SeesawPred

SeesawPred predicts transcription factors (TFs) that act as cell-fate determinants from transcriptomics data using a gene regulatory network (GRN) framework to analyze cellular differentiation.

Key Features:

• Gene Regulatory Network (GRN) model: Uses a GRN-based modeling approach to represent interactions among genes and transcription factors governing differentiation.
• Support for user-provided transcriptomics data: Accepts and analyzes user-uploaded transcriptomics datasets as input for prediction.
• Cross-species applicability: Applicable to multiple species with demonstrated examples in mouse and human differentiation contexts.
• Comparative performance: Demonstrated superior performance in comparative analyses for predicting known cell-fate determinants versus existing methods.

Scientific Applications:

• Identification of cell-fate determinants: Predicts transcription factors involved in driving progenitor-to-differentiated cell transitions.
• Developmental biology and regenerative medicine: Supports studies of regulatory mechanisms underlying cellular differentiation relevant to developmental biology and regenerative medicine.

Methodology:

Computational prediction of cell-fate determinants from user-provided transcriptomics data integrated within a gene regulatory network (GRN) framework.