| scholarly article | Q13442814 |
| P50 | author | Yungang Xu | Q42316519 |
| P2093 | author name string | Xiaobo Zhou | |
| Weiling Zhao | |||
| Jiesi Luo | |||
| Yongcui Wang | |||
| P2860 | cites work | Mapping the functional domains of BRCA1. Interaction of the ring finger domains of BRCA1 and BARD1 | Q22008789 |
| An integrated encyclopedia of DNA elements in the human genome | Q22122150 | ||
| Biases in Illumina transcriptome sequencing caused by random hexamer priming | Q24620975 | ||
| Epigenetics in alternative pre-mRNA splicing | Q24625879 | ||
| A survey of computational tools for downstream analysis of proteomic and other omic datasets | Q26778173 | ||
| Embryonic stem cell lines derived from human blastocysts | Q27861010 | ||
| Alternative isoform regulation in human tissue transcriptomes | Q27861118 | ||
| Deep learning | Q28018765 | ||
| The selection and function of cell type-specific enhancers | Q28081960 | ||
| Genome-wide analysis of PTB-RNA interactions reveals a strategy used by the general splicing repressor to modulate exon inclusion or skipping | Q28269954 | ||
| SON connects the splicing-regulatory network with pluripotency in human embryonic stem cells | Q28298055 | ||
| Integrative analysis of the Caenorhabditis elegans genome by the modENCODE project | Q28301622 | ||
| Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing | Q29547470 | ||
| Mapping and analysis of chromatin state dynamics in nine human cell types | Q29547552 | ||
| RNA processing and its regulation: global insights into biological networks | Q29615182 | ||
| RNA and disease | Q29615183 | ||
| Integrative analysis of 111 reference human epigenomes | Q29615565 | ||
| The developmental transcriptome of Drosophila melanogaster | Q29617262 | ||
| The NIH Roadmap Epigenomics Mapping Consortium | Q29619856 | ||
| A superfamily of conserved domains in DNA damage-responsive cell cycle checkpoint proteins | Q29619879 | ||
| A convex formulation for learning a shared predictive structure from multiple tasks | Q30547055 | ||
| rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data | Q30875027 | ||
| Large-scale imputation of epigenomic datasets for systematic annotation of diverse human tissues | Q41410703 | ||
| Nucleosomes are well positioned in exons and carry characteristic histone modifications | Q41863525 | ||
| Heterochromatin protein 1 (HP1a) positively regulates euchromatic gene expression through RNA transcript association and interaction with hnRNPs in Drosophila | Q41951844 | ||
| Recognition of trimethylated histone H3 lysine 4 facilitates the recruitment of transcription postinitiation factors and pre-mRNA splicing | Q41999449 | ||
| RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease | Q42558500 | ||
| Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups | Q55871746 | ||
| Chromatin organization marks exon-intron structure | Q57058000 | ||
| Alternative splicing events identified in human embryonic stem cells and neural progenitors | Q33304209 | ||
| Acetylation by the transcriptional coactivator Gcn5 plays a novel role in co-transcriptional spliceosome assembly | Q33510906 | ||
| Alternative splicing in the differentiation of human embryonic stem cells into cardiac precursors | Q33515260 | ||
| Histone modification levels are predictive for gene expression | Q33624069 | ||
| Deciphering the splicing code | Q34022324 | ||
| Regulation of alternative splicing by histone modifications | Q34035543 | ||
| Combinations of histone modifications mark exon inclusion levels | Q34126698 | ||
| Psip1/Ledgf p52 binds methylated histone H3K36 and splicing factors and contributes to the regulation of alternative splicing | Q34276472 | ||
| Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning | Q34487019 | ||
| Genome-wide analysis of alternative splicing in Caenorhabditis elegans | Q34548324 | ||
| Functional consequences of developmentally regulated alternative splicing | Q34632366 | ||
| Histone modifications are associated with transcript isoform diversity in normal and cancer cells | Q35182079 | ||
| Modeling the relationship of epigenetic modifications to transcription factor binding | Q35562092 | ||
| Predicting protein residue-residue contacts using deep networks and boosting | Q36432211 | ||
| Predicting effects of noncoding variants with deep learning-based sequence model | Q36621822 | ||
| Transcriptional and epigenetic dynamics during specification of human embryonic stem cells | Q37006585 | ||
| Lariat sequencing in a unicellular yeast identifies regulated alternative splicing of exons that are evolutionarily conserved with humans | Q37068517 | ||
| Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks | Q37076984 | ||
| Differential chromatin marking of introns and expressed exons by H3K36me3. | Q37111430 | ||
| BRCA1/FANCD2/BRG1-Driven DNA Repair Stabilizes the Differentiation State of Human Mammary Epithelial Cells | Q37170604 | ||
| Epigenomic analysis of multilineage differentiation of human embryonic stem cells | Q37205415 | ||
| MBNL proteins repress ES-cell-specific alternative splicing and reprogramming. | Q37601795 | ||
| Understanding splicing regulation through RNA splicing maps | Q37828274 | ||
| Alternative splicing in cancer: implications for biology and therapy | Q38179828 | ||
| H3K4me2 reliably defines transcription factor binding regions in different cells | Q39023626 | ||
| Computational analysis of associations between alternative splicing and histone modifications. | Q39203932 | ||
| Splicing enhances recruitment of methyltransferase HYPB/Setd2 and methylation of histone H3 Lys36. | Q39500409 | ||
| Reciprocal intronic and exonic histone modification regions in humans. | Q39634800 | ||
| Deterministic Restriction on Pluripotent State Dissolution by Cell-Cycle Pathways. | Q40671579 | ||
| Deep learning of the tissue-regulated splicing code | Q40749721 | ||
| P275 | copyright license | Creative Commons Attribution-NonCommercial 4.0 International | Q34179348 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 21 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | deep learning | Q197536 |
| P304 | page(s) | 12100-12112 | |
| P577 | publication date | 2017-09-28 | |
| P1433 | published in | Nucleic Acids Research | Q135122 |
| P1476 | title | Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision | |
| P478 | volume | 45 |
| Q89792837 | 6mA-RicePred: A Method for Identifying DNA N 6-Methyladenine Sites in the Rice Genome Based on Feature Fusion |
| Q61813259 | A Novel Protein Subcellular Localization Method With CNN-XGBoost Model for Alzheimer's Disease |
| Q58740511 | Alternative splicing links histone modifications to stem cell fate decision |
| Q57486454 | Classifying Included and Excluded Exons in Exon Skipping Event Using Histone Modifications |
| Q89529812 | Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers |
| Q92128043 | Convolutional Neural Network and Bidirectional Long Short-Term Memory-Based Method for Predicting Drug-Disease Associations |
| Q60949449 | Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs |
| Q64067898 | Dual Convolutional Neural Networks With Attention Mechanisms Based Method for Predicting Disease-Related lncRNA Genes |
| Q60582079 | Environmental influences on RNA processing: Biochemical, molecular and genetic regulators of cellular response |
| Q60915595 | Gene-Based Nonparametric Testing of Interactions Using Distance Correlation Coefficient in Case-Control Association Studies |
| Q93051882 | Gene2vec: gene subsequence embedding for prediction of mammalian N 6-methyladenosine sites from mRNA |
| Q64997830 | Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation. |
| Q64245824 | Predicting Ion Channels Genes and Their Types With Machine Learning Techniques |
| Q99237675 | Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model |
| Q91884632 | Prospect of using deep learning for predicting differentiation of myeloid progenitor cells after sepsis |
| Q58761006 | RBM4a-SRSF3-MAP4K4 Splicing Cascade Constitutes a Molecular Mechanism for Regulating Brown Adipogenesis |
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