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Tensor Least Angle Regression for Sparse Representations of Multidimensional Signals.张量最小角回归在多维信号稀疏表示中的应用。
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Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification.基于 L1/2 罚项的稀疏逻辑回归在癌症分类中的基因选择。
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eNetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models.eNetXplorer：用于广义线性模型中弹性网络家族的定量探索的 R 包。
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L1/2 regularization: a thresholding representation theory and a fast solver.L1/2 正则化：一种阈值表示理论和快速求解器。
IEEE Trans Neural Netw Learn Syst. 2012 Jul;23(7):1013-27. doi: 10.1109/TNNLS.2012.2197412.
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On constrained and regularized high-dimensional regression.关于约束与正则化高维回归
Ann Inst Stat Math. 2013 Oct;65(5):807-832. doi: 10.1007/s10463-012-0396-3.
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A path algorithm for the support vector domain description and its application to medical imaging.一种用于支持向量域描述的路径算法及其在医学成像中的应用。
Med Image Anal. 2007 Oct;11(5):417-28. doi: 10.1016/j.media.2007.07.008. Epub 2007 Aug 19.
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Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features.使用逐步 L1、L2 正则化和特征选择对具有大量特征的小数据集进行预测。
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An adaptive support driven reweighted L1-regularization algorithm for fluorescence molecular tomography.一种用于荧光分子断层成像的自适应支持驱动重加权L1正则化算法。
Biomed Opt Express. 2014 Oct 28;5(11):4039-52. doi: 10.1364/BOE.5.004039. eCollection 2014 Nov 1.
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Finding Principal Paths in Data Space.在数据空间中寻找主路径。
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Quantifying the tissue-specific regulatory information within enhancer DNA sequences.量化增强子DNA序列中的组织特异性调控信息。
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ModHMM: A Modular Supra-Bayesian Genome Segmentation Method.ModHMM：一种模块化超贝叶斯基因组分割方法。
J Comput Biol. 2020 Apr;27(4):442-457. doi: 10.1089/cmb.2019.0280. Epub 2019 Dec 18.
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Reconciling modern machine-learning practice and the classical bias-variance trade-off.调和现代机器学习实践与经典偏差-方差权衡。
Proc Natl Acad Sci U S A. 2019 Aug 6;116(32):15849-15854. doi: 10.1073/pnas.1903070116. Epub 2019 Jul 24.
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BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone.比人：仅使用 DNA 序列通过深度学习模型预测增强子。
Bioinformatics. 2017 Jul 1;33(13):1930-1936. doi: 10.1093/bioinformatics/btx105.
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A synergistic DNA logic predicts genome-wide chromatin accessibility.一种协同DNA逻辑预测全基因组染色质可及性。
Genome Res. 2016 Oct;26(10):1430-1440. doi: 10.1101/gr.199778.115. Epub 2016 Jul 25.
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Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks.巴塞特：利用深度卷积神经网络学习可及基因组的调控密码。
Genome Res. 2016 Jul;26(7):990-9. doi: 10.1101/gr.200535.115. Epub 2016 May 3.
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Discriminative prediction of mammalian enhancers from DNA sequence.从 DNA 序列中鉴别预测哺乳动物增强子。
Genome Res. 2011 Dec;21(12):2167-80. doi: 10.1101/gr.121905.111. Epub 2011 Aug 29.
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