Cargando…
Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review
This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients’ gene expression and clinical data through a variety of techniques to predict patients’ outcomes, mechanistic mo...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669004/ https://www.ncbi.nlm.nih.gov/pubmed/38002445 http://dx.doi.org/10.3390/bioengineering10111320 |
_version_ | 1785149198177402880 |
---|---|
author | Sofia, Dilruba Zhou, Qilu Shahriyari, Leili |
author_facet | Sofia, Dilruba Zhou, Qilu Shahriyari, Leili |
author_sort | Sofia, Dilruba |
collection | PubMed |
description | This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients’ gene expression and clinical data through a variety of techniques to predict patients’ outcomes, mechanistic models focus on investigating cells’ and molecules’ interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors’ microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes. |
format | Online Article Text |
id | pubmed-10669004 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106690042023-11-16 Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review Sofia, Dilruba Zhou, Qilu Shahriyari, Leili Bioengineering (Basel) Review This review explores the multifaceted landscape of renal cell carcinoma (RCC) by delving into both mechanistic and machine learning models. While machine learning models leverage patients’ gene expression and clinical data through a variety of techniques to predict patients’ outcomes, mechanistic models focus on investigating cells’ and molecules’ interactions within RCC tumors. These interactions are notably centered around immune cells, cytokines, tumor cells, and the development of lung metastases. The insights gained from both machine learning and mechanistic models encompass critical aspects such as signature gene identification, sensitive interactions in the tumors’ microenvironments, metastasis development in other organs, and the assessment of survival probabilities. By reviewing the models of RCC, this study aims to shed light on opportunities for the integration of machine learning and mechanistic modeling approaches for treatment optimization and the identification of specific targets, all of which are essential for enhancing patient outcomes. MDPI 2023-11-16 /pmc/articles/PMC10669004/ /pubmed/38002445 http://dx.doi.org/10.3390/bioengineering10111320 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Sofia, Dilruba Zhou, Qilu Shahriyari, Leili Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review |
title | Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review |
title_full | Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review |
title_fullStr | Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review |
title_full_unstemmed | Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review |
title_short | Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review |
title_sort | mathematical and machine learning models of renal cell carcinoma: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10669004/ https://www.ncbi.nlm.nih.gov/pubmed/38002445 http://dx.doi.org/10.3390/bioengineering10111320 |
work_keys_str_mv | AT sofiadilruba mathematicalandmachinelearningmodelsofrenalcellcarcinomaareview AT zhouqilu mathematicalandmachinelearningmodelsofrenalcellcarcinomaareview AT shahriyarileili mathematicalandmachinelearningmodelsofrenalcellcarcinomaareview |