Life Science — Scientific Literature Review Automation Pipeline

Overview

The purpose of this DAG is to streamline the review of scientific literature by automating the ingestion, normalization, and synthesis of data from various sources. It ingests scientific corpora from databases and internal documents, ensuring a comprehensive collection of relevant literature. The architecture includes a data ingestion pipeline that normalizes the incoming data and extracts key entities to create a structured taxonomy. Multi-level syntheses are generated, which are then validated

The purpose of this DAG is to streamline the review of scientific literature by automating the ingestion, normalization, and synthesis of data from various sources. It ingests scientific corpora from databases and internal documents, ensuring a comprehensive collection of relevant literature. The architecture includes a data ingestion pipeline that normalizes the incoming data and extracts key entities to create a structured taxonomy. Multi-level syntheses are generated, which are then validated by subject matter experts (SMEs) to ensure accuracy and relevance. The outputs of this process are disseminated via a continuous monitoring portal, which is equipped with configurable alerts to track new publications in real-time. Quality control measures are implemented throughout the process to ensure the traceability of citations and adherence to documentation standards. Key performance indicators (KPIs) are monitored to assess the effectiveness of the literature review process, including the number of citations tracked, the frequency of updates, and user engagement metrics. The business value of this DAG lies in its ability to accelerate the literature review process, enhance the quality of research outputs, and support informed decision-making in the life sciences sector.

Part of the Knowledge Portal & Ontologies solution for the Life Science industry.

Use cases

• Accelerates the literature review process for researchers
• Enhances the quality and reliability of research findings
• Supports timely decision-making with real-time alerts
• Improves compliance with documentation standards
• Facilitates collaboration among researchers through shared insights