Developing a Comprehensive Strategy for Implementing AI & Multi-Omics for Translational Research

Trends, Challenges, and Considerations Shaping Biomarker Development and Precision Medicine in 2026 and Beyond

The integration of artificial intelligence (AI) and multi-omics technologies is reshaping translational research, offering new approaches to drug discovery and precision medicine. In this dynamic landscape, biotech and pharmaceutical organizations are faced with the question of how to maximize the value of their investments in these tools while avoiding, or at least minimizing, costly missteps. The answer lies in understanding the innovation landscape, building a comprehensive strategy before generating expensive datasets, and developing the organizational readiness to act on findings—even when those findings point to the need for a change in direction.

In this article, we examine key trends, challenges, and considerations for organizations seeking to integrate AI and multi-omics into their translational research programs, drawing on perspectives from a panel of leaders across data science, clinical genomics, proteomics, biospecimen management, and biomarker-driven clinical trial design and execution, including:

• Deb Phippard, Chief Scientific Officer, Precision for Medicine
• Philippe Menu, EVP, Chief Medical Officer and Chief Product Officer, SOPHiA GENETICS
• Heng (Tony) Qian, Head of Data Science (North America), Olink, Part of Thermo Fisher Scientific
• Rob Fannon, Head of Biospecimen Solutions, Precision for Medicine

Starting with a Strategy Ahead of Your AI and Multi-Omics Investment

Multi-omics have made it technically feasible to interrogate biology at unprecedented depth, from whole exomes and high-plex proteomics to single-cell spatial maps. However, the datasets generated through these approaches are expensive to produce and applying AI‑driven analytics to them without a clear strategic framework can expose substantial investment with limited actionable outcomes. Rather than defaulting to broad, high‑throughput data generation, organizations are best served by defining their translational objectives before committing to extensive omics programs.

A strategy‑first approach begins with a clear understanding of the translational research questions to be answered and the downstream decisions the data are intended to inform. This includes aligning biological hypotheses with disease context and drug mechanism of action, as well as clarifying whether a given dataset is being generated to support indication selection, patient stratification, dose optimization, or go/no‑go decisions during clinical development.

A strategy-first approach involves:

• Understanding the current innovation landscape, including new or emerging technologies, data platforms, and AI and multi-omics integration methodologies
• Gaining insight into disease context and drug mechanism of action to identify plausible biological hypotheses
• Clarifying the decision this data will inform, whether it is indication, patient selection, dose, or even go/no-go
• Determining what can realistically be collected from patients, at which time points, and under which regulatory constraints

Developing a fit-for-purpose translational strategy allows organizations to select the right modalities and design operationally feasible workflows, leveraging the power of less to perform the right multi-omics in the right patients at the right moments, instead of defaulting to maximal, unfocused data collection.

As AI and multi-omics technologies mature, a strategy-first approach will also require organizations and the industry as a whole to develop comfort in making go/no-go decisions when data reveal answers that validate or redirect clinical priorities. This disciplined approach to implementing AI and multi-omics in translational research will become increasingly important as insights derived from omics data shift drug development toward more targeted therapies for smaller patient populations. Now that diseases, such as lung cancer, which were once treated as single entities are now being subdivided into molecularly distinct subtypes that are functionally rare diseases, it is incumbent upon biotech and pharmaceutical companies to focus their development efforts on those groups that are most likely to benefit. Thus, organizations that establish clear translational objectives before committing to extensive omics investments are better positioned to extract more value from their data.

Why Data Quality Determines Whether AI and Multi-Omics Are Actionable

The “garbage in, garbage out” tenet is central to successful multi-omics implementation. While AI tools can identify patterns and associations that escape human observation, the interpretability and actionability of those findings remain highly dependent on the quality and characterization of the input data.

This underscores the need for a biospecimen strategy that provides detailed specifications for any variable that can affect downstream data quality, from sample type and collection kit to processing and handling protocols. For example, in oncology studies, running assays on samples with variable tumor percentages can complicate interpretation if tumor content is not measured and accounted for. When tumor cellularity or tumor area is quantified up front, analytical approaches such as weighting or normalization can be applied to compensate for this variability, whereas unmeasured variability can lead to misleading conclusions regardless of analytical sophistication.

AI-powered pathology tools can be used to automate quantification of tumor cell content prior to omics assessments helping to ensure that variability is explicitly incorporated into downstream analysis.

At the analytical level, the high dimensionality of multi‑omics data introduces additional considerations for rigor. As datasets are interrogated repeatedly across thousands of features, the likelihood of identifying statistically significant but biologically meaningless correlations increases. Without a clearly defined analysis plan established before the first interrogation of the data, it can be difficult to distinguish hypothesis‑generating observations from signals that are sufficiently robust to inform translational or clinical decisions.

Meanwhile, the FAIR—findable, accessible, interoperable, and reusable—principles provide a useful framework for AI readiness and multi-omics data management. Ensuring that multi-omic datasets conform to these principles facilitates integration and interrogation across modalities and enables retrospective analyses that can rescue value from trials with unexpected or even failed outcomes.

Where AI and Multi-Omics Deliver the Most Translational Research Value

The value of multi-omics lies in its ability to provide more holistic views of disease biology:

• Genomics identifies inherited and acquired alterations
• Transcriptomics reveals gene expression patterns
• Proteomics measures functional protein levels, which are closer to phenotype
• Emerging modalities such as spatial biology and digital pathology add essential tissue context

When signals converge across multiple platforms, confidence in biological relevance and predictive accuracy increases. Published research has shown that rigorous target validation using multi-omic technologies in cell lines and tissues accelerates drug development and enhances the likelihood of clinical success.¹ These advantages stem from the ability to validate findings across orthogonal data types, pushing translational teams to design biomarker studies that incorporate multiple omics layers when resources permit.

However, integration presents practical challenges since data from different platforms often require different normalization approaches, operate at different biological scales, and may be collected at different timepoints. Establishing data infrastructure and analytical workflows that can manage these complexities will be essential.

AI-powered analysis is integral to realizing the full potential of multi-omics data. Pattern recognition algorithms can identify candidate biomarkers from high-dimensional datasets that would overwhelm manual analysis. Machine learning approaches can accelerate the connection between disease biology and clinical endpoints by identifying predictive signatures from complex multi-omic inputs. Moreover, AI and machine learning techniques can be applied to simplify multimodal datasets by transforming high-dimensional data into a lower-dimensional space, making it easier to distinguish reproducible biological signals from statistical noise and enabling more informed prioritization of biomarker candidates for validation.

How Real-World Data and Collective Intelligence Expand What Trials Can Explain

Real world data (RWD) offers a necessary complement to clinical trial data, which is derived from carefully selected, controlled groups and may not represent the broader patient population. In addition, treatment landscapes often shift between trial design and drug launch, limiting the applicability of historical comparators, and study endpoints may not reflect routine clinical practice.

RWD enables hypothesis generation from observed outcomes in diverse patient populations, including those that contradict expectations. For example, real-world treatment patterns demonstrate that subsets of patients with EGFR-mutated lung cancer receive immunotherapy and achieve prolonged disease control despite trial data suggesting poor efficacy. Such unexpected observations are not noise, but signals worth investigating through multimodal analysis. Rather than dismissing these findings as data anomalies, organizations can use them as starting points for AI to uncover biomarkers or signatures that explain why some patients are not expected to respond do.

The concept of collective intelligence takes RWD one step further. Individual investigators and institutions accumulate limited experience with any given clinical scenario. By pooling data across institutions while maintaining appropriate privacy protections, organizations can identify patterns that no single center alone could detect. Network-based platforms such as SOPHiA GENETICS that enable cross-institutional learning while keeping data generation decentralized and local represent one approach to leveraging collective intelligence.

How Do Multi-Omics Really Move from Exploration to Go/No-Go Decisions?

A key challenge of translational research is moving multi-omic biomarker findings from exploratory readouts to embedded decision-making tools that shape trial design and drug development. This transition involves navigating regulatory requirements for screening assays and companion diagnostics (CDx), which differ across geographies. The pathway typically involves progressive validation, where a broad net is cast in Phase 1 studies to identify promising signals that are then confirmed with different technologies and in independent cohorts before being developed into standardized assays suitable for regulatory submission. Each step requires thoughtful design to build the evidence package needed for regulatory acceptance.

Looking to the future, a watershed moment will arrive when a later-phase trial randomizes patients based on a multimodal, AI-derived signature, or digital CDx. Development of this signature will likely require iterative cycles of retrospective discovery, prospective investigation as an exploratory endpoint, and eventual elevation into an on-trial decision-making tool. While the timeline for this milestone remains uncertain, accumulating experience with multimodal biomarker strategies in earlier-phase trials is building the foundation for this transition.

Practical Starting Points for AI and Multi-Omics Today

Currently, one of the highest-impact applications for AI and multi-omics is in early-phase clinical development, where the primary objectives are effective patient stratification, insight into mechanism of action, and biomarker hypothesis generation. These settings offer the flexibility to explore multiple omics modalities while building the evidence base needed to justify larger investments in later-phase programs.

Current strategies for implementing AI and multi-omics emphasize selective depth guided by biology and decision needs, with the goal of extracting maximal insight from limited biospecimen material and modest study population size. From the Precision for Medicine perspective, prioritizing assays that can be translated into scalable, cost-effective CDx is critical to success.

Organizations considering implementation of AI and multi-omics should assess:

• Biospecimen collection and processing capabilities aligned with downstream assay requirements
• Computational infrastructure for handling large multi-omic datasets
• Data governance frameworks that enable responsible data use
• Analytical expertise, whether in-house or through partnerships
• Integration pathways that connect omics findings with clinical development decision-making

Establishing internal AI and multi-omics capabilities requires substantial investment in infrastructure, talent, and ongoing maintenance. Partnering with expert service providers offers access to accumulated experience and established workflows, potentially reducing both cost and time to insight. Using specialized platforms such Olink and SOPHiA GENETICS for multi-omics and multimodal analytics and integrated biospecimen and translational solutions such as Precision for Medicine avoids reinvention and reduces infrastructure burden.

What Organizations Must Get Right to Realize Value from AI and Multi-Omics

Realizing the potential of AI and multi-omics demands strategic clarity about research objectives, rigorous attention to data quality, and organizational readiness to act on findings. To best position themselves to extract value from these technologies, organizations must build biospecimen strategies aligned with multi-omic requirements, establish data infrastructure capable of handling integrated analyses, develop cross-functional alignment between translational research and clinical development teams, and maintain the flexibility to pursue or abandon directions as data dictates.

Beyond better biomarkers, the promise of AI and multi-omics lies in redesigning translational and clinical development paradigms. While AI-based approaches are already being applied broadly to operational aspects of clinical development such as site selection, patient identification, and recruitment monitoring, adoption and activation of multi-omics signals as strategic decision-making tools for portfolio prioritization and go/no-go determinations is still a frontier. As organizations build capability and confidence, implementation of AI and multi-omics will accelerate drug development and enable more precise therapeutic strategies.

Learn more about shaping future-ready strategies using AI and multi-omics.

Reference

1. Hayes CN, Nakahara H, Ono A, Tsuge M, Oka S. From omics to multi-omics: a review of advantages and tradeoffs. Genes (Basel). 2024;15(12):1551. doi:10.3390/genes15121551