RightFind XML for Mining is the only full-text search solution that enables you to build indexes of full-text scientific literature from multiple publishers through a single source, giving you the most complete article collection for mining.
RightFind XML for Mining enables you to:
• Enhance search initiatives by fueling data processing pipelines with structured full text.
• Improve results by discovering entities and relationships that can be found only in full text.
• Build the most comprehensive article collection of full-text articles in XML format.
• Reduce manual processes associated with procuring content and rights for text mining.
• Simplify copyright compliance with access to millions of articles pre-authorized for commercial text mining.
• Improve search initiatives by fueling data processing pipelines with structured full text.
EARLY PHASE RESEARCH
Researchers can discover previously unknown biomarkers and drug targets that may be promising candidates for research.
• Mine Scientific literature to expose indirect connections (A affects B, B affects C, therefore A may be related to C) that can be found only in full-text articles.
• Reduce the costs of successful drug discovery by recognizing previously published negative results upfront.
• Speed discovery and save time by reducing noise and false positive search results.
Drug safety assessment and monitoring can be more comprehensive and deliver more precise results, focusing expert reviews on the most relevant issues.
• Mine full-text articles to suggest links between adverse events and pharmacological substances.
• Collaborate with colleagues on drug safety queries to achieve the right precision and recall.
• Receive automated updates of newly published literature related to drugs, drug classes, and their effects.
Competitor information can be monitored and mined across a broad landscape of published research.
• Enhance competitive intelligence initiatives by mining insights from full-text, non-patent literature.
• Collect and monitor competitive information across full-text articles and within sections of articles.