Media | January 5, 2026
Accelerating Biomarker Discovery with AI-Enhanced Omics
Get the Resource
Read the ArticleAnalytical science is generating molecular data at unprecedented scale, raising new questions about how best to make sense of it all. AI-driven multi-omics is increasingly part of that conversation, using artificial intelligence to enhance how researchers process, integrate, and interpret molecular information. These advances are ushering in a new era for analytical science – one in which interpretation, rather than data generation, becomes the defining challenge.
We recently sat down with The Analytical Scientist to delve deeper into the topic of AI-driven multi-omics, and specifically how evolving mass spectrometry workflows paired with AI, both for data processing and to uncover meaningful patterns in that data, is shaping the next generation of efficient, successful drug development research.
Read an excerpt from the full feature below.
Q: When we talk about “AI” in an analytical science context, what are we actually referring to?
A: There is certainly some confusion, especially with the explosion of generative AI tools that dominate headlines today. At its core, AI is about training computer systems to perform complex, repetitive tasks with high fidelity – and analytical sciences are rife for those kinds of tasks. With today’s technologies, we’re generating data at an unprecedented scale – tens, hundreds, even a thousand times more than just a few years ago. The challenge isn’t about generating data anymore; it’s about making sense of it.
Take mass spectrometry as an example. With newer high-throughput systems we can now measure tens of thousands of molecules, from proteins to metabolites to lipids, in a single biosample – and do so across thousands of samples at a time. But after that data is collected, you need to process enormous spectral files, remove noise, align peaks, perform quality control, and format everything for analysis. This kind of pipeline is exactly where AI is already having a huge impact. Thanks to advances in cloud computing, distributed systems, and now generative AI, we can now process and prepare complex datasets orders of magnitude faster than ever before. It may not be the flashiest application of AI – no robots, no rocket launches – but it is an incredibly practical, impactful use case that is transforming how we approach discovery.
And once the data is cleaned and structured, then comes the fun part: the interpretive layer. We can use AI to start asking deep biological questions, such as: Who is likely to develop a specific disease over the next decade? Who will or won’t respond to a given therapy? Or what is the best drug target? Finding answers to these kinds of questions requires analysis of multi-dimensional datasets, encompassing thousands of measurements across thousands of individuals and many timepoints. Generative AI can now help us uncover patterns in this sea of information and translate it into actionable biological insight, allowing for true AI-driven multi-omics biomarker discovery.
Q: What do you see as the most promising areas where AI can transform how MS data are analyzed and interpreted?
A: One of the most exciting opportunities for AI in mass spectrometry is its ability to help us move beyond overly simplistic models of disease. For a long time, we’ve operated under the “one gene, one protein, one disease” framework – but that is more mythology than science. In reality, even so-called “single-gene disorders” are influenced by numerous modifier genes and environmental factors.
When we approach disease by measuring a single biomarker and attempting to develop a drug around it, we leave an enormous amount of information untapped. It’s like trying to solve a jigsaw puzzle with only a fraction of the pieces; it doesn’t matter how good you are at putting the pieces you do have together – you’ll never achieve the full picture.
AI-driven multi-omics changes that.
…