Conference Agenda

Treatment responses in oncology vary strongly across patients, and identifying actionable biomarkers of heterogeneous treatment benefit remains a central challenge in precision medicine. While traditional machine learning (ML) approaches provide limited insight into how therapeutic benefit differs between individuals, causal ML offers a principled approach to address this problem by estimating individual and subgroup-specific treatment effects from high-dimensional molecular and clinical data, enabling optimization of personalized treatment strategies. However, robust computational frameworks that combine systematic characterization of treatment effect heterogeneity with biologically grounded predictive biomarker discovery remain scarce. Here we present a multimodal causal ML framework for robust predictive biomarker discovery and subgroup refinement in randomized controlled trials (RCTs) with deep molecular characterization. Using state-of-the-art causal inference methods, treatment effects are estimated at the individual level and systematically analyzed to characterize treatment heterogeneity and identify clinically relevant predictive biomarkers and subgroups. To enable biologically informed discovery and clinically actionable results, the approach combines two complementary strategies: (1) bottom-up data-driven discovery of novel predictive biomarkers, and (2) top-down validation and refinement of clinically established biomarkers and domain-informed hypotheses. We demonstrate our framework using the randomized phase III FIRE-3 trial (AIO KRK-0306) in metastatic colorectal cancer (mCRC), integrating genomics, transcriptomics, and clinical data to identify molecular and clinical features associated with sensitivity or resistance to anti-EGFR (cetuximab) versus anti-VEGF (bevacizumab) therapy. The analysis enables biologically informed characterization of predictive biomarkers and systematic evaluation of clinically established and candidate subgroups in mCRC, including RAS mutation status and primary tumor sidedness, while uncovering additional sources of variation in treatment benefit to refine current patient stratification strategies. This work highlights the promise of causal ML to move toward data-driven, biologically informed personalized decision-making, providing a scalable computational strategy for systematic predictive biomarker discovery and subgroup refinement in RCTs, ultimately improving individual patient outcomes.

Numerical simulation of temperature and stress evolution in multi-physics problems is typically performed using physics-based methods such as the finite element method (FEM). While these methods provide accurate solutions, they are computationally expensive, especially for fine discretizations and large domains or repeated evaluations required in process optimization and design studies.

In this work, we investigate neural operator-based surrogate models as efficient alternatives for predicting thermo-mechanical fields in tempered glass. The proposed approach learns a parameter-to-field mapping, where a vector of process parameters is directly transformed into spatio-temporal temperature and residual stress distributions. Two neural operator architectures are analyzed: Multi-Input Fourier Neural Operators (MIFNO) and Multi-Input Operator Networks (MIONet). Both models incorporate multiple physical parameters as inputs and predict full 1D temperature and stress fields over space and time.

The models are trained using data generated from finite element simulations of the glass tempering process and evaluated on unseen parameter combinations. The results show that MIFNO achieves prediction errors of approximately 2.1% for temperature and 3.2% for stress on unseen or untrained cases, while providing a computational speed-up of about 22× compared to FEM. The MIONet model demonstrates even greater efficiency, achieving speed-ups of over 600× while maintaining prediction errors of about 1.1% for temperature and 3.5% for stress.

These results demonstrate that neural operator-based surrogate models provide a promising framework for efficient prediction of thermo-mechanical fields in glass tempering processes. Such models enable rapid parametric analysis and support real-time decision making and optimization in glass manufacturing and other industrial purposes.

Gradient high-performance liquid chromatography (HPLC), often coupled with mass spectrometry, is widely used to separate and identify small molecules in complex samples. During chromatographic separation, each compound travels through the column at a different rate and elutes at a characteristic retention time (RT), which provides complementary information for compound identification.

Predicting RT from molecular structure enables the integration of chromatographic information into computational analysis pipelines. However, RT depends not only on molecular properties but also on system-specific parameters such as column type, solvent gradient, and temperature. As a result, existing machine-learning approaches are typically restricted to a single experimental setup and require retraining or transfer learning to adapt to new chromatographic systems.

In this work, we employ the physical principles of liquid chromatography to create GRIP, a physics-informed deep learning model for retention time prediction across different chromatographic setups. The model uses a message-passing graph neural network to encode molecular structure and a feed-forward network to represent chromatographic conditions. Together they predict linear solvent strength (LSS) parameters describing the interaction of a compound with the mobile phase, which are then used in the fundamental equation of gradient elution to compute retention times.

We trained GRIP on 65 reverse-phase HPLC datasets spanning diverse column, gradient, and temperature conditions and evaluated its ability to generalize across chromatographic setups. The model demonstrates zero-shot prediction on previously unseen systems while matching or outperforming transfer-learning-based baselines fine-tuned with varying amounts of system-specific data. We further assessed generalization across chemical space using a similarity-based split that separates structurally related compounds between training and test sets. These results suggest that GRIP enables reliable retention time prediction across diverse chromatographic setups. This can support in-silico optimization of chromatographic methods by predicting RT under previously untested conditions to improve compound separation.

Recent advances in artificial intelligence (AI) increasingly rely on large-scale interaction data generated through the everyday use of digital tools. Beyond explicit data, such interactions may also capture patterns of tacit knowledge, including decision strategies, problem-solving heuristics, and domain-specific expertise. Tacit knowledge refers to forms of knowing that are not easily articulated or formalised (Polanyi, 1966; Collins, 2010). As AI systems learn from these interaction traces, elements of human experiential reasoning may become functionally represented within neural models.
This contribution examines the ethical implications of this emerging form of knowledge transformation. The analysis focuses on two contexts particularly relevant to AI-driven knowledge infrastructures. First, in engineering practice, prolonged interaction with AI-assisted workplace systems may embed the tacit expertise of domain specialists within organisational AI environments. Secondly, in scientific research, exploratory reasoning, early-stage hypotheses, and methodological ideas exchanged with large language models may enter platform infrastructures before they are formally published or attributed. In both contexts, interaction with AI systems may generate persistent representations of tacit knowledge that remain accessible to institutions or platform providers, often without explicit awareness or consent, even after the original contributor has lost control over them.
Existing regulatory frameworks provide only partial coverage of this issue: i) data protection law addresses identifiable personal data, ii) intellectual property regimes protect concrete works, and iii) labour law governs work products. None of these frameworks adequately captures human tacit knowledge expressed through interaction patterns with AI systems. Building on the concept of conditional shareability, the paper argues that tacit knowledge should be understood as neither purely private property nor an unrestricted common resource. If tacit knowledge is extracted at scale and replicated without consent, the economic and epistemic position of knowledge workers may become structurally precarious. The issue therefore extends beyond data governance to the question of whether individuals retain meaningful control over forms of knowledge that constitute their professional agency and identity.

Background: The synthesis of anonymized data derived from real-world cohorts offers a promising strategy for regulatory-compliant and privacy-preserving biological data sharing, potentially facilitating model development that can improve predictive performance. However, the extent to which generative models can preserve biological signals while remaining resilient to adversarial privacy attacks in high-dimensional omics contexts remains underexplored. To address this gap, the CAMDA 2025 Health Privacy Challenge launched a community-driven effort to systematically benchmark synthetic and privacy-preserving data generation for bulk RNA-seq cohorts (https://benchmarks.elsa-ai.eu/?ch=4).

Results: Building on this initiative, we systematically benchmarked 11 generative methods across two cancer cohorts (~1,000 and ~5,000 patients) over 978 landmark genes. Methods were evaluated across complementary axes of distributional fidelity, downstream utility, biological plausibility and empirical privacy risk, with emphasis on trade-offs between vulnerability to membership inference attacks (MIA) and other evaluation dimensions. Expressive deep generative models achieved strong predictive utility and differential expression recovery, but were often more vulnerable to membership inference risk. Differentially private methods improved resistance to attacks at the cost of reduced utility, while simpler statistical approaches offered competitive utility with moderate privacy risk and fast training.

Conclusions: Synthetic bulk RNA-seq quality is inherently multi-dimensional and shaped by trade-offs between utility, biological preservation and privacy. Our results indicate that differences in model architecture drive distinct trade-offs across these axes, suggesting that model choice should align with dataset characteristics, intended downstream use and privacy requirements. Privacy risk should also be assessed using multiple complementary attack methods and, where possible, formal differential privacy protection.

Keywords: Synthetic data generation, private data generation, RNA-seq, generative models, membership inference attack, reproducibility, evaluation metrics and trade-offs