36 citations · 85 across the 6 of their papers we have counts for
8 papers
Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions
Florian Becker, Age K. Smilde, Evrim Acar
Computational phenotyping allows for unsupervised discovery of subgroups of patients as well as corresponding co-occurring medical conditions from electronic health records (EHR).…
All Sparse PCA Models Are Wrong, But Some Are Useful. Part I: Computation of Scores, Residuals and Explained Variance
J. Camacho, A. K. Smilde, E. Saccenti +1
Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA) that combines variance maximization and sparsity w…
Numerical Representations of Metabolic Systems
Age K. Smilde, Thomas Hankemeier
Metabolomics is becoming a mature part of analytical chemistry as evidenced by the growing number of publications and attendees of international conferences dedicated to this topic…
Heterofusion: Fusing genomics data of different measurement scales
Age K. Smilde, Yipeng Song, Johan A. Westerhuis +3
In systems biology, it is becoming increasingly common to measure biochemical entities at different levels of the same biological system. Hence, data fusion problems are abundant i…
Logistic principal component analysis via non-convex singular value thresholding
Yipeng Song, Johan A. Westerhuis, Age K. Smilde
Multivariate binary data is becoming abundant in current biological research. Logistic principal component analysis (PCA) is one of the commonly used tools to explore the relations…
Separating common (global and local) and distinct variation in multiple mixed types data sets
Yipeng Song, Johan A. Westerhuis, Age K. Smilde
Multiple sets of measurements on the same objects obtained from different platforms may reflect partially complementary information of the studied system. The integrative analysis…