These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
132 related articles for article (PubMed ID: 30423059)
1. An ontology-based method for assessing batch effect adjustment approaches in heterogeneous datasets. Schmidt F; List M; Cukuroglu E; Köhler S; Göke J; Schulz MH Bioinformatics; 2018 Sep; 34(17):i908-i916. PubMed ID: 30423059 [TBL] [Abstract][Full Text] [Related]
2. Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results. Rahman M; Jackson LK; Johnson WE; Li DY; Bild AH; Piccolo SR Bioinformatics; 2015 Nov; 31(22):3666-72. PubMed ID: 26209429 [TBL] [Abstract][Full Text] [Related]
3. POIBM: batch correction of heterogeneous RNA-seq datasets through latent sample matching. Holmström S; Hautaniemi S; Häkkinen A Bioinformatics; 2022 Apr; 38(9):2474-2480. PubMed ID: 35199138 [TBL] [Abstract][Full Text] [Related]
4. Detecting hidden batch factors through data-adaptive adjustment for biological effects. Yi H; Raman AT; Zhang H; Allen GI; Liu Z Bioinformatics; 2018 Apr; 34(7):1141-1147. PubMed ID: 29617963 [TBL] [Abstract][Full Text] [Related]
5. CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity. Yan X; Zheng R; Wu F; Li M Bioinformatics; 2023 Mar; 39(3):. PubMed ID: 36821425 [TBL] [Abstract][Full Text] [Related]
6. Mitigating the adverse impact of batch effects in sample pattern detection. Fei T; Zhang T; Shi W; Yu T Bioinformatics; 2018 Aug; 34(15):2634-2641. PubMed ID: 29506177 [TBL] [Abstract][Full Text] [Related]
7. SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data. Gan D; Li J Bioinformatics; 2023 Jan; 39(1):. PubMed ID: 36548380 [TBL] [Abstract][Full Text] [Related]
8. Evaluation of hierarchical models for integrative genomic analyses. Denis M; Tadesse MG Bioinformatics; 2016 Mar; 32(5):738-46. PubMed ID: 26545823 [TBL] [Abstract][Full Text] [Related]
9. Removal of batch effects using distribution-matching residual networks. Shaham U; Stanton KP; Zhao J; Li H; Raddassi K; Montgomery R; Kluger Y Bioinformatics; 2017 Aug; 33(16):2539-2546. PubMed ID: 28419223 [TBL] [Abstract][Full Text] [Related]
10. Robustifying genomic classifiers to batch effects via ensemble learning. Zhang Y; Patil P; Johnson WE; Parmigiani G Bioinformatics; 2021 Jul; 37(11):1521-1527. PubMed ID: 33245114 [TBL] [Abstract][Full Text] [Related]
11. BEENE: deep learning-based nonlinear embedding improves batch effect estimation. Rahman MA; Tutul AA; Sharmin M; Bayzid MS Bioinformatics; 2023 Aug; 39(8):. PubMed ID: 37561107 [TBL] [Abstract][Full Text] [Related]
12. Scalable preprocessing for sparse scRNA-seq data exploiting prior knowledge. Mukherjee S; Zhang Y; Fan J; Seelig G; Kannan S Bioinformatics; 2018 Jul; 34(13):i124-i132. PubMed ID: 29949988 [TBL] [Abstract][Full Text] [Related]