Genomics and Rare Diseases

Neueste Paper

• RareDx: Controlled Knowledge Integration and Graph-Grounded Policy Optimization for Rare-Disease Diagnosis

  Bo Zhang, Yuchen Wang, Dongbai Li, Matthew Yu Heng Wong, Qingkai Zeng, Lijun Wang, Tien-Yin Wong, Peng Cui, Tianyu Liu · 29. September 2026

  Rare-disease diagnosis is a long-tail reasoning problem: phenotypes are incomplete, individual disorders are sparsely documented, and relevant evidence is distributed across ontologies, gene annotations, and biomedical text. Language models consequently favor common conditions, miss rare candidates,…

• GenoMorph: Pathway-Grounded Genomic Disease Reasoning via Adaptive Latent Computation

  Tanmoy Kanti Halder, Akash Ghosh, Arijit Roy, Sriparna Saha · 29. September 2026

  Large language models (LLMs) have demonstrated strong capabilities in biological reasoning; however, genomic disease inference remains largely dependent on memorized gene-disease associations rather than understanding biological pathways. This shortcut learning undermines robustness and generalizati…

• Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

  Tohid Ghasemnejad, Ahmadreza Argha, Mark Grosser, John Wang, Min Yang, Thantrira Porntaveetus, Tony Roscioli, Nigel H. Lovell, Mahmoud Aarabi, Hamid Alinejad-Rokny · 18. September 2026

  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 Generati…

• Integrating knowledge from case reports: a medical ontology based multimodal information system with structured summary

  Shuyu Guo, Lan Huang, Yichen Liu, Hanbin Ma, Tian Bai · 18. September 2026

  Published medical case reports serve as a crucial medical information carrier, documenting discoveries in rare diseases, diagnostic methods, and innovative treatments. Despite the wealth of clinical knowledge in millions of case reports in the public medicine literature database (PubMed), accessing …

• HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition

  Kamilia Zaripova, Nassir Navab, Azade Farshad, Annalisa Marsico · 17. September 2026

  More than 300 million people worldwide are affected by one of over 7,000 known rare diseases, yet diagnosis remains difficult because patients initially present with incomplete and heterogeneous phenotypes. We present HPOQuest, a training-free framework for sequential phenotype acquisition in rare-d…

• NSIDDx: A Design Framework for Neuro-Symbolic, Practitioner-First Differential Diagnosis in Low-Resource Settings

  Aarav Singh · 2. September 2026

  LLM-based diagnostic systems achieve high semantic accuracy on benchmarks, but open-ended evaluation on clinically uncommon presentations reveals a systematic gap between headline accuracy and verifiable clinical reliability. We evaluate an LLM+rare-disease-RAG pipeline across two cohorts and show t…

• Rare Diseases, Common Dilemmas: LLMs Prioritize Equal Resource Distribution over Patient Benefit in Decision-Making

  Minda Zhao, Xu Han, Rishabh Goel, Maya Dagan, Noa Dagan, Adithya Madduri, Payal Chandak, Shilpa Nadimpalli Kobren, Isaac S. Kohane · 27. August 2026

  Clinical decision-making often involves prioritizing ethical values, such as beneficence, non-maleficence, respecting a patient's autonomy, and justice. Recent work has begun to assess how large language models (LLMs) make such subjective, value-laden clinical judgments. However, evaluations of LLM …

• Multi-Level Evidence Aggregation for Robust Facial Phenotype Retrieval in Rare Genetic Disorder Prioritization

  Alexander Hustinx, Carolin Kaffiné, Behnam Javanmardi, Tzung-Chien Hsieh, Peter Krawitz · 12. August 2026

  AI-assisted facial phenotyping supports rare genetic disorder prioritization by retrieving visually similar diagnosed cases from facial image reference databases such as the GestaltMatcher Database (GMDB). Existing GestaltMatcher-based retrieval frameworks compare each test image with individual gal…

• RareLens: Towards End-to-End Rare Disease Care via Aligning Divergent Large Language Model Reasoning

  Xi Chen, Hongru Zhou, Shiyu Feng, Hanyu Zhou, Huahui Yi, Rongsheng Wang, Tiancheng He, Kun Wang, Pingping Liu, Qiankun Li, Sicheng Lin, Huiying Ou, Xiaohong Zheng, Tianying Zang, Zhuohang Wu, Leheng Jiang, Kexin Cao, Wenhan Zhang, ChengYi Li, Zhiyang Wang, Songlin Li, Benyou Wang, Ningbei Yin, Shaoting Zhang, Weili Fu, Jian Li, Kang Li · 11. August 2026

  Rare diseases represent one of the most challenging settings for clinical decision-making, where heterogeneous presentations, sparse evidence and limited expertise create persistent uncertainty throughout the care pathway. Although artificial intelligence could help, existing systems largely address…

• GraphRareBench: An Auditable Graph-Evidence Benchmark for Phenotype-Driven Rare-Disease Diagnosis

  Guiling Guo, Jia Yang, Jiahao Xu, Shuyuan Zheng, Zhonghai Sun, Qiyuan Li · 29. Juli 2026

  Phenotype-driven diagnostic benchmarks usually report the rank of the reference disease, but they rarely reveal which plausible alternatives are ranked above it or what evidence a tool-using model examines before making its decision. We introduce GraphRareBench, a provenance-preserving benchmark con…

• BioSecBench-Surveillance: A Verifiable Benchmark for AI Agents in Pathogen Genomic Surveillance

  Harmon Bhasin, Kevin Flyangolts, Dianzhuo Wang, Evan Seeyave, Arjun Banerjee, Amanda Darling, Joshua Stallings, David Stern, Shawn Higdon, Claire Duvallet, Bryan Tegomoh, Kenny Workman · 22. Juli 2026

  As pathogen genomic surveillance scales, the bottleneck is shifting from data generation to analysis. We present BioSecBench-Surveillance, a verifiable benchmark of 100 evaluations testing whether AI agents can infer the right analysis pipeline from raw sequencing data and surveillance context. Each…

• Judge-dependent safety gains and model-specific helpfulness costs of evidence-sufficiency prompting in clinical LLMs

  Koyar Afrasyab · 21. Juli 2026

  Background: LLM judges increasingly score whether clinical language models give overconfident answers under incomplete evidence, yet whether a measured "safety gain" reflects real behavior change or the judge's calibration is unresolved. Using a structured evidence-sufficiency prompt as a test case,…

• Capabilities of Claude Fable 5 on Biomedical Challenge Problems

  Dominic Okonkwo, Magnus Hodgson, Temitope I. David, Susan Adanna Ihejirika · 14. Juli 2026

  Frontier language models are increasingly evaluated on biomedical benchmarks, but two problems undermine most published evaluations: legacy benchmarks are near-saturated, and open-ended responses are graded by other language models. We evaluate Claude Fable 5, Anthropic's most capable publicly avail…

• SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking

  Nicolai Dinh Khang Truong, Richard R\"ottger · 13. Juli 2026

  Motivation: Rare disease (RD) diagnosis is frequently delayed due to the similarities in symptoms to common disease variants. Machine Learning Algorithms applied to Electronic Health Records show promise for accelerating the diagnosis; however, legal and privacy concerns pose significant barriers. T…

• KARMA: Knowledge graph-based Automated Reasoning Materialization and Alignment

  Jinkyeong Choi, Chaebin Jeong, Donghyeon Park · 7. Juli 2026

  Template-based contrastive synthesis is scalable, but its candidates often differ only in a few entity-slots while sequence-level optimization spreads supervision over mostly shared templates. We formalize this as the Resolution Mismatch Problem and propose KARMA, which enumerates schema-constrained…

• RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation

  Deyang Jiang, Haoran Wu, Ziyi Wang, Yiming Rong, Yunlong Zhao, Ye Jin, Bo Xu · 2. Juli 2026

  Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space. However, existing AI approaches typically rely on pipeline-based ph…

• DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects

  Shiyu Li, Ziqi Yan, Zhihao Wu, Jielong Lu, Weiran Liao, Jiajun Yu, Genjie Li, Zeyu Chu, Jiajun Bu, Haishuai Wang · 24. Juni 2026

  Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability. In the subset with suspected genetic etiologies, exome and genome sequencing have moved many cases from variant detection to post-sequencing interpretation: clinicians must rank patient-specific candidate var…

• A specialized reasoning large language model for accelerating rare disease diagnosis: a randomized AI physician assistance trial

  Haichao Chen, Songchi Zhou, Zhengyun Zhao, Shikai Hu, Xianghong Jin, Hongwei Ji, Li He, Shuli Li, Yiming Qin, Xin Tan, Runfeng Shi, Yih Chung Tham, Jiaye Zhu, Ye Li, Ye Jin, Longhao Cao, Dawei Li, Honghan Wu, Hongqiu Gu, Guanqiao Li, Tudor Groza, Chunying Li, Dian Zeng, Weihong Yu, Gareth Baynam, Saumya Shekhar Jamuar, Min Shen, Shuyang Zhang, Bin Sheng, Sheng Yu, Tien Yin Wong · 24. Juni 2026

  Rare diseases affect millions of individuals worldwide, yet timely diagnosis remains a major public health challenge due to scarcity of specialized clinical expertise. While large language models (LLMs) show promise to support rare disease diagnosis, current models are constrained by insufficient cl…

• Circuit Tracing in Autoregressive Protein Language Models

  Darin Tsui, William Deinzer, Daniel Saeedi, Amirali Aghazadeh · 16. Juni 2026

  Protein language models (pLMs) can generate novel protein sequences with properties beyond those observed in nature, yet the mechanisms underlying protein generation remain poorly understood. Existing mechanistic interpretability methods based on sparse autoencoders and transcoders primarily focus o…

• LiteOdyssey: A Lightweight Reasoning AI Agent for Interpretable Rare-Disease Diagnosis

  Minh-Ha Nguyen, Erica Gray, Chih-Ting Yang, Rizwan Hamid, Lingyao Li, Siyuan Ma, Thomas A. Cassini, Cathy Shyr · 16. Juni 2026

  Most medical AI systems improve by scaling additional machinery: more fine-tuning data, more agents, and/or larger retrieval databases. In rare-disease diagnosis, however, such scaling can produce systems that are difficult to deploy, audit, and maintain. We asked whether state-of-the-art diagnostic…

• EpiBench: Verifiable Evaluation of AI Agents on Epigenomics Analysis

  Harihara Muralidharan, Reema Baskar, Soo Hee Lee, Tim Proctor, Kenny Workman · 12. Juni 2026

  We introduce EpiBench, a verifiable benchmark for short-horizon epigenomics analysis. EpiBench evaluates whether agents can make well-defined analysis decisions from realistic workflow states and return deterministically gradable answers. The benchmark includes 106 evaluations across CUT\&Tag/CUT\&R…

• Beyond English benchmarks: clinical llm evaluation in Brazilian Portuguese

  Giordano de Pinho Souza, Glaucia Melo, Josefino Cabral Melo Lima, Daniel Schneider · 9. Juni 2026

  Large Language Models are transforming the support for clinical decision and their application in real scenarios. Yet, most benchmarks are conducted in English, and cross-lingual evaluation is needed to tackle the language gaps in global access. We introduce ClinicalBr, the first bilingual benchmark…

• AnnotateMissense: a genome-wide annotation and benchmarking framework for missense pathogenicity prediction

  Muhammad Muneeb, David B. Ascher · 26. Mai 2026

  Missense variant interpretation remains challenging because pathogenicity depends on heterogeneous evidence from population frequency, evolutionary conservation, transcript context, amino acid substitution severity, prior pathogenicity predictors and protein-language-model-derived features. We prese…

• Synthetic Data Alone is Enough? Rethinking Data Scarcity in Pediatric Rare Disease Recognition

  Ganlin Feng, Yuxi Long, Erin Lou, Lianghong Chen, Zihao Jing, Pingzhao Hu, Wei Xu · 22. Mai 2026

  Children with rare genetic diseases often exhibit distinctive facial phenotypes, yet developing computer vision systems for early diagnosis remains challenging due to extreme data scarcity, privacy constraints, and limited data sharing in pediatric settings. These challenges not only hinder automate…

• ViroGym: Realistic Large-Scale Benchmarks for Evaluating Viral Proteins

  Yichen Zhou, Jonathan Golob, Amir Karimi, Stefan Bauer, Patrick Schwab · 20. Mai 2026

  Protein language models (pLMs) have shown strong potential for zero-shot prediction of missense variant effects, yet systematic benchmarking on viral proteins remains limited, a critical gap given the need for proactive tools that can anticipate emerging mutations ahead of experimental validation. H…