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Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

Medicine and Health

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

Y. Takahashi, M. Ueki, et al.

Discover the groundbreaking research by Yuta Takahashi and colleagues on a novel prediction model for depressive symptoms using the HSIC Lasso algorithm! This innovative study leverages a vast metabolomic dataset from the population affected by the Great East Japan Earthquake, revealing key metabolites that enhance predictive power.... show more
Citation Metrics
Citations
36
Influential Citations
1
Reference Count
51
Citation by Year

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

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