arXiv AI
Incorporating LLM Embeddings for Variation Across the Human Genome
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arXiv AI
Aug 12
JEPA-DNA: Grounding Genomic Foundation Models through Joint-Embedding Predictive Architectures
arXiv:2602. 17162v3 Announce Type: replace Abstract: Genomic Foundation Models (GFMs) typically rely on Masked Language Modeling (MLM) or Next-Token Prediction (NTP) to learn the "Laws of Nature".
arXiv AI
Jul 29
DeepVRegulome: DNABERT-based deep-learning framework for predicting the functional impact of short genomic variants on the human regulome
arXiv:2511. 09026v2 Announce Type: replace-cross Abstract: Whole-genome sequencing (WGS) has revealed numerous non-coding short variants whose functional impacts remain poorly understood.
arXiv Machine Learning
Aug 19
Quantifying Memorization and Privacy Risks in Genomic Language Models
The paper introduces a comprehensive privacy evaluation framework for genomic language models (GLMs) that quantifies memorization risks using perplexity-based detection, canary sequence extraction, and membership inference. By planting canary sequences at different repetition rates in synthetic and real datasets, the authors systematically assess how repetition, model capacity, and training dynamics affect memorization across various GLM architectures. The study demonstrates that GLMs do memorize training data to varying degrees and that no single attack method fully captures this risk, highlighting the necessity of multi-vector privacy auditing for genomic AI systems.
arXiv Machine Learning
Jun 9
From Hazard Functions to Language Space: Cox-Supervised Distillation of Survival Risk into a Large Language Model
arXiv:2606. 08945v1 Announce Type: new Abstract: We investigate whether information about time-to-event risk estimated by a Cox proportional hazards model can be transferred into a generative large language model.
arXiv AI
Sep 7
A Semantic Model of Genetic Evidence: A Step Toward Bridging the Basic-Science-Clinic Gap
The article presents a new semantic model for representing scientific evidence, specifically tailored to genetics, that extends existing standards by adding fine‑grained, domain‑specific structure. It aligns with FHIR Evidence and SEPIO, incorporates a compact vocabulary validated by SHACL, and was tested in a human‑AI annotation pilot on six genetics papers, producing 28 evidence items and 95 source‑anchored assertions. The authors argue that this model advances trustworthy, AI‑ready infrastructure for variant interpretation by providing a reference data model and validation schema for genetic evidence.
arXiv Machine Learning
Aug 4
xMICD: Explainable Representation of Multiple ICD Codes
arXiv:2608. 00935v1 Announce Type: new Abstract: Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning.