Zika

Zika compiles and integrates molecular interaction and pathophysiological data to support analysis and hypothesis generation about Zika virus (ZIKV) biology.

Key Features:

• Rapid compilation and integration: Uses generic knowledge management software for literature mining, expert knowledge curation, data integration, reporting, and visualization.
• Collaborative expertise: Employs a multidisciplinary team of clinicians, virologists, bioinformaticians, and knowledge management specialists operating under a predefined workflow for evidence integration and evaluation.
• Structured molecular interaction maps: Constructs comprehensive molecular interaction maps from curated literature and integrates them with existing knowledge resources.
• Predictive modeling and hypothesis generation: Transforms textual information from publications into complex networks to suggest novel hypotheses, predict drug efficacy, and identify host factors and signaling pathways affected by ZIKV.

Scientific Applications:

• Computational data analysis: Supports computational analysis to derive mechanistic insights into ZIKV biology.
• Hypothesis generation and gap identification: Enables identification of knowledge gaps and generation of testable hypotheses from integrated literature-derived networks.
• Predictive modeling for therapeutics: Facilitates prediction of drug efficacy and prioritization of host factors and signaling pathways for experimental follow-up.
• Informing infectious disease research: Provides structured, aggregated knowledge to inform research and response efforts for ZIKV and related infectious diseases.

Methodology:

Automated literature mining and analysis, expert knowledge curation, transformation of textual information into molecular interaction networks, and data integration, reporting, and visualization using generic knowledge management software.