Biology subjects

Leonardi, G.

Publications and source records attributed to Leonardi, G..

3 recordsLinked to original sources

bioRxiv2025

Coherent Cross-modal Generation of Synthetic Biomedical Data to Advance Multimodal Precision Medicine

Integration of multimodal, multi-omics data is critical for advancing precision medicine, yet its application is frequently limited by incomplete datasets where one or more modalities are missing. To address this challenge, we developed a generative framework capable of synthesizing any missing modality from an arbitrary subset of available modalities. We introduce Coherent Denoising, a novel ensemble-based generative diffusion method that aggregates predictions from multiple specialized, single-condition models and enforces consensus during the sampling process. We compare this approach against a multi-condition, generative model that uses a flexible masking strategy to handle arbitrary subsets of inputs. The results show that our architectures successfully generate high-fidelity data that preserve the complex biological signals required for downstream tasks. We demonstrate that the generated synthetic data can be used to maintain the performance of predictive models on incomplete patient profiles and can leverage counterfactual analysis to guide the prioritization of diagnostic tests. We validated the frameworks efficacy on a large-scale multimodal, multi-omics cohort from The Cancer Genome Atlas (TCGA) of over 10,000 samples spanning across 20 tumor types, using data modalities such as copy-number alterations (CNA), transcriptomics (RNA-Seq), proteomics (RPPA), and histopathology (WSI). This work establishes a robust and flexible generative framework to address sparsity in multimodal datasets, providing a key step toward improving precision oncology. Author SummaryTo make precision medicine a reality, doctors need to understand a patients status from many angles, using different data types like genetic information (omics) and tissue slide images (histopathology). The problem is that most patient records are incomplete, with one or more of these data types missing, which can limit the effectiveness of powerful predictive tools. We have built a generative AI system designed to learn the complex biological patterns that connect all these different data types. By looking at the patient data that is available, our system can then generate a realistic, synthetic version of any missing piece. We developed a novel method called Coherent Denoising to do this, which is flexible and helps protect patient privacy. We validated this approach on a large dataset of over 10,000 cancer patient profiles. We show that our AI-generated data is high-fidelity and can successfully complete these sparse patient profiles, allowing AI models for crucial tasks like cancer staging and survival prediction to work at their best even with incomplete patient data. We also demonstrate how this tool can be used to evaluate the potential impact of new tests, helping to prioritize which expensive diagnostic tests would be most beneficial for a patient.

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bioRxiv2025

Toward Reliable Synthetic Omics: Statistical Distances for Generative Models Evaluation

BackgroundSynthetic data generation is emerging as an approach to overcome the limitations of real-world data scarcity in omics studies, especially in precision medicine and oncology. Omics datasets, with their high dimensionality and relatively small sample sizes, often lead to overfitting, especially in deep learning models. Generative models offer a promising way to generate realistic synthetic data preserving the original data distribution. However, there is still no objective consensus on how to evaluate their performance. In this study, we set out to validate generative networks for transcriptomics data generation by using statistical distances as robust evaluation metrics. ResultsWe observe that statistical distances enable simultaneous evaluation of global and local data fidelity of generated synthetic data. Because these distances satisfy the properties of true metrics, they also enable formal hypothesis testing to assess whether generative models have in fact converged or are merely approaching the reference distribution. Crucially, optimizing for these distances was found to implicitly select models maximizing other widely used metrics of generative performance, providing evidence of their broad applicability. Overall, our findings indicate that the adoption of these metrics can play a key role in guiding the development of generative models across a wide range of domains.

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bioRxiv2024

Generative Models Validation via Manifold Recapitulation Analysis

Comparing empirical distributions is central to generative model evaluation, hypothesis testing and data augmentation in high-dimensional biological data. Established methods such as energy distance summarize each point's relationship to the opposing distribution through a single expected distance, providing sensitivity to location shifts. We introduce Signature Distance (SD), a statistical distance that compares empirical distributions through the mean absolute difference of their sorted pointwise distance profiles. SD is a structural generalization of energy distance and matches its quadratic pairwise-distance cost, with an additional sorting step. In controlled experiments and on TCGA pan-cancer transcriptomic data, we show that (1) SD detects density changes with greater sensitivity than energy distance in the tested scale-contraction scenarios; (2) per-point mean-distance and signature-profile landscapes reveal the geometric mechanisms behind their different penalties; (3) linearly interpolated biological samples that receive no increased penalty from energy distance are penalized by SD; (4) SD provides a direct differentiable potential energy for model-free Langevin data expansion, with a bootstrap resampling protocol to assess the stopping epoch; and (5) SD is directly usable as a differentiable generative training loss. Code to reproduce all experiments is available at github.com/lazzaronico/signature-distance.

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