Healthcare and Life Sciences - bobbae/gcp GitHub Wiki
Healthcare API
https://cloud.google.com/healthcare-api/
Cloud Healthcare Data Engine
https://cloud.google.com/healthcare
Cloud Life Sciences
https://cloud.google.com/life-sciences
Cloud Healthcare API
The Cloud Healthcare API provides industry-standard protocols and formats for ingesting, storing, analyzing, and integrating healthcare data with cloud-based applications.
The API supports the following applications:
- Healthcare machine learning applications
- Data-level integration of healthcare systems
- Secure storage and retrieval of healthcare and life science data, including electronic protected health information (ePHI) and other forms of PII
For many applications, the Cloud Healthcare API can provide a cloud-based alternative to on-premises stacks implementing the following standards:
- Digital Imaging and Communications in Medicine (DICOM)
- Fast Healthcare Interoperability Resources (FHIR) DSTU2, STU3, and R4 standards
- Health Level Seven Version 2.x (HL7v2)
The Cloud Healthcare API simplifies data integration with existing systems and allows developers to focus on differentiating features, such as UX and intelligence.
There is no shortage of opportunities for clinical decision support and cognitive assistance in healthcare.
Benchmark on FHIR data streaming
https://vneilley.medium.com/most-fhir-servers-are-unusable-in-production-8833cb1480b1
HIPAA
Google Cloud infrastructure provides reliable information security designed to meet or exceed the requirements of HIPAA and protected health information. Covered by our HIPAA Business Associates Agreement and available via FedRAMP ATO for the National Cancer Institute Cancer Cloud Pilots.
https://www.cdc.gov/phlp/publications/topic/hipaa.html
FHIR
Fast Healthcare Interoperability Resources (FHIR) is a healthcare standard for representing and exchanging electronic medical information.
https://cloud.google.com/healthcare/docs/concepts/fhir
Stream and synchronize FHIR resources with BigQuery
https://cloud.google.com/healthcare/docs/tutorials/fhir-bigquery-streaming-tutorial
HL7v2
Health Level Seven International Version 2 (HL7v2) is a clinical messaging format that provides data about events that occur inside an organization.
https://cloud.google.com/healthcare/docs/concepts/hl7v2
DICOM
Digital Imaging and Communications in Medicine (DICOM) is an international standard used for medical images such as X-rays, MRIs, ultrasounds, and other medical imaging modalities.
https://cloud.google.com/healthcare/docs/concepts/dicom
Google Cloud Healthcare Data Protection Suite
https://github.com/GoogleCloudPlatform/healthcare-data-protection-suite
Life Sciences
Cloud Life Sciences
Cloud Life Sciences is a suite of services and tools for managing, processing, and transforming life sciences data.
Cloud Life Sciences (formerly Google Genomics) enables the life sciences community to process biomedical data at scale. Cost effective and supported by a growing partner ecosystem, Cloud Life Sciences lets you focus on analyzing data and reproducing results while Google Cloud takes care of the rest.
It also enables advanced insights and operational workflows using highly scalable and compliant infrastructure. Cloud Life Sciences includes features such as the Cloud Life Sciences API and extract-transform-load (ETL) tools, and more.
Deep Learning in Life Sciences
There are useful courses on Deep Learning in Sciences and Bioinformatics.
Variant Transforms tool
https://cloud.google.com/life-sciences/docs/how-tos/variant-transforms
Store raw VCF files in Cloud Storage
https://cloud.google.com/life-sciences/docs/how-tos/store-variants
Managing long-running operations
https://cloud.google.com/life-sciences/docs/how-tos/long-running-operations
Public datasets
https://cloud.google.com/life-sciences/docs/resources/public-datasets
Examples
Storing and loading genomic variants
This page describes how to use the Variant Transforms tool to transform and load VCF files directly into BigQuery for large-scale analysis.
Large-scale bioinformatics in the cloud with GCP, Kubernetes and Snakemake
Perform a large metagenomics sequencing experiment – 96 10X Genomics linked read libraries sequenced across 25 lanes on a HiSeq4000 in GCP.
Tutorials for the ISB-CGC Google Cloud Engine
This github repository is for the running workflows on the Google Cloud Platform for the ISB-CGC. The documentation at http://isb-cancer-genomics-cloud.readthedocs.org.
This Google genomics github account contains code samples and information about Google Life Sciences.
Github tutorials
https://cloud.google.com/healthcare/docs/tutorials/github