Search arXivSearch

arXiv · 2606.20315

bioETH-Beacon: A Confidential On-Chain Genomic Beacon with Encrypted Counts, Filters, and Bounded Noise over a Fully Homomorphic EVM

Abstract

The Global Alliance for Genomics and Health (GA4GH) Beacon protocol lets researchers ask whether a genomic variant has been observed in a participating cohort and receive aggregate variant-level counts. As Beacon networks grow, two privacy risks remain: host institutions can see plaintext queries, and repeated rare-variant queries can support membership-inference attacks. We present bioETH-Beacon, a smart-contract prototype that runs the Beacon "aggregate count" query over encrypted data on a fully homomorphic Ethereum Virtual Machine (fhEVM). Hospitals upload encrypted marker-count entries, authorized researchers submit encrypted marker queries, and the contract returns an encrypted answer that is released, via an off-chain key-management service, only to the requester named in the contract's on-chain ACL. The design is organized as a 3x4 tier-by-query-family grid spanning genotype, sex, age, and phenotype queries, with tiers that trade stronger confidentiality for lower query cost. For genotype paths, the prototype can add bounded on-chain noise to mitigate probing attacks. Experiments on synthetic panels derived from a Polygenic Score (PGS) catalog show the expected scaling behavior and demonstrate that pre-aggregation can substantially reduce query gas when public marker presence is an acceptable trade-off. Overall, bioETH-Beacon provides a research prototype for confidential Beacon-style genomic querying without a trusted compute evaluator.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Christos Galanopoulos, Kimon Antonios Provatas, Ilias Georgakopoulos-Soares. 2026-06-18. bioETH-Beacon: A Confidential On-Chain Genomic Beacon with Encrypted Counts, Filters, and Bounded Noise over a Fully Homomorphic EVM. https://arxiv.org/abs/2606.20315

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Revolutionizing Genomics with Reinforcement Learning Techniques

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics.

q-bio.GN

Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment response prediction a critical unmet need. Transcriptomics-based biomarkers derived from bulk and single-cell RNA sequencing (scRNA-seq) offer a promising avenue for capturing tumour-immune interactions, yet the cross-cohort generalisability of existing prediction models remains unclear.We systematically benchmark nine state-of-the-art transcriptomic ICI response predictors, five bulk RNA-seq-based models (COMPASS, IRNet, NetBio, IKCScore, and TNBC-ICI) and four scRNA-seq-based models (PRECISE, DeepGeneX, Tres and scCURE), using publicly available independent datasets unseen during model development. Overall, predictive performance was modest: bulk RNA-seq models performed at or near chance level across most cohorts, while scRNA-seq models showed only marginal improvements. Pathway-level analyses revealed sparse and inconsistent biomarker signals across models. Although scRNA-seq-based predictors converged on immune-related programs such as allograft rejection, bulk RNA-seq-based models exhibited little reproducible overlap. PRECISE and NetBio identified the most coherent immune-related themes, whereas IRNet predominantly captured metabolic pathways weakly aligned with ICI biology. Together, these findings demonstrate the limited cross-cohort robustness and biological consistency of current transcriptomic ICI prediction models, underscoring the need for improved domain adaptation, standardised preprocessing, and biologically grounded model design.

q-bio.GN

Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.

q-bio.GN