Ranking Structure Instituted in Duke Libraries

Ranking Targets in Structure-Based Virtual Screening of Three-Dimensional Protein Libraries: Methods and Problems

Structure-based virtual screening is a promising tool to identify putative targets for a specific ligand. Instead of docking multiple ligands into a single protein cavity, a single ligand is docked in a collection of binding sites. In inverse screening, hits are in fact targets which have been prioritized within the pool of best ranked proteins. The target rate depends on specificity and promis...

Popularity Weighted Ranking for Academic Digital Libraries

We propose a popularity weighted ranking algorithm for academic digital libraries that uses the popularity factor of a publication venue overcoming the limitations of impact factors. We compare our method with the naive PageRank, citation counts and HITS algorithm, three popular measures currently used to rank papers beyond lexical similarity. The ranking results are evaluated by discounted cum...

Using Lotkaian Informetrics for Ranking in Digital Libraries

The purpose of this paper is to propose the use of models, theories and laws in bibliometrics and scientometrics to enhance information retrieval processes, especially ranking. A common pattern in many man-made data sets is Lotka’s Law which follows the well-known power-law distributions. These informetric distributions can be used to give an alternative order to large and scattered result sets...

Tom Duke

Optimal ranking in networks with community structure

The World-Wide Web (WWW) with its enormous size (~ 10 webpages) presents a challenge for efficient information retrieval and ranking. By effectively utilizing the topological information to rank the webpages, Google became the most popular tool on the web. One important feature of the WWW is that it exhibits a strong community structure in which groups of webpages (e.g. those devoted to a commo...