Exploring Cancer Genomics with Graph Convolutional Networks: A Comparative Explainability Study with Integrated Gradients and SHAP

In the rapidly advancing field of cancer genomics, identifying new cancer genes and understanding their molecular mechanisms are essential for advancing targeted therapies and improving patient outcomes. This study explores the capability of Graph Convolutional Networks (GCNs) for integrating comple...

Whakaahuatanga katoa

Ngā taipitopito rārangi puna kōrero

Ngā kaituhi matua:	Battula Joshit, Jillelamudi Venkata Ashok, Sammeta Chaitanya Krishna, Amilpur Santhosh
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	EDP Sciences 2025-01-01
Rangatū:	BIO Web of Conferences
Ngā marau:	shap ig graph convolutional networks multiomics interpretability
Urunga tuihono:	https://www.bio-conferences.org/articles/bioconf/pdf/2025/14/bioconf_icbbb2025_01003.pdf
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!