Classification of tumor types using XGBoost machine learning model: a vector space transformation of genomic alterations

Abstract Background Machine learning (ML) represents a powerful tool to capture relationships between molecular alterations and cancer types and to extract biological information. Here, we developed a plain ML model aimed at distinguishing cancer types based on genetic lesions, providing an addition...

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Detaylı Bibliyografya
Asıl Yazarlar:	Veronica Zelli, Andrea Manno, Chiara Compagnoni, Rasheed Oyewole Ibraheem, Francesca Zazzeroni, Edoardo Alesse, Fabrizio Rossi, Claudio Arbib, Alessandra Tessitore
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	BMC 2023-11-01
Seri Bilgileri:	Journal of Translational Medicine
Konular:	Machine learning XGBoost classifier models Vector Space Transformation Genomic Alterations Tumors Cancer diagnosis
Online Erişim:	https://doi.org/10.1186/s12967-023-04720-4
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

Classification of tumor types using XGBoost machine learning model: a vector space transformation of genomic alterations

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