A review of machine learning methods for cancer characterization from microbiome data

Autores da FMUP
Participantes de fora da FMUP
- Teixeira, M
- Silva, F
- Ferreira, RM
- Pereira, T
- Oliveira, HP
Unidades de investigação
Abstract
Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.
Dados da publicação
- ISSN/ISSNe:
- 2397-768X, 2397-768X
- Tipo:
- Article
- Páginas:
- -
- Link para outro recurso:
- www.scopus.com
npj Precision Oncology NATURE PORTFOLIO
Citações Recebidas na Web of Science: 3
Citações Recebidas na Scopus: 3
Documentos
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Filiações
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Keywords
- COLORECTAL-CANCER; FEATURE-SELECTION; DECISION TREES; CLASSIFICATION; MODELS; GENE; BACTERIA; TISSUE; ATLAS; TUMOR
Proyectos asociados
Unravelling Helicobacter pylori strategies to disrupt cell-cell junctions
Investigador Principal: Maria do Céu Fontes Herdeiro Figueiredo
Estudo Clínico Académico (Helicobacter) . 2019
Immune checkpoint inhibitors for the treatment of gastric cancer
Investigador Principal: Maria do Céu Fontes Herdeiro Figueiredo
Estudo Clínico Académico . 2021
Evaluation of the use of Helicobacter pylori genotypes and gastric microbiota markers to identify individuals at high risk of gastric carcinoma
Investigador Principal: Maria do Céu Fontes Herdeiro Figueiredo
Estudo Clínico Académico . 2021
Citar a publicação
Teixeira M,Silva F,Ferreira RM,Pereira T,Figueiredo C,Oliveira HP. A review of machine learning methods for cancer characterization from microbiome data. npj Precis. Oncol. 2024. 8. (1):123. IF:7,900. (1).