Towards Automatic Extraction of Social Networks of Organizations in PubMed Abstracts

TL;DR

This paper presents an automated method for extracting and normalizing organizational social networks from PubMed abstracts, aiding research analysis and collaboration mapping in biomedical fields.

Contribution

It introduces a novel normalization process combining clustering and connected component analysis for organization name disambiguation in biomedical literature.

Findings

Effective organization name normalization demonstrated in angiogenesis research.

Method facilitates identification of key research groups in specific disease areas.

Potential to enhance translational research and funding decisions.

Abstract

Social Network Analysis (SNA) of organizations can attract great interest from government agencies and scientists for its ability to boost translational research and accelerate the process of converting research to care. For SNA of a particular disease area, we need to identify the key research groups in that area by mining the affiliation information from PubMed. This not only involves recognizing the organization names in the affiliation string, but also resolving ambiguities to identify the article with a unique organization. We present here a process of normalization that involves clustering based on local sequence alignment metrics and local learning based on finding connected components. We demonstrate the application of the method by analyzing organizations involved in angiogenensis treatment, and demonstrating the utility of the results for researchers in the pharmaceutical and biotechnology industries or national funding agencies.