PubFinder: a tool for improving retrieval rate of relevant PubMed abstracts

Abstract

Since it is becoming increasingly laborious to manually extract useful information embedded in the ever-growing volumes of literature, automated intelligent text analysis tools are becoming more and more essential to assist in this task. PubFinder (www.glycosciences.de/tools/PubFinder) is a publicly available web tool designed to improve the retrieval rate of scientific abstracts relevant for a specific scientific topic. Only the selection of a representative set of abstracts is required, which are central for a scientific topic. No special knowledge concerning the query-syntax is necessary. Based on the selected abstracts, a list of discriminating words is automatically calculated, which is subsequently used for scoring all defined PubMed abstracts for their probability of belonging to the defined scientific topic. This results in a hit-list of references in the descending order of their likelihood score. The algorithms and procedures implemented in PubFinder facilitate the perpetual task for every scientist of staying up-to-date with current publications dealing with a specific subject in biomedicine.

Figure 1

Setting up a new scan with PubFinder's ‘New Scan’ dialogue. After having provided a short description and selecting the starting year of the query, the user has to enter a set of PubMed IDs, which represent the scientific topic to be scanned for. In this example we chose a set of 28 PubMed abstracts that deal with literature mining.

Figure 2

The scanned articles are listed in descending order according to their likelihood of discussing the user's given topic. The score is calculated as stated in (8) and represents the logarithm of the likelihood of belonging to the topic or not.