Ensemble of Heterogeneous Machine Learning Models with Multiple Inputs for Multi-Omics Analysis-Reference-Cited by-同舟云学术

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Ensemble of Heterogeneous Machine Learning Models with Multiple Inputs for Multi-Omics Analysis

Published:2023-12-07 Issue: Volume: Page:187-188
ISSN:
Container-title:2023 IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology
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Author:

Trivizakis Eleftherios¹,Koutoulidis Vassilis²,Moulopoulos Lia A.²,Terpos Evangelos³,Ntanasis-Stathopoulos Ioannis³,Malandrakis Panagiotis³,Grigoropoulos Panagiotis²,Papadopoulos Panagiotis²,Nikiforaki Katerina¹,Marias Kostas¹,Papanikolaou Nickolas⁴

Affiliation:

1. Foundation for Research and Technology – Hellas,Heraklion,Greece

2. National Kapodistrian University of Athens,School of Medicine, Aretaieion Hospital,1st Department of Radiology,Athens,Greece

3. National and Kapodistrian University of Athens,School of Medicine,Department of Clinical Therapeutics,Athens,Greece

4. Champalimaud Foundation,Centre of the Unknown,Computational Clinical Imaging Group,Lisbon,Portugal

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10404092/10404107/10404108.pdf?arnumber=10404108

Reference4 articles.

1. Radiomics Analysis for Multiple Myeloma: A Systematic Review with Radiomics Quality Scoring

2. Multiple Myeloma Genomics – A Concise Review

3. Deep Radiotranscriptomics of Non-Small Cell Lung Carcinoma for Assessing Molecular and Histology Subtypes with a Data-Driven Analysis

4. SMOTE: Synthetic Minority Over-sampling Technique

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