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1
Intro
2
Outline
3
Genome Sequencing Machine
4
Genome Assembly Problem
5
Challenges
6
Raven Genome Assembler
7
Machine Learning Framework
8
Assembly Graph Construction
9
Edge Labeling
10
Designing GNNs for Assembly Graphs
11
Edge Prediction Layer
12
Network Training
13
Experimental Setting
14
Evaluation
15
Dataset and Code
Description:
Explore a groundbreaking approach to genome assembly using deep learning in this 43-minute lecture by Xavier Bresson from the National University of Singapore. Delve into the challenges of untangling genome assembly graphs and discover how graph convolutional networks can be trained to resolve these complex structures. Learn about the innovative framework that outperforms traditional hand-crafted heuristics in reconstructing chromosomes, achieving higher accuracy and improved assessment metrics. Gain insights into the experimental setup, dataset generation, and evaluation methods used to demonstrate the model's remarkable ability to generalize across different chromosomes.

Learning to Untangle Genome Assembly Graphs - IPAM at UCLA

Institute for Pure & Applied Mathematics (IPAM)
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