Identify study plan steps from GEO data to protein interaction networks using the string database, then screen drugs via the Drugs in Interaction Database and perform enrichment analysis.

Procuring data from gene expression omnibus, filter for Homo sapiens and expression profiling, then explore datasets and perform initial analysis steps for downstream article preparation.

Manipulate and organize gene expression data in google sheets, apply normalization, filter by p-value 0.05, and identify overexpressed genes in Huntington's and Parkinson's disease using box plots.

Import datasets, identify differentially expressed genes common to Huntington's and Parkinson's diseases, and use a data explorer to create a Venn diagram showing shared expression patterns.

Create a protein-protein interaction network with the string tool, identify the top ten hub genes using Cytoscape, and export a publication-ready network image for further drug exploration.

Learn to export a hub gene network to Cytoscape without the String App, using short tabular text output and CSV import, then apply top ten degree and hierarchical layouts.

Identify drugs linked to your genes using the drug gene interaction database (DGIdb) by uploading a top ten list and applying toxicity and other filters to select candidates.

Learn how the updated DGIdb lets you find drugs linked to a hub gene and build an FDA-approved ligand set for docking and virtual screening.

Master drawing 3D ligand structures with Chem Sketch, save as mol file, convert to PDB using OpenBabel, and prepare structures for docking and docking-related workflows.

Learn to screen drugs using DruLiTo and OSIRIS by setting grade and interaction criteria, evaluating ADMET properties, and applying Lipinski filters to assess drug-likeness and blood-brain barrier permeability.