Integrative prognostic modeling for breast cancer: Unveiling optimal multimodal combinations using graph convolutional networks and calibrated random forest-Reference-Cited by-同舟云学术

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Integrative prognostic modeling for breast cancer: Unveiling optimal multimodal combinations using graph convolutional networks and calibrated random forest

Published:2024-03 Issue: Volume:154 Page:111379
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Palmal Susmita^ORCID,Arya Nikhilanand,Saha Sriparna,Tripathy Somanath

Publisher

Elsevier BV

Reference45 articles.

1. Heterogeneity of tumors in breast cancer: implications and prospects for prognosis and therapeutics;Tuasha;Scientifica,2020

2. Breast cancer as an example of tumour heterogeneity and tumour cell plasticity during malignant progression;Lüönd;Br. J. Cancer,2021

3. Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study;Carey;JAMA,2006

4. SALMON: survival analysis learning with multi-omics neural networks on breast cancer;Huang;Front. Genet.,2019

5. Biological subtypes of breast cancer: Prognostic and therapeutic implications;Yersal;World J. Clin. Oncol.,2014

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