Life Science Data | Why Big Data, AI & Analytics Matter

Greg Mazzu. November 30, 2021
Life Science Data | Why Big Data, AI & Analytics Matter

Are you wondering about life science data? We explain the trends in life science data, why it’s essential, and the role of AI and big data in life science data analytics.

What is life science data?

Life science refers to the study of living organisms, including microbes, humans, animals, plants, and fungi. Life science generates massive amounts of data, which can be extracted and analyzed. Life science data is used research such as:

  • Disease identification
  • Drug discovery
  • Clinical trials
  • Genome sequencing

How Does Cloud Computing Play a Role in Life Science Data Research?

“Life sciences” as an area of study is rather broad. In general, when data scientists refer to life sciences or life science data, they are most often referring to one or more of the following areas of study:

  • Genomic Sequencing: DNA sequencing is an exciting area of medical and biological research that hopes to plumb the depths of the human genome to inform how we treat disease. Sequencing DNA is a huge computational task, however, and its feasibility as a point of research has only recently been realized through high-performance cloud platforms.
  • Pharmaceutical Trial Analysis: Drug companies and healthcare providers run clinical trials daily, with immense amounts of results to sift through. Cloud platforms help unlock this data much more rapidly than humans can do while at the same time providing unique insights into the data by recognizing the smallest of patterns.
  • Patient Diagnosis Analysis: Cloud AI is powering how doctors diagnose diseases through a combination of analyzing patient records, population health information and long-term health patterns.
  • Disease Tracking and Modeling: As the COVID-19 pandemic has taught us, our population is vulnerable to diseases that strike our most vulnerable populations. Modeling disease transmission and spread is increasingly complex and challenging, and cloud analytics contribute to efforts to help biomedical experts better understand current and future epidemics.
  • Patient Treatment Personalization: One of the losses that modern medicine has recognized is that it is increasingly difficult to provide personalized and tailored healthcare for individuals. Cloud analytics provide insights and suggestions based on behavioral data and records to suggest personalized treatments for patients, alleviating the pressure on doctors and nurses to manage their increasing patient loads.
  • Optimizing Analysis of Medical Documents: AI and machine learning algorithms learn how to read medical objects like scans, test results and other imaging. With cloud platforms and AI, doctors are getting additional support through predictive analytics to help recognize problems that might escape the eye of even the most well-trained doctor.

Traditionally, these practices were driven by human experts managing data to make decisions and derive scientific hypotheses about medicine and biology. As is the case in other industries, managing that data effectively and accurately during the rise of digital technologies and cloud platforms became impossible for medical experts. Not only was the volume of data too much for any human to digest accurately but expecting these professionals to spend time analyzing data meant taking time away from research and patient care.

The evolution of cloud computing, modern analytics and machine learning and AI technologies changed the shape of life science data analysis in a few key ways:

  • It facilitated the collection and orchestration of medical and scientific data. Cloud platforms equipped with the right software and hardware automate the ingestion of data from various sources through a variety of media to make it accessible to data scientists.
  • It empowered analytics and machine learning. Some of the more complex life science workloads (like genomic sequencing) call for high-performance computing to manipulate data and make sense of it. Not only does the cloud provide scalable computation for these tasks, but it also powers advanced algorithms that can aid medical and scientific experts in making sense of that data.
  • It created a scalable platform for expanded work. Speaking of scalability, cloud computing provided life science researchers with the scalability and elasticity they need to manage their workloads. The cloud can scale or pull back on required storage or computing resources rapidly to support evolving needs.
  • It optimized efficiency in research, development and treatment. Efficiency is not something that doctors and nurses should necessarily concern themselves with above the well-being of their patients. But cloud-driven AI can help provide insights into optimal procedures for providers, insurers and administrators to reduce costs in terms of finances and time, streamlining care and business operations simultaneously.

The Challenges of Life Science Data and the Cloud

While these innovations are remarkable and revolutionizing healthcare, the processing and storage of life science data aren’t without their challenges.

Some of the issues that arise when working with life science data include the following:

  • Security: The Health Insurance Portability and Accountability Act (HIPAA) dictates strict privacy, security and ethical regulations for organizations handling Personal Health Information (PHI). Cloud platforms are not an exception to this, and anyone processing life sciences data could run into strict compliance demands to which your infrastructure must adhere.
  • Ethics: PHI is an incredibly private part of most people’s lives. Beyond protecting that information from theft or unauthorized disclosure, you must also consider the ethical implications of using it to perform research. While many life-saving treatments have emerged from this kind of work, it’s also the case that an organization could abuse its position by using this data for personal financial gain.
  • Performance: Computationally intensive workloads in life sciences call for high-performance cloud environments, and not every platform delivers. A poorly performing infrastructure can hinder or halt important research.
  • Collaboration: One of the more significant advantages of cloud computing for life sciences is that it allows researchers worldwide to collaborate. With the correct data availability, a research project can call on the combined expertise of dozens of doctors, data scientists and life scientists. However, if the platform isn’t configured to accomplish this task effectively, you will find that one of the cloud’s biggest strengths isn’t playing a role in your project.
  • Scalability: Cloud environments are supposed to scale. Strategic cloud bursting, hybrid environments and high-performance infrastructure can empower that scalability. They don’t do this by default; however–they need experienced engineers and leadership to make them run. You need the right experts to ensure that your cloud is actually working for you and your project.

Revolutionizing Life Science Workloads with WEKA

A constant need we’ve identified with our clients in biomedicine and life science research is readily available and high-performance computing that can power advanced machine learning and analytics while making data accessible to their distributed teams. That’s why we’ve built WEKA to power the most intensive workloads. From data collection and transmission to processing, storage, and access, WEKA provides advanced capabilities to researchers and scientists who change how we think about medicine and science.

WEKA is cloud-native, with the elasticity required to help manage resource usage. Our platform includes the critical features to scale your projects up or down.

These features include the following:

  • Autoscaling storage for high-demand performance
  • On-premises and hybrid-cloud solutions for testing and production
  • Industry-best, GPUDirect Performance (113 Gbps for a single DGX-2 and 162 Gbps for a single DGX A100)
  • In-flight and at-rest encryption for GRC requirements
  • Agile access and management for edge, core, and cloud development
  • Scalability up to exabytes of storage across billions of files

If your organization is developing complex and revolutionary research into the life sciences, then we would love to support you in your mission. Contact us today!

Additional Resources

HPC for Life Sciences
AI-based Drug Discovery with Atomwise and Weka on AWS
Accelerating Genomic Discovery with Cost-Effective, Scalable Storage
Accelerating Discovery and Improving Patient Outcomes With Next-Generation Storage
How to Analyze Genome Sequence Data on AWS with WekaFS and NVIDIA Clara Parabricks
Top 5 Myths in HPC for Life Sciences

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