KMC_2017-dev/jupyter_notebooks.html at master · MaayanLab/KMC_2017-dev · GitHub

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      <p id='example_title'>Omics Mining for the Illuminating the Druggable Genome Project</p>
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      Exploratory Jupyter Notebooks
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      <p>
        Jupyter notebooks provide means to share editable code, text and figures to report data analysis processes in a transparent and reusable form. Below is an initial collection of Jupyter notebooks developed to more deeply explore the role of under-studied kinases, GPCRs and ion channels.
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      <h4>Under-studied Protein Kinases Co-expression Notebook (Nicolas Fernandez)</h4>
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        We identified a cluster of co-expressed cell cycle kinases that includes three dark kinases (PKMYT1, PKN3, and NEK4). We conclude that these under-studied kinases may be involved in the cell cycle and show that this cluster of kinases is highly expressed in hematopoietic and lymphoid tissues from CCLE, which are known to be highly proliferative. <a href="http://nbviewer.jupyter.org/github/MaayanLab/KMC_2017/blob/master/notebooks/Inter_Dark_Kinase_Function_from_Clustering.ipynb">Link to notebook.</a>
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      <h4>Under-studied Ion Channels Co-expression Notebook (Nicolas Fernandez)</h4>
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        We identified two clusters of co-expressed ion channels that are enriched for cardiac and addiction pathways, respectively. These clusters include under-studied ion channels suggesting their role in cardiac functions and the brain reward circuit.
        <a href="http://nbviewer.jupyter.org/github/MaayanLab/KMC_2017/blob/master/notebooks/Infer_Dark_Ion_Channel_Function_from_Clustering.ipynb">Link to notebook.</a>
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      <h4>Under-studied GPCRs Co-expression Notebook (Nicolas Fernandez)</h4>
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        We identified a cluster of co-expressed GPCRs with enrichment for immune-related pathways. This cluster also includes several under-studied GPCRs.
        <a href="http://nbviewer.jupyter.org/github/MaayanLab/KMC_2017/blob/master/notebooks/Infer_Dark_GPCR_Function_from_Clustering.ipynb">Link to notebook.</a>

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      <h4>Under-studied Protein Kinases as Biomarkers to Predict Outcome of Patients from TCGA Notebook (Denis Torre)</h4>
      <p>
        Gene expression and patient survival data from TCGA were used to investigate three under-studied kinases identified as part of a cell cycle co-expression cluster. The under-studied kinase PKMYT1 is negatively associated with patient survival of Kidney Renal Clear Cell Carcinoma.
        <a href="http://nbviewer.jupyter.org/github/MaayanLab/KMC_2017/blob/master/notebooks/tcga-analysis.ipynb">Link to notebook.</a>
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          <div class=" footer_text">Developed by the <a class='blue_links' target="_blank"  href="http://icahn.mssm.edu/research/labs/maayan-laboratory">Ma'ayan Lab</a> at the <a class='blue_links' target="_blank"   href="http://icahn.mssm.edu/">Icahn School of Medicine at Mount Sinai</a>  for the <a target="_blank" href="http://commonfund.nih.gov/idg/overview">KMC-IDG</a>  </div>
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