. Residue-wise pairings are computed by comparing the set of perspectives of the first site with that of the second, followed by a greedy approach that incrementally combines residue pairings into a mapping. The mappings in different frames are then evaluated by different metrics encoding the extent of alignment of individual geometric perspectives. Different initial seed alignments are computed, each subsequently extended by detecting consequential atomic alignments in a three-dimensional grid, and the best 500 stored in a database. Alignments are then ranked, and the top scoring alignments reported, which are then streamed into Pymol for visualization and analyses. The method is validated for accuracy and sensitivity and benchmarked against existing methods. An advantage of PocketAlign, as compared to some of the existing tools available for binding site comparison in literature, is that it explores different schemes for identifying an alignment thus has a better potential to capture similarities in ligand recognition abilities. PocketAlign, by finding a detailed alignment of a pair of sites, provides insights as to why two sites are similar and which set of residues and atoms contribute to the similarity.

Special Issue: Engineering applications of representations of function, Part 1

In engineering design, the end goal is the creation of an artifact, product, system, or process that fulfills some functional requirements at some desired level of performance. As such, knowledge of functionality is essential in a wide variety of tasks in engineering activities, including modeling, generation, modification, visualization, explanation, evaluation, diagnosis, and repair of these artifacts and processes. A formal representation of functionality is essential for supporting any of these activities on computers. The goal of Parts 1 and 2 of this Special Issue is to bring together the state of knowledge of representing functionality in engineering applications from both the engineering and the artificial intelligence (AI) research communities.