Topic dynamics: an alternative model of bursts in streams of topics
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Visual Exploration across Biomedical Databases
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Interpretation and trust: designing model-driven visualizations for text analysis
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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What is the structure of the research reported on melanoma? How has it evolved over the last 40 years? Which parts of this research field are correlated with the study of genes and proteins? Are there sudden increases in the number of occurrences of certain gene or protein names, reflecting a surge of interest? How are genes, protein and papers interconnected via co-occurrence patterns? This paper aims to provide answers to these questions by analyzing a data set consisting of papers from Medline, genes from the Entrez Gene database, and proteins from UniProt.Word burst detection and co-occurrence analyses were both performed.The spatial layout algorithm VxOrd was applied to create the very first map that shows papers, genes, and proteins and their co-occurrence relationships.The results were validated by five domain experts leading to a number of interesting facts pertaining to structure and dynamics of the melanoma researchy field.