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

Data de publicação:

Autores da FMUP

  • Maria Do Céu Fontes Herdeiro Figueiredo

    Autor

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

npj Precision Oncology  NATURE PORTFOLIO

Tipo:
Article
Páginas:
-
Link para outro recurso:
www.scopus.com

Citações Recebidas na Web of Science: 3

Citações Recebidas na Scopus: 3

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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

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