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Guiding questions to avoid data leakage in biological machine learning applications
Abstract Machine learning methods for extracting patterns from high-dimensional data are
very important in the biological sciences. However, in certain cases, real-world applications …
very important in the biological sciences. However, in certain cases, real-world applications …
Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review
Background A comprehensive overview of artificial intelligence (AI) for cardiovascular
disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external …
disease (CVD) prediction and a screening tool of AI models (AI-Ms) for independent external …
[HTML][HTML] Consolidated reporting guidelines for prognostic and diagnostic machine learning modeling studies: development and validation
Background The reporting of machine learning (ML) prognostic and diagnostic modeling
studies is often inadequate, making it difficult to understand and replicate such studies. To …
studies is often inadequate, making it difficult to understand and replicate such studies. To …
Computational models for clinical applications in personalized medicine—guidelines and recommendations for data integration and model validation
The future development of personalized medicine depends on a vast exchange of data from
different sources, as well as harmonized integrative analysis of large-scale clinical health …
different sources, as well as harmonized integrative analysis of large-scale clinical health …
[HTML][HTML] Pitfalls in develo** machine learning models for predicting cardiovascular diseases: challenge and solutions
YQ Cai, DX Gong, LY Tang, Y Cai, HJ Li… - Journal of Medical …, 2024 - jmir.org
In recent years, there has been explosive development in artificial intelligence (AI), which
has been widely applied in the health care field. As a typical AI technology, machine …
has been widely applied in the health care field. As a typical AI technology, machine …
Spatial machine learning: new opportunities for regional science
This paper is a methodological guide to using machine learning in the spatial context. It
provides an overview of the existing spatial toolbox proposed in the literature: unsupervised …
provides an overview of the existing spatial toolbox proposed in the literature: unsupervised …
Cracking the black box of deep sequence-based protein–protein interaction prediction
Identifying protein–protein interactions (PPIs) is crucial for deciphering biological pathways.
Numerous prediction methods have been developed as cheap alternatives to biological …
Numerous prediction methods have been developed as cheap alternatives to biological …
The role of artificial intelligence in radiotherapy clinical practice
This review article visits the current state of artificial intelligence (AI) in radiotherapy clinical
practice. We will discuss how AI has a place in the modern radiotherapy workflow at the …
practice. We will discuss how AI has a place in the modern radiotherapy workflow at the …
Radiomics and dosiomics signature from whole lung predicts radiation pneumonitis: A model development study with prospective external validation and decision …
Purpose Radiation pneumonitis (RP) is one of the common side effects of radiation therapy
in the thoracic region. Radiomics and dosiomics quantify information implicit within medical …
in the thoracic region. Radiomics and dosiomics quantify information implicit within medical …
Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning
There is unprecedented opportunity to use machine learning to integrate high-dimensional
molecular data with clinical characteristics to accurately diagnose and manage disease …
molecular data with clinical characteristics to accurately diagnose and manage disease …