Research graph
References from Missing value imputation on gene expression data using bee-based algorithm to improve classification performance. Local targets link to admitted publications; unresolved targets remain external evidence.
Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization
10.1091/mbc.9.12.3273 · 1998 · External reference
High-throughput methods for detection of genetic variation
10.2144/01302tt01 · 2001 · External reference
Molecular portraits of human breast tumours
10.1038/35021093 · 2000 · External reference
Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data
10.1186/gb-2003-4-3-r21 · 2003 · External reference
Cross-species antibody microarray interrogation identifies a 3-protein panel of plasma biomarkers for early diagnosis of pancreas cancer
10.1158/1078-0432.ccr-13-3474 · 2015 · External reference
Microarray profiling shows distinct differences between primary tumors and commonly used preclinical models in hepatocellular carcinoma
10.1186/s12885-015-1814-8 · 2015 · External reference
Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning
10.1038/nm0102-68 · 2002 · External reference
Iterated local least squares microarray missing value imputation
10.1142/s0219720006002302 · 2006 · External reference
Missing value estimation methods for DNA microarrays
10.1093/bioinformatics/17.6.520 · 2001 · External reference
Gene expression during the life cycle of Drosophila melanogaster
10.1126/science.1072152 · 2002 · External reference
10.1142/9789814447362_0002
10.1142/9789814447362_0002 · 2000 · External reference
An efficient ensemble method for missing value imputation in microarray gene expression data
2021 · External reference
Missing values in big data research: some basic skills
2015 · External reference
Missing value estimation for DNA microarray gene expression data: local least squares imputation
10.1093/bioinformatics/bth499 · 2005 · External reference
SICE: an improved missing data imputation technique
2020 · External reference
PCA-based missing information imputation for real-time crash likelihood prediction under imbalanced data
10.1080/23249935.2018.1542414 · 2019 · External reference
10.1145/3375959.3375974
10.1145/3375959.3375974 · External reference
Nonlinear PCA: a new hierarchical approach
2002 · External reference
Non-linear PCA: a missing data approach
10.1093/bioinformatics/bti634 · 2005 · External reference
10.1007/978-3-540-71233-6_4
10.1007/978-3-540-71233-6_4 · External reference
10.1007/978-3-540-73750-6_2
10.1007/978-3-540-73750-6_2 · 2008 · External reference
Validation of nonlinear PCA
10.1007/s11063-012-9220-6 · 2012 · External reference
Sequential local least squares imputation estimating missing value of microarray data
10.1016/j.compbiomed.2008.08.006 · 2008 · External reference
Missing value imputation approach for mass spectrometry-based metabolomics data
2018 · External reference
Advances in water resources assessment with SWAT—an overview
2015 · External reference
A study of K-nearest neighbour as an imputation method
2002 · External reference
Missing data imputation for geolocation-based price prediction using KNN-MCF method
10.3390/ijgi9040227 · 2020 · External reference
10.1016/b978-008045157-2/50081-x
10.1016/b978-008045157-2/50081-x · 2006 · External reference
10.1109/sitis.2016.108
10.1109/sitis.2016.108 · External reference
10.1109/icci57424.2023.10111846
10.1109/icci57424.2023.10111846 · External reference
Unresolved reference
External reference
Scikit-learn: Machine Learning in Python
2011 · External reference
CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research
10.1089/cmb.2018.0238 · 2019 · External reference
Influence of data splitting on performance of machine learning models in prediction of shear strength of soil
10.1155/2021/4832864 · 2021 · External reference
The proportion for splitting data into training and test set for the bootstrap in classification problems
10.2478/bsrj-2021-0015 · 2021 · External reference
On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning
10.1007/s41664-018-0068-2 · 2018 · External reference
Why NRMSE is not completely reliable for forecast/hindcast model test performances
2013 · External reference
Impact of missing data imputation methods on gene expression clustering and classification
10.1186/s12859-015-0494-3 · 2015 · External reference
