Korea University at ASHG 2026
Oct 20–24, 2026 Montréal, Canada ~7.5k attendees Website
Korea University brings 7 presentations from 2 research groups to ASHG 2026 in Montréal. Its program centres on computational genomics and therapeutic discovery. AN Lab brings a two-layer autism phenotype fingerprint and long-read sequencing of 71 Korean autism families, while AN Lab · AI for Nature brings autism VUS reclassification and genomic foundation-model analysis of CD19 enhancer variants.
| Organization | ASHG 2026 Attendance |
|---|---|
Korea University Seoul, South Korea | 5 PhD Students · 1 Undergrad |
AN Labjoonanlab.github.io Trains foundation models on scRNA-seq and scATAC-seq data. Uses long-read WGS for autism genetics and AI drug discovery.
| Poster Wed Oct 21 2:30 pm A two-layer phenotype fingerprint for autism via sentence-attention transformers and LLM silver labeling on clinical reports AutismArtificial intelligencePhenotypeCharacterization of disorders Poster Thu Oct 22 4:15 pm A2Denovo expands de novo variant discovery in long-read sequencing of 71 Korean autism families Long-read sequencingGenomicsAutismVariant calling Poster Fri Oct 23 2:30 pm AnnQ quantifies abnormal cellular states from single-cell annotation uncertainty Single-cellComputational toolsBioinformaticsNeurodevelopmental |
AN Lab · AI for Naturejoonanlab.github.io Trains foundation models on scRNA-seq, scATAC-seq, long-read WGS and multi-omics data. Studies autism genetics and brain development with Seoul National University Bundang Hospital clinicians.
| Poster Thu Oct 22 4:15 pm Reclassification of de novo coding VUS in autism spectrum disorder cohorts using SFARI-tiered PS2 and calibrated PP3/BP4 evidence AutismComputational toolsDiagnosticsExome/genome sequencing Poster Thu Oct 22 4:15 pm In silico mutagenesis using a genomic foundation model identifies functional variants and cell-type-specific regulation at the CD19 enhancer Variant interpretationDeep learningGene regulationTranscription factor Poster Fri Oct 23 2:30 pm Long-read-trained confidence scoring of short-read structural variants and tandem repeats reveals novel SV candidates and systemic repeat miscalling Machine learningLong-read sequencingCopy number/structural variationTriplet and other repeats |
| 1 more presenter — research group not yet identified | |
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