Accurate and scalable techniques for the complex/pathway membership problem in protein networks.

A protein network shows physical interactions as well as functional associations. An important usage of such networks is to discover unknown members of partially known complexes and pathways. A number of methods exist for such analyses, and they can be divided into two main categories based on their treatment of highly connected proteins.

Integrating multi-attribute similarity networks for robust representation of the protein space.

Motivation: A global view of the protein space is essential for functional and evolutionary analysis of proteins. In order to achieve this, a similarity network can be built using pairwise relationships among proteins. However, existing similarity networks employ a single similarity measure and therefore their utility depends highly on the quality of the selected measure.

Towards index-based similarity search for protein structure databases.

We propose two methods for finding similarities in protein structure databases. Our techniques extract feature vectors on triplets of SSEs (Secondary Structure Elements) of proteins. These feature vectors are then indexed using a multidimensional index structure.

Automated protein classification using consensus decision.

We propose a novel technique for automatically generating the SCOP classification of a protein structure with high accuracy. High accuracy is achieved by combining the decisions of multiple methods using the consensus of a committee (or an ensemble) classifier. Our technique is rooted in machine learning which shows that by judicially employing component classifiers, an ensemble classifier can be constructed to outperform its components.

Decision tree based information integration for automated protein classification.

We propose a novel technique for automatically generating the SCOP classification of a protein structure with high accuracy. We achieve accurate classification by combining the decisions of multiple methods using the consensus of a committee (or an ensemble) classifier. Our technique, based on decision trees, is rooted in machine learning which shows that by judicially employing component classifiers, an ensemble classifier can be constructed to outperform its components.

Index-based similarity search for protein structure databases.

We propose new methods for finding similarities in protein structure databases. These methods extract feature vectors on triplets of SSEs (Secondary Structure Elements) of proteins. The feature vectors are then indexed using a multidimensional index structure.

PSI: indexing protein structures for fast similarity search.

Motivation: We consider the problem of finding similarities in protein structure databases. Current techniques sequentially compare the given query protein to all of the proteins in the database to find similarities. Therefore, the cost of similarity queries increases linearly as the volume of the protein databases increase.