Missing value estimation methods for DNA microarrays

4,313 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Understanding multicellular function and disease with human tissue-specific networks (2015, neutral): “We defined T ′ for each tissue as genes specifically annotated to unrelated tissues.”
Deep Partial Multi-View Learning (2020, methodology): “The representative global imputation algorithms include SVD imputation (SVDim-pute) [36] and Bayesian principal component analysis (BPCA) [37].”
Imputation of missing values for compositional data using classical and robust methods (2010, methodology): “also happen when using the Euclidean distance for standard multivariate data (Troyanskaya et al., 2001) for searching the nearest neighbors.”
Ten quick tips for machine learning in computational biology (2017, methodology): “the the k-nearest neighbors method [20] (image adapted from [72]).”
Flow Models for Arbitrary Conditional Likelihoods (2019, methodology): “Classic methods for imputation include k -nearest neighbors (Troyanskaya et al. 2001), random forest (Stekhoven and B¨uhlmann 2011) and auto-encoder (Gondara and Wang 2018) approaches.”
Imputation of missing data in life‐history trait datasets: which approach performs the best? (2014, methodology): “Among single imputation approaches, the k-Nearest Neighbour (kNN) is one of the most precise (Troyanskaya et al. 2001).”