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Updated Oct 6, 2021
The flexible cloud computing services offered by Microsoft Azure have pushed bioinformatics into the future. With the help of software product companies like GenUI, biotech companies are finding new ways to implement existing scientific knowledge, faster ways to make discoveries, and better ways to apply findings to real-world problems. With biotech-specific projects like Project Hanover and Microsoft Genomics, Azure is powering collaborative research leading to rapid biological advancements that help everyone.
In this article, we take a look at some recent innovations made possible by advanced Azure capabilities. With cloud agility and affordability, biotech projects are freed from traditional constraints of dataset size, storage availability, and compute capacity. They're rapidly creating new solutions that would have been impossible using older technology approaches.
01 Personalizing cancer treatments with machine reading
Advances in genomic technology have helped researchers discover the genetic mutations that drive cancer. Today, doctors can identify these mutations in their patients. Combined with the growing development of drugs that target specific mutations, doctors are now able to prescribe tailored treatments to cancer patients. This medical approach, known as precision medicine, is often more effective and has fewer side effects than previous therapies.
Precision medicine requires the compilation of knowledge from continually published medical literature. With the rapid growth of genetic discovery, reviewing literature for each patient is impractical without the assistance of artificial intelligence (AI). Microsoft’s Project Hanover supports this work by using machine reading to make Curation-as-a-Service (CaaS) possible. According to Microsoft, Project Hanover, “developed a general framework for incorporating diverse forms of indirect supervision to compensate for the lack of labeled examples, by combining deep learning with probabilistic logic.” This subverts the limits of standard machine learning (ML) that requires the meticulous annotation of examples.
Searchable databases created with Project Hanover offer healthcare providers a tool to find an exhaustive list of applicable research. After ML extracts knowledge from publications, expert human curators work to validate the information that has been flagged. This can be done through an assisted curation interface on Azure. In precision medicine, this means medical providers can apply up-to-date genetic research to individual patients in a timely manner. Our work has shown us how necessary Azure can be in wrangling critical data quickly.
The current state of precision medicine concerns doctors with single genes and highly targeted drugs. In the future, we believe specialists will be able to understand complex gene and treatment interactions aiding the selection of highly refined and personalized therapeutic strategies. We’re looking forward to being a part of that great work.
02 Discovering infectious diseases using AI
Next-generation sequencing (NGS), a high-throughput approach to DNA sequencing relying on massively parallel processing, has the potential to accelerate the detection and characterization of infectious diseases. However, the computational power required for analysis can hinder these compute-intensive genomic workflows. Cromwell on Azure, originally developed by the Broad Institute, is an open-source, cloud-based workflow management tool that applies the hyperscale compute capabilities of Azure, enabling infectious disease researchers to scale and automate genomic workflows. Cromwell dynamically provisions computing resources through Azure Batch and integrates with researchers’ Azure Blob Storage.
Coupled with AI, NGS facilitates precision disease detection and diagnosis, including infectious diseases like COVID-19. This method is quicker than other disease identification techniques such as culturing and polymerase chain reaction (PCR) tests. Through NGS, patients are guided to treatment at a faster rate leading to better health outcomes.
03 Managing in-vitro diagnostic medical devices remotely with IoT
As technological progress is made, medical professionals adopt new tools and devices that assist in diagnostics, treatment, and patient monitoring. In vitro devices (IVDs), such as blood sugar monitoring systems for diabetics or pregnancy tests, are used in abundance across the healthcare system to examine human biological specimens. With more units placed in circulation at medical facilities every day, servicing the many IVDs in use is time-consuming and expensive.
Through Internet of Things (IoT) technology, IVDs can be managed and monitored remotely. Using Azure’s IoT Hub and IoT solutions accelerator, companies can develop an IoT platform that supports their specific IVD data needs. IoT Hub provides a cloud-hosted backend to manage devices with a secure and reliable connection to IVDs in use. Bidirectional communication allows secure messages to be sent to connected devices and supports the collection of incoming data.
The IoT platforms used to manage IVDs collect near-real-time data like location and operational status. This supports the analysis of device usage and consumption of supplies, helping medical professionals have access to the tools they need at all times. As IoT platforms collect operational data from enough devices, predictive maintenance models will help avoid downtime.
The data collected from IVDs also have big picture applications. IVD developers can use this information to tailor offerings to healthcare organizations, provide better customer support, improve the performance and efficiency of IVDs, and develop products that meet new needs. Healthcare organizations can apply the data to organizational improvement and the understanding of healthcare trends. The overall result of applying IoT technology to IVDs is seen in the conveniences and efficiencies that lead to better patient outcomes.
One commonly overlooked aspect of IoT solutions is the user experience. At GenUI, our work with IoT platforms has demonstrated the importance of a quality user experience to enable people to make the best use of data.
04 Discovering genetic cancer risk factors through data lake analytics
Uncovering the link between human genes and inherited cancer risk factors would arm health care providers and patients with critical information to make smarter health decisions. Patients identified as being genetically at-risk for cancer would receive care tailored to their predispositions such as regular cancer screenings and cancer-specific counseling. The development of new treatments may also arise from the identification of risk factors.
Discovering genetic cancer risk factors requires the analysis of massive sets of big data that put storage and compute resources to the test. To pursue this line of study, genomics databases are provided by healthcare and research organizations for collaboration.
As new genomics data becomes available, researchers rely on Azure Data Lake Analytics to archive and analyze complex genome sequences. This service enables researchers to run massively parallel data transformation and processing programs on genomic data cost-effectively. Through this research, biotech organizations are putting genomics data collected from cancer patients to the best possible use.
05 Treating rare, inherited childhood diseases with big data sharing
Stretching the possibilities of genome sequencing through cloud computing has helped researchers more accurately identify structural DNA variations and analyze difficult to access parts of the genome. The availability of genomic data is crucial to understanding the genetic irregularities that contribute to childhood diseases, especially rare conditions.
Healthcare organizations are processing pediatric genomic data all the time, but resources for study are limited. We collaborate with organizations sharing research and medical data, and the impact of making data accessible to researchers cannot be overstated. Shared data is often the foundation of research that saves lives.
To further vital pediatric research, genomic datasets are being made available to the researchers who can do the most with it. The difficulty of distributing genomic data comes down to, not only the size of the datasets, but the format and security of the data. With Azure API for FHIR, organizations sharing pediatric genomic data can streamline collaboration by sharing data in the standard HL7 FHIR (Fast Healthcare Interoperability Resources) format.
This allows for the rapid exchange of protected health information to and from existing data sources like electronic medical records and research databases. Equipping pediatric researchers with data is the important first step in finding effective treatments for rare, inherited diseases.
What innovations are next?
Genomics, medicine, and biological research are all speeding forward as a result of cloud computing. Microsoft Azure makes the application of cloud computing straightforward, especially with the opportunities for collaboration. GenUI can help you leverage Azure to build on existing strategies or pioneer new applications. From designing a public database that promotes novel research to capitalizing on IoT technology to ensure healthcare device maintenance, Azure services can be used to achieve sustained scientific progress.
As product design and tech commercialization experts, at GenUI we work closely with biotech and related companies to make visionary ideas real using Azure and other modern technologies. Start a conversation with us or learn more about our Azure services here.