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.
97 related articles for article (PubMed ID: 26508990)
1. A Systematic Evaluation of Feature Selection and Classification Algorithms Using Simulated and Real miRNA Sequencing Data. Yang S; Guo L; Shao F; Zhao Y; Chen F Comput Math Methods Med; 2015; 2015():178572. PubMed ID: 26508990 [TBL] [Abstract][Full Text] [Related]
2. Predicting human microRNA precursors based on an optimized feature subset generated by GA-SVM. Wang Y; Chen X; Jiang W; Li L; Li W; Yang L; Liao M; Lian B; Lv Y; Wang S; Wang S; Li X Genomics; 2011 Aug; 98(2):73-8. PubMed ID: 21586321 [TBL] [Abstract][Full Text] [Related]
3. Cancer survival classification using integrated data sets and intermediate information. Kim S; Park T; Kon M Artif Intell Med; 2014 Sep; 62(1):23-31. PubMed ID: 24997860 [TBL] [Abstract][Full Text] [Related]
4. Deregulated microRNAs in triple-negative breast cancer revealed by deep sequencing. Chang YY; Kuo WH; Hung JH; Lee CY; Lee YH; Chang YC; Lin WC; Shen CY; Huang CS; Hsieh FJ; Lai LC; Tsai MH; Chang KJ; Chuang EY Mol Cancer; 2015 Feb; 14():36. PubMed ID: 25888956 [TBL] [Abstract][Full Text] [Related]
5. Comparisons of isomiR patterns and classification performance using the rank-based MANOVA and 10-fold cross-validation. Zhang H; Yang S; Guo L; Zhao Y; Shao F; Chen F Gene; 2015 Sep; 569(1):21-6. PubMed ID: 25447923 [TBL] [Abstract][Full Text] [Related]
6. Experimental design and data analysis of Ago-RIP-Seq experiments for the identification of microRNA targets. Tichy D; Pickl JMA; Benner A; Sültmann H Brief Bioinform; 2018 Sep; 19(5):918-929. PubMed ID: 28379479 [TBL] [Abstract][Full Text] [Related]
7. Toward a next-generation atlas of RNA secondary structure. Bai Y; Dai X; Harrison A; Johnston C; Chen M Brief Bioinform; 2016 Jan; 17(1):63-77. PubMed ID: 25922372 [TBL] [Abstract][Full Text] [Related]
8. An optimal test with maximum average power while controlling FDR with application to RNA-seq data. Si Y; Liu P Biometrics; 2013 Sep; 69(3):594-605. PubMed ID: 23889143 [TBL] [Abstract][Full Text] [Related]
9. Quantifying copy number variations using a hidden Markov model with inhomogeneous emission distributions. McCallum KJ; Wang JP Biostatistics; 2013 Jul; 14(3):600-11. PubMed ID: 23428932 [TBL] [Abstract][Full Text] [Related]
10. How does normalization impact RNA-seq disease diagnosis? Han H; Men K J Biomed Inform; 2018 Sep; 85():80-92. PubMed ID: 30041017 [TBL] [Abstract][Full Text] [Related]
11. Identification of homologous microRNAs in 56 animal genomes. Li SC; Chan WC; Hu LY; Lai CH; Hsu CN; Lin WC Genomics; 2010 Jul; 96(1):1-9. PubMed ID: 20347954 [TBL] [Abstract][Full Text] [Related]
12. Prioritizing and selecting likely novel miRNAs from NGS data. Backes C; Meder B; Hart M; Ludwig N; Leidinger P; Vogel B; Galata V; Roth P; Menegatti J; Grässer F; Ruprecht K; Kahraman M; Grossmann T; Haas J; Meese E; Keller A Nucleic Acids Res; 2016 Apr; 44(6):e53. PubMed ID: 26635395 [TBL] [Abstract][Full Text] [Related]
13. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. Xue C; Li F; He T; Liu GP; Li Y; Zhang X BMC Bioinformatics; 2005 Dec; 6():310. PubMed ID: 16381612 [TBL] [Abstract][Full Text] [Related]
14. mirExplorer: detecting microRNAs from genome and next generation sequencing data using the AdaBoost method with transition probability matrix and combined features. Guan DG; Liao JY; Qu ZH; Zhang Y; Qu LH RNA Biol; 2011; 8(5):922-34. PubMed ID: 21881406 [TBL] [Abstract][Full Text] [Related]
15. microPred: effective classification of pre-miRNAs for human miRNA gene prediction. Batuwita R; Palade V Bioinformatics; 2009 Apr; 25(8):989-95. PubMed ID: 19233894 [TBL] [Abstract][Full Text] [Related]
16. miR-RACE: an effective approach to accurately determine the sequence of computationally identified miRNAs. Wang C; Fang J Methods Mol Biol; 2015; 1296():109-18. PubMed ID: 25791595 [TBL] [Abstract][Full Text] [Related]
17. Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation. Williamson V; Kim A; Xie B; McMichael GO; Gao Y; Vladimirov V Brief Bioinform; 2013 Jan; 14(1):36-45. PubMed ID: 23334922 [TBL] [Abstract][Full Text] [Related]
18. CNCTDiscriminator: coding and noncoding transcript discriminator - an excursion through hypothesis learning and ensemble learning approaches. Biswas AK; Zhang B; Wu X; Gao JX J Bioinform Comput Biol; 2013 Oct; 11(5):1342002. PubMed ID: 24131051 [TBL] [Abstract][Full Text] [Related]
19. Expression profile analysis of microRNAs in prostate cancer by next-generation sequencing. Song C; Chen H; Wang T; Zhang W; Ru G; Lang J Prostate; 2015 Apr; 75(5):500-16. PubMed ID: 25597612 [TBL] [Abstract][Full Text] [Related]
20. Comparative analysis of methods for identifying somatic copy number alterations from deep sequencing data. Alkodsi A; Louhimo R; Hautaniemi S Brief Bioinform; 2015 Mar; 16(2):242-54. PubMed ID: 24599115 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]