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ORGANIZER;CN=Michael McDonald:mailto:michael.mcdonald@fsu.edu
DESCRIPTION;LANGUAGE=en-US:"Multimodal Graph Representation Learning for Si
 ngle-Cell Cancer Genomics"\n\nXian F. Mallory\nDepartment of Computer Scie
 nce\nFlorida State University (FSU)\n\nPlease feel free to forward/share t
 his invitation with other groups/disciplines that might be interested in t
 his talk/topic. All are welcome to attend.\n\nNOTE: In-person attendance i
 s requested in our 499 Dirac Science Library (DSL) Seminar Room. Zoom acce
 ss is intended for external (non-departmental) participants only.\n\nhttps
 ://fsu.zoom.us/j/94273595552\nMeeting # 942 7359 5552\n\n\nWednesday\, Oct
  7\, 2026\, Schedule:\n\n  *\n3:00 to 3:30 PM Eastern Time (US and Canada)
 \n     *\n☕ Nespresso & Teatime - 417 DSL Commons\n\n  *\n3:30 to 4:30 P
 M Eastern Time (US and Canada)\n     *\n🕟 Colloquium - 499 DSL Seminar 
 Room\n\nAbstract:\nSingle-cell sequencing makes it possible to characteriz
 e tumor heterogeneity at cellular resolution. However\, each molecular mod
 ality has inherent limitations. Transcriptomic profiles of malignant and n
 ormal cells often overlap\, copy number alteration (CNA) signals are low r
 esolution and not comprehensive\, and single-nucleotide variations (SNVs) 
 are largely unobserved in low-coverage sequencing. This talk presents a gr
 aph-based framework for integrating complementary modalities. Cells are re
 presented as nodes\, one modality defines the node features\, and a second
  modality defines the edge weights. A graph attention autoencoder then lea
 rns low-dimensional cell embeddings that jointly reflect both sources of i
 nformation. Two applications of this framework will be discussed. SCGclust
  performs subclone clustering on ultra-shallow single-cell DNA sequencing 
 data by combining read-count-based CNA features with SNV-derived cell simi
 larity. It consistently outperforms methods that rely on a single signal. 
 DeepMalignant identifies malignant cells in single-cell RNA sequencing dat
 a by combining expression signatures with CNA-derived cell similarity. Acr
 oss 26 samples\, four cancer types\, and three sequencing platforms\, it a
 chieves the most favorable balance of precision and recall\, and it genera
 lizes to spatial transcriptomics. Ablation analyses confirm that both the 
 multimodal edge construction and the attention mechanism contribute to per
 formance.\n\n\nAdditional colloquium details can be found here:\nhttps://w
 ww.sc.fsu.edu/news-and-events/colloquium/colloquium-with-xian-mallory-2026
 -10-07<https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fw
 ww.sc.fsu.edu%2Fnews-and-events%2Fcolloquium%2Fcolloquium-with-xian-mallor
 y-2026-10-07&data=05%7C02%7Csc-seminar-announce%40lists.fsu.edu%7Cca4d9b20
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 lloquium recordings will be made available here:\nhttps://www.sc.fsu.edu/n
 ews-and-events/seminars<https://nam04.safelinks.protection.outlook.com/?ur
 l=https%3A%2F%2Fwww.sc.fsu.edu%2Fnews-and-events%2Fseminars&data=05%7C02%7
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SUMMARY;LANGUAGE=en-US:Scientific Computing Colloquium with Xian Mallory
DTSTART;TZID=Eastern Standard Time:20261007T153000
DTEND;TZID=Eastern Standard Time:20261007T162500
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20261007T122648Z
TRANSP:OPAQUE
STATUS:CONFIRMED
SEQUENCE:1
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