GeneCite

GeneCite performs systematic extraction and integration of biological facts from scientific literature, UniSTS sequence-tagged site data, and integrated pathway archives to enable high-throughput data mining and interpretation.

Key Features:

• High-Throughput Data Mining: Performs parallel high-throughput searches across multiple knowledge sources for comprehensive exploration of large datasets.
• Integrated Search Platform (IR): Queries online literature databases such as PubMed and the UniSTS sequence-tagged sites database.
• Data Retrieval (DR) and Pathway Mining: Mines an integrated archive of biological pathways contained within the software.
• Data Management System (DMS): Presents retrieved results in a spreadsheet-format output for downstream analysis.
• Real-Time Connectivity: Embeds real-time hyperlinks in each output cell linking directly to the corresponding online archive.

Scientific Applications:

• Bioinformatics: Aggregates and extracts factual information from literature and sequence databases to support computational analyses.
• Genomics: Integrates sequence-tagged site data from UniSTS with literature-derived facts for genomic investigations.
• Systems Biology: Mines biological pathway archives to inform network and pathway-level interpretations.
• Hypothesis Generation and Experimental Design: Compiles multi-source evidence to aid formulation of hypotheses and experimental planning.
• Validation of Scientific Findings: Facilitates cross-referencing of retrieved data with original archives to support validation of results.

Methodology:

Uses an integrated information retrieval platform to query online literature (e.g., PubMed) and sequence databases (e.g., UniSTS), a data retrieval system to mine integrated pathway archives, and real-time connectivity to link output cells to source archives.