Detecting Tipping Points of Complex Diseases by Network Information Entropy
Description
source code and row data for paper "Detecting Tipping Points of Complex Diseases by Network Information Entropy". The progression of complex diseases often involves abrupt and non-linear changes, characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we present a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers (DNB), sample-specific network (SSN), and information entropy theories, NIEE detects critical states or tipping points in diverse data types, including bulk, single sample expression data. Demonstrating its effectiveness, we applied NIEE to several real disease datasets, successfully detecting critical points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development.
Files
Steps to reproduce
The parameters of NIEE.py are as follows: -s StringDB dataframe input (https://www.string-db.org/cgi/download) and user can use other background network instead of StringDB. -sl StringDB score limit (default: 450, range: 0-1000). -dfexpr gene expression dataframe input (row for genes, column for samples). -dfanno annotation dataframe input. -dic dictionary for ENSP to symbol (generated from Preprocessing.ipynb). -o output name.