Journal Club

Ng and Han - Efficient and Effective Clustering Methods for Spatial Data Mining

Ester et al - A density-based algorithm for discovering clusters in large spatial databases with noise

Effect of Cell Type Calling on the Predictability of DTD Estimates

Wang et al - A statistical information grid approach to spatial data mining

Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications

Vesanto et al - Clustering of the self-organizing map

Dembele et al - Fuzzy C-means method for clustering microarray data

Pilevar et al - CGHL A grid-clustering algorithm for high dimensional data

Park and Jun - A simple and fast algorithm for K-medoids clustering (PAM)

Garzon et al - A Gene Selection Approach based on Clustering

Gen et al - RECOME A new density-based clustering algorithm using relative KNN kernel density

2004 - Benito et al. - Adjustment of systematic microarray data biases

2007 - Johnson et al. - Adjusting batch effects in microarray expression data using empirical Bayes methods

2012 - Gagnon-Bartsch et al. - Using control genes to correct for unwanted variation in microarray data

2016 - Hornung et al. - Combining location-and-scale batch effect adjustment with data cleaning by latent factor adjustment

2017 - Shaham et al. - Removal of batch effects using distribution-matching residual networks

2021 - Yang et al. - SMNN batch effect correction for single-cell RNA-seq data via supervised mutual nearest neighbor detection

2021 - Zhang et al. - Robustifying Genomic Classifiers To Batch Effects Via Ensemble Learning

2023 - Yan et al. - CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity

2021 - Hettegger et al. - Random rotation for identifying differentially expressed genes with linear models following batch effect correction

lirec: R package ford _literatur research

Finding meaningful data subsets for cancer progression modeling

NVIDIAs GPU-Operator