11 Machine learning approaches to genomics

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    ENCODE has applied machine learning approaches to enable integration and exploration of large and diverse data

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    Figure 2: Modelling transcription levels from histone modification and transcription-factor-binding patterns.
    Figure 5: Integration of ENCODE data by genome-wide segmentation.
    Figure 7: High-resolution segmentation of ENCODE data by self-organizing maps (SOM).
    Figure 1: Overview of the pipeline for identifying the six types of regions for one cell line.
    Figure 1: Accuracy of the TF model for predicting TSS expression levels.
    Figure 5: Cell line specificity of the TF model.
    Figure 1: TF Co-association
    Supplementary Figure 18: Using a self-organizing map to cluster DHSs by cross-cell-type pattern.
    Supplementary Figure 19
    Supplementary Figure 20: Instance counts of patterns discovered by the SOM (Supp. Fig. 18)
    Figure 1: Schematic of models to predict transcription factor occupancy from sequence and chromatin.
    Figure 2: SVM sequence models better predict binding sites than traditional motif approaches.
    Figure 3: SVM spatial chromatin models better predict binding sites than simpler models.
    Figure 3: Identification and directional classification of novel promoters.
    Supplementary Figure 9: Overlaps between novel promoters, CAGE clusters, and ESTs.
    Supplementary Figure 10: Additional examples of novel promoters identified in K562 cells.

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    11 Machine learning approaches to genomics. Nature (2019) doi:10.1038/nature28180

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