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DTSTART;TZID=America/Los_Angeles:20260625T130000
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SUMMARY:BME/Genomics Seminar: Supervised and Unsupervised DeepGene Finding and Genome Foundation Models
DESCRIPTION:Presenter: Mario Stanke\, Professor of Bioinformatics\, University of Greifswald \nDescription: This talk will explore recent machine learning approaches for eukaryotic genome annotation. Our supervised ab initio deep gene finder\, Tiberius\, correctly predicts more than four times as many human protein-coding gene structures as its father\, Augustus\, and in some clades\, it approaches the accuracy of evidence-based pipelines such as BRAKER. Genome foundation models can automatically learn annotation-relevant embeddings from unannotated training genomes. I will also present Vipsania\, the unsupervised wife of Tiberius. Vipsania is a genome foundation model that learns hidden Markov models to find gene structures from naked genomes using a BERT-style masked language model objective. Finally\, I will report on ongoing efforts to use phylogenetic teaching signals from whole-genome vertebrate alignments to train a genome foundation model comparatively. \nKeywords: hidden Markov model layer\, linear recurrent unit\, continuous-time Markov chains on trees \nBio: Mario Stanke studied mathematics and computer science at the University of Göttingen and UCBerkeley\, and received his Dr. rer. nat. from the University of Göttingen. He completed a postdoctoral fellowship in the Haussler lab at UC Santa Cruz in 2006–2007. He has been a Professor of Bioinformatics at the Institute of Mathematics and Computer Science at the University of Greifswald since 2010. \nHosted by: Genomics Institute \nLocation: E2-599 (limited space) \nZoom: https://ucsc.zoom.us/j/95380317295?pwd=0HbwSYKRQqyCtBcPXGfoB0tPOsA16V.1
URL:https://live-events-ucsc.pantheonsite.io/event/bme-genomics-seminar-supervised-and-unsupervised-deepgene-finding-and-genome-foundation-models/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Lectures & Presentations,Seminars
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