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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Gene Teller: an extensible Alexa Skill for gene-relevant databases.

Jon D Hill1

  • 1Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT 06877, USA.

Bioinformatics (Oxford, England)
|August 22, 2020
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Summary
This summary is machine-generated.

Gene Teller is a voice assistant that allows scientists to easily access gene information. This tool leverages voice technology for intuitive biological data querying, enhancing research accessibility.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Voice assistants are prevalent in consumer electronics.
  • Biological research data access remains underutilized by voice assistants.

Purpose of the Study:

  • To develop an intuitive voice assistant for querying gene-centric biological data.
  • To enhance accessibility of biological research information for scientists.

Main Methods:

  • Developed Gene Teller using Alexa Skills Kit and Amazon Lambda functions.
  • Implemented features like synonym disambiguation and short-term memory for natural conversation.
  • Utilized Amazon Simple Storage Service and AWS Lambda for a scalable and cost-efficient architecture.

Main Results:

  • Gene Teller provides an intuitive interface for accessing gene information.
  • The system supports natural conversational interactions.
  • The architecture is cost-efficient and scalable.

Conclusions:

  • Voice assistants can be effectively utilized for accessing biological research data.
  • Gene Teller offers a novel and accessible method for gene information retrieval.
  • The platform is extensible for future integration of new biological resources.