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A SEMIPARAMETRIC MODELING APPROACH USING BAYESIAN ADDITIVE REGRESSION TREES WITH AN APPLICATION TO EVALUATE HETEROGENEOUS TREATMENT EFFECTS.
In The annals of applied statistics
Zeldow Bret, Lo Re Vincent, Roy Jason
Bayesian Additive Regression Trees, antiretrovirals, structural mean model
In Journal of medicine and life
Nazari Elham, Farzin Amir Hossein, Aghemiri Mehran, Avan Amir, Tara Mahmood, Tabesh Hamed
AML, deep learning, machine learning, microarray, neural network
Designing low-cost, accurate cervical screening strategies that take into account COVID-19: a role for self-sampled HPV typing2.
In Infectious agents and cancer
Ajenifuja Kayode Olusegun, Belinson Jerome, Goldstein Andrew, Desai Kanan T, de Sanjose Silvia, Schiffman Mark
COVID-19, Cervical screening, HPV, Self-sampling, Triage
Automated discretization of 'transpiration restriction to increasing VPD' features from outdoors high-throughput phenotyping data.
In Plant methods
Kar Soumyashree, Tanaka Ryokei, Korbu Lijalem Balcha, Kholová Jana, Iwata Hiroyoshi, Durbha Surya S, Adinarayana J, Vadez Vincent
Feature selection, Gini index, High throughput phenotyping, Machine learning, Neural network, Sensitivity analysis, Time series, Transpiration rate, Unsupervised random-forest, Vapor pressure deficit
T4SE-XGB: Interpretable Sequence-Based Prediction of Type IV Secreted Effectors Using eXtreme Gradient Boosting Algorithm.
In Frontiers in microbiology
Chen Tianhang, Wang Xiangeng, Chu Yanyi, Wang Yanjing, Jiang Mingming, Wei Dong-Qing, Xiong Yi
SHAP (SHapley additive exPlanations), extreme gradient boosting, feature secelction, interpretable analysis, type IV secreted effector
Usefulness of machine learning in COVID-19 for the detection and prognosis of cardiovascular complications.
In Reviews in cardiovascular medicine
Zimmerman Allison, Kalra Dinesh
COVID-19, artificial intelligence, cardiovascular, machine learning