Anne O Tate

Anne O Tate analyzes PubMed search results and performs text mining to extract summaries, topic co-occurrence patterns, publication-type predictions, and citation-network information for biomedical literature analysis.

Key Features:

• Summarization and drill-down: Generates summaries and supports drill-down by keywords in titles and abstracts, topics, topic clusters, author names, affiliations, journal names, and publication year.
• Sorting and ranking: Sorts and ranks articles by keywords, important phrases within titles and abstracts, author names, journal, publication year, number of authors, and statistically enriched co-occurring topic pairs.
• Publication-type display and prediction: Displays NLM-indexed publication types and predicts 50 publication types and study designs using a machine learning-based model.
• Mine the Gap!: Identifies pairs of topics that are under-represented within a result set to highlight potential research gaps.
• Citation Cloud: Constructs citation networks showing articles that cite a given article, articles it cites, co-cited articles, and bibliographically coupled works.

Scientific Applications:

• Literature review and meta-analysis: Provides detailed sorting, ranking, and summarization of PubMed results to support comprehensive reviews and meta-analyses.
• Research gap identification: Uses Mine the Gap! to detect under-represented topic pairs and guide formulation of new research questions.
• Citation analysis: Uses Citation Cloud-derived networks to assess influence, co-citation relationships, and bibliographic coupling among publications.

Methodology:

Applies advanced text mining techniques and statistical analysis to identify enriched co-occurring topic pairs, and employs a machine learning-based model to predict 50 publication types and study designs; constructs citation networks from PubMed citation data.