| FBCwPlaid: A Functional Biclustering Analysis of Epi-Transcriptome Profiling Data Via a Weighted Plaid Model | 0 | 0.34 | 2022 |
| Metatx: Deciphering The Distribution Of Mrna-Related Features In The Presence Of Isoform Ambiguity, With Applications In Epitranscriptome Analysis | 0 | 0.34 | 2021 |
| Weakly Supervised Learning Of Rna Modifications From Low-Resolution Epitranscriptome Data | 0 | 0.34 | 2021 |
| ConsRM: collection and large-scale prediction of the evolutionarily conserved RNA methylation sites, with implications for the functional epitranscriptome | 0 | 0.34 | 2021 |
| Rmdisease: A Database Of Genetic Variants That Affect Rna Modifications, With Implications For Epitranscriptome Pathogenesis | 1 | 0.38 | 2021 |
| M(6)A-Atlas: A Comprehensive Knowledgebase For Unraveling The N-6-Methyladenosine (M(6)A) Epitranscriptome | 1 | 0.38 | 2021 |
| Gibbs Sampling Based Bayesian Biclustering Of Gene Expression Data | 0 | 0.34 | 2020 |
| REW-ISA: unveiling local functional blocks in epi-transcriptome profiling data via an RNA expression-weighted iterative signature algorithm. | 2 | 0.40 | 2020 |
| m7GHub: deciphering the location, regulation and pathogenesis of internal mRNA N7-methylguanosine (m7G) sites in human. | 0 | 0.34 | 2020 |
| m6Acomet: large-scale functional prediction of individual mA RNA methylation sites from an RNA co-methylation network. | 1 | 0.37 | 2019 |
| RNA methylation and diseases: experimental results, databases, Web servers and computational models. | 2 | 0.39 | 2019 |
| FunDMDeep-m6A: identification and prioritization of functional differential m6A methylation genes. | 0 | 0.34 | 2019 |
| Detection of m 6 A RNA Methylation in Nanopore Sequencing Data Using Support Vector Machine | 0 | 0.34 | 2019 |
| Met-Db V2.0: Elucidating Context-Specific Functions Of N-6-Methyl-Adenosine Methyltranscriptome | 5 | 0.52 | 2018 |
| MeTDiff: a Novel Differential RNA Methylation Analysis for MeRIP-Seq Data | 0 | 0.34 | 2018 |
| A Machine Learning Approach for Uncovering N6-methyladenosine-Disease Association. | 0 | 0.34 | 2018 |
| trumpet: transcriptome-guided quality assessment of m6A-seq data. | 0 | 0.34 | 2018 |
| A Topology-Aware Framework for Graph Traversals. | 0 | 0.34 | 2017 |
| QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model. | 2 | 0.40 | 2017 |
| Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net. | 0 | 0.34 | 2017 |
| A Topology-Adaptive Strategy for Graph Traversing | 0 | 0.34 | 2017 |
| Linguistic-valued lattice implication algebra TOPSIS method based on entropy weight method | 0 | 0.34 | 2017 |
| A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data. | 7 | 0.62 | 2016 |
| m6A-Driver: Identifying Context-Specific mRNA m6A Methylation-Driven Gene Interaction Networks. | 1 | 0.37 | 2016 |
| MeT-DB: a database of transcriptome methylation in mammalian cells. | 8 | 0.68 | 2015 |
| Sketching the distribution of transcriptomic features on RNA transcripts with Travis coordinates | 0 | 0.34 | 2015 |
| Modeling Of Replicates Variances For Detecting Rna Methylation Site In Merip-Seq Data | 0 | 0.34 | 2015 |
| Classification of imperfectly time-locked image RSVP events with EEG device. | 1 | 0.37 | 2014 |
| Differential analysis of RNA methylome with improved spatial resolution | 0 | 0.34 | 2014 |
| Detecting differentially methylated mRNA from MeRIP-Seq with likelihood ratio test. | 0 | 0.34 | 2014 |
| Integration Of Gene Expression, Genome Wide Dna Methylation, And Gene Networks For Clinical Outcome Prediction In Ovarian Cancer | 0 | 0.34 | 2013 |
| Unveiling the dynamics in RNA epigenetic regulations | 1 | 0.41 | 2013 |
| Exome-based analysis for RNA epigenome sequencing data. | 14 | 1.58 | 2013 |
| Differential analysis of rna methylation sequencing data. | 0 | 0.34 | 2013 |
| A bag-of-words model for task-load prediction from EEG in complex environments | 4 | 0.46 | 2013 |
| An HMM-based Exome Peak-finding package for RNA epigenome sequencing data. | 0 | 0.34 | 2013 |
| A Deep Learning method for classification of images RSVP events with EEG data. | 0 | 0.34 | 2013 |
| Classification of EEG recordings without perfectly time-locked events | 0 | 0.34 | 2012 |
| Understanding MicroRNA Regulation: A computational perspective. | 3 | 0.43 | 2012 |
| Exploiting correlated discriminant features in time frequency and space for characterization and robust classification of image RSVP events with EEG data | 0 | 0.34 | 2012 |
| Basis-expansion factor models for uncovering transcription factor regulatory network | 0 | 0.34 | 2012 |
| Dynamic compressive spectrum sensing for cognitive radio networks | 4 | 0.44 | 2011 |
| Collaborative Spectrum Sensing from Sparse Observations in Cognitive Radio Networks | 87 | 2.55 | 2011 |
| Uncover cooperative gene regulations by microRNAs and transcription factors in glioblastoma using a nonnegative hybrid factor model | 1 | 0.38 | 2011 |
| Clustering DNA methylation expressions using nonparametric beta mixture model | 2 | 0.50 | 2011 |
| An Iterated Conditional Modes solution for sparse Bayesian factor modeling of transcriptional regulatory networks | 0 | 0.34 | 2010 |
| An iterated conditional mode solution for Bayesian factor modeling of transcriptional regulatory networks | 0 | 0.34 | 2010 |
| Uncovering transcriptional regulatory networks by sparse Bayesian factor model | 5 | 0.73 | 2010 |
| Sparse Event Detection In Wireless Sensor Networks Using Compressive Sensing | 66 | 2.86 | 2009 |
| Enrichment constrained time-dependent clustering analysis for finding meaningful temporal transcription modules. | 7 | 0.50 | 2009 |
