[1] Appen . 2022年人工智能和机器学习现状报告[R](2022).

[4] HACKELING G[M]. Mastering machine learning with scikit-learn(2017).

[5] KENYON C J. The genetics of ageing[J]. Nature, 464, 504-512(2010).

[6] DA SILVA P N, PLASTINO A, FREITAS A A. A novel genetic algorithm for feature selection in hierarchical feature spaces[C], 738-746(2018).

[7] DE MAGALHÃES J P, BUDOVSKY A, LEHMANN G et al. The human ageing genomic resources: Online databases and tools for biogerontologists[J]. Aging Cell, 8, 65-72(2009).

[8] ASHBURNER M, BALL C A, BLAKE J A et al. Gene ontology: Tool for the unification of biology[J]. Nature Genetics, 25, 25-29(2000).

[9] ZHENG Y F, LIU X X, LE W Q et al. A human circulating immune cell landscape in aging and COVID-19[J]. Protein & Cell, 11, 740-770(2020).

[10] YAN Q, WEEKS D E, XIN H Y et al. Deep-learning-based prediction of late age-related macular degeneration progression[J]. Nature Machine Intelligence, 2, 141-150(2020).

[13] TAO D C, LI X L, WU X D et al. Geometric mean for subspace selection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 260-274(2009).

[14] WAN C, FREITAS A A, DE MAGALHÃES J P. Predicting the pro-longevity or anti-longevity effect of model organism genes with new hierarchical feature selection methods[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12, 262-275(2015).

[15] DA SILVA P N, PLASTINO A, FREITAS A A. Prioritizing positive feature values: A new hierarchical feature selection method[J]. Applied Intelligence, 50, 4412-4433(2020).