AWS Public Sector Blog
Author: Jeremy Ng
Dr. Jeremy Ng is a senior bioinformatician in the Division of Pathology at Singapore General Hospital (SGH). He completed his doctoral training at the National University of Singapore, where he studied the determinants of transforming growth factor-beta signaling (TGFB) outcome. He has been with SGH since September 2020, working in the oncology space.
Leverage generative AI for biocuration using Amazon Bedrock and Amazon Nova foundation models
Personalized therapy for diseases such as cancer utilizes an individual’s unique genomic profile to guide treatment decisions. However, the effect and clinical significance of most genetic variants are uncertain. Accurate classification of the clinical significance of novel genetic variants requires extensive curation of peer-reviewed biomedical literature. In recent years, generative AI has demonstrated promising results in information extraction and text summarization. In this post, we explore how various AWS-native solutions can be used to create a secure, retrieval-augmented, and cost-effective biomedical chatbot designed to facilitate biocuration.
Building a secure and low-code bioinformatics workbench on AWS HealthOmics
Singapore General Hospital (SGH), SingHealth Office of Academic Informatics (OAI), and Amazon Web Services (AWS) collaborated to develop a cost-effective, scalable cloud infrastructure that enables researchers to perform their own analyses on a centrally secured and compliant cloud platform. AWS HealthOmics offers a suite of services that help bioinformaticians, researchers, and scientists to store, query, analyze, and generate insights from genomic and other biological data. Read this post to learn more about the three primary components of HealthOmics used in the solution.