Biotech’s Future: How Machine Learning Revolutionizes Life Sciences
The biotech industry is on the brink of a revolution as it begins to embrace the power of machine learning. Over the next two decades, a more multidisciplinary and data-intensive approach to life sciences will transform our understanding of and ability to manipulate living matter. However, the lack of data sophistication is currently limiting the industry’s potential for rapid advancement. The longer we wait, the further behind we’ll fall.
The challenges faced by biotech in adopting machine learning tools are primarily related to data collection, information silos, and the industry’s biology-first approach, rather than a software-first mindset. Yet, when addressing complex systems-level challenges, like those in biotech, the adoption of machine learning is unavoidable if we want to achieve scale and cost-efficiency over time. Business leaders who fail to understand this will find themselves trailing behind those who embrace data science at the core of their biotechnology companies.
The abundance of uncategorized and scattered data across biotech is growing rapidly. In the coming years, exabytes of data will be generated within the biological sciences as numerous experiments are conducted to prove concepts. Software has proven to be more efficient than humans when it comes to repetitive experiment types or analysis. Yet, the industry lacks mature data collection and management standards, and this needs to change.
The more software the biotech industry utilizes, the more data it can collect. This data can then be analyzed using machine learning algorithms, leading to new discoveries and inferences that may not have been the intent of the experiments. Additionally, hypothesis-free tests can generate scientific insights that may have otherwise remained undiscovered. The sheer volume of data available is staggering.
Cost and effort reduction are crucial in biotech, where experiments can cost millions of dollars, and tens of thousands of experiments are needed to achieve scale. Augmenting biological trials with machine learning workflows, including software that can handle complex datasets, is essential.
Machine learning’s ability to study fundamental aspects and relationships within biotech data paves the way for new concepts to come to life. The software industry has started supporting these efforts, such as Nvidia’s launch of the BioNeMo Large Language Model (LLM) service. Early results from this collaboration with Evozyne show the potential in accelerating protein discovery.
Biotech companies can adopt software strategies to improve their machine learning capabilities today. Three tested applications that have shown promise include utilizing machine learning with large sets of biological data to identify similarities in gene expression, automating the tracking and quality assessment of cells to enhance analysis throughput and viability, and leveraging machine learning solutions for conservation work.
While biotech companies do not need to create cutting-edge data solutions, they must adopt mature data collection and management standards. By prioritizing technology advancements, elevating computer scientists to leadership positions, and creating collaborative learning environments, biotech can meet the demands and opportunities of the next twenty years.
Failing to adopt the most urgent and promising technologies means that we won’t be able to address the challenges posed by disease, climate change, and health conditions within our lifetimes. This is unacceptable when we have existing solutions at our disposal.
Now, it is crucial for enterprise decision-makers to encourage, fund, and build enterprise-wide machine learning solutions within the biotech industry. This requires prioritizing technology adoption in budgeting, integrating computer scientists into traditional scientific teams, and enhancing IT infrastructure. By doing so, biotech can transition rapidly and seize the opportunities that lie ahead.
