Edge Computing for Genomics, Bioinformatics and Proteomics
March 20, 2023
Since the rise of big data in the medical and life sciences fields, researchers have had enormous amounts of information at their disposal. The challenge has been finding innovative ways to apply data science and analytics to mine this data for useful and actionable insights.
High-performance edge computing devices can bring AI to these disciplines efficiently and securely. In turn, biopharmaceutical researchers will be able to accelerate their efforts and launch novel drugs based on the latest biomedical data available.
Read on to discover more about the importance of AI and edge computing for life sciences.
What Are the Different Types of Life Sciences?
Life sciences is the study of biology, and the field of biotechnology applies this research to the development of products that harness cellular and biomolecular processes to improve health outcomes. Many biopharmaceutical companies are combining biotechnology and pharmaceutical manufacturing to prevent, relieve, or treat diseases.
Genome sequencing on a massive scale with The Human Genome Project and the subsequent Human Proteome Project have generated an enormous amount of biological data and launched several new fields of research. These emerging fields of life sciences are:
- Genomics: The study of the genome of an organism, which can potentially offer new therapies for treating many diseases that might be linked to a person’s genes or DNA.
- Proteomics: The study of the proteome or all the proteins of an organism, which could reveal certain biomarkers for diagnostic purposes or help with developing novel drugs.
- Bioinformatics: The use of mathematics, statistics, and computer programming to collect and analyze biological data. Gene sequencing and other bioinformatics processes have greatly enhanced genomic and proteomic research.
How AI Can Transform Life Sciences
There’s no shortage of data for life sciences and pharmaceutical research, but traditional data analytics methods are slow and expensive. Manual data analysis and other inefficiencies are limiting the potential for biopharmaceutical companies to bring life-changing drugs and treatments to market.
Machine learning can uncover insights in massive life sciences datasets that might not have been possible using traditional analytics methods. In fact, machine learning algorithms can be applied to a broad range of complex biomedical data — such as imaging, genomics, and clinical information — to identify new medical trends and targets for novel drugs. This can accelerate clinical trials and drug discovery while also reducing research and development costs.
The Advantages of Edge Computing for Life Sciences
The performance potential for AI and machine learning solutions in life sciences can vary depending on whether they’re implemented using cloud or edge inferencing. This is the difference between sending data to the cloud for processing, or analyzing this data locally where it’s collected.
Cloud-based solutions require the data to be transferred over the Internet for AI inferencing, which isn’t as efficient or secure as edge inferencing. This approach greatly increases the time and resources involved with generating AI insights from massive datasets. Transferring sensitive clinical data over the Internet also introduces the risk of it being intercepted by unauthorized third parties.
Shifting AI inferencing to the edge is more efficient and secure because the data is processed locally within embedded devices or within the intranet using edge servers. This approach eliminates bandwidth constraints because the data doesn’t need to be transferred anywhere for processing. It also keeps sensitive research data within local intranet, eliminating the risk of man-in-the-middle or other cybersecurity threats.
Delivering Edge AI Devices for Life Sciences
Despite the advantages of edge computing in life sciences, many developers have previously faced technological constraints when implementing edge inferencing. However, the challenges with medical AI and edge inferencing are decreasing as hardware technology becomes faster and cheaper. This has made it more economical to implement inferencing within edge devices and embedded systems.
MBX is a hardware specialist that can help biopharmaceutical companies deliver AI-powered devices for medical and life sciences use cases. We offer hardware building blocks purpose-built for genomics, proteomics, and bioinformatics that can streamline the development of new life sciences solutions. Our approach balances the use of pre-designed hardware and customization to bring time and cost efficiencies to the device manufacturing process.
In turn, biopharmaceutical companies can accelerate the discovery of novel drugs and medical breakthroughs. This will not only lead to new treatments reaching patients faster, but also help reduce the costs of healthcare. MBX can guide biopharmaceutical companies in adapting to an AI-centric future and change the healthcare industry for the better.
Learn more about edge computing in healthcare within our recent solution brief: The Shift from Cloud Inferencing to Edge Inferencing.