KELLER

KELLER infers dynamic gene regulatory networks from time-series gene expression data using kernel-reweighted logistic regression to detect temporal rewiring events.

Key Features:

• Temporal Evolution Capture: Captures dynamic evolution of gene regulatory networks over time by modeling changes in network structure across successive time points.
• Kernel-Reweighted Logistic Regression: Employs a kernel-reweighted logistic regression framework to handle temporal data and estimate latent sequences of network rewiring events.
• Application to Developmental Processes: Has been applied to a dataset of 588 genes from Drosophila melanogaster to estimate the sequence of temporal network rewiring during development.
• Stage-Specific Gene Functions: Identifies genes that exhibit distinct functional roles at different developmental stages by revealing stage-specific regulatory interactions.

Scientific Applications:

• Gene Network Dynamics: Enables analysis of temporal dynamics and structural evolution in gene regulatory networks.
• Predictive Modeling: Facilitates prediction of future network configurations and responses by elucidating temporal rewiring patterns.
• Developmental Biology Research: Supports investigation of regulatory mechanisms governing organismal development, as demonstrated in Drosophila melanogaster.

Methodology:

Integrates time-series gene expression data into a logistic regression framework enhanced by kernel weighting and estimates latent sequences of network rewiring to reconstruct temporal interaction networks by evaluating changes in network topology over successive time points.