|Ms. Rasna Rani Walia Publications|
|1||2011||PRIDB: A Protein–RNA Interface Database|
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The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein–RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein–RNA complexes are provided.
|2||2008||Collaborative Filtering: A Comparison Of Graph-based Semisupervised Learning Methods And Memory-based Methods|
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Collaborative filtering is a method of making predictions about the interests of a user based on interest similarity to other users and consequently recommending the predicted items. There is a widespread use of collaborative filtering systems in commercial websites, such as Amazon.com, which has popularized item-based methods. There are also many music and video sites such as iLike and Everyone’s a Critic (EaC) that implement collaborative filtering systems. This trend is growing in product-based sites. This paper discusses the implementation of graph-based semisupervised learning methods and memory-based methods to the collaborative filtering scenario and compares these methods to baseline methods such as techniques based on weighted average. This work compares the predictive accuracy of these methods on the MovieLens data set. The metrics used for evaluation measure the accuracy of generated predictions based on already known, held-out ratings that constitute the test set. Preliminary results indicate that graph-based semi-supervised learning methods perform better than baseline methods. However, some of the memory-based methods outperform the graph-based semi-supervised learning methods as well as the baseline methods.