MADNet

MADNet performs integrated data mining and visualization of high-throughput biological datasets (microarrays, phage display, metagenome experiments) to contextualize experimental results with NCBI nucleotide and protein, KEGG, TRANSFAC, and DrugBank information.

Key Features:

• Supported data types: Supports microarrays, phage display, and metagenome experimental datasets.
• Database integration: Integrates experimental data with NCBI nucleotide and protein databases, KEGG, TRANSFAC, and DrugBank.
• Pathway mapping: Maps genes and proteins to metabolic and signaling pathways using KEGG.
• Transcriptional regulation mapping: Associates genes with transcription factors and regulatory information from TRANSFAC.
• Drug target mapping: Links genes and proteins to drug target annotations from DrugBank.
• Data mining and visualization: Performs data mining and generates visualizations of biological interactions and networks.

Scientific Applications:

• Gene expression analysis: Contextualizes microarray-derived gene expression patterns within metabolic and signaling pathways.
• Protein interaction and network exploration: Supports exploration of protein interactions and biological networks from phage display and other datasets.
• Metagenome interpretation: Interprets metagenome experimental data in the context of metabolic and signaling pathways.
• Therapeutic target identification: Identifies links between genes/proteins and DrugBank drug targets to support potential therapeutic target discovery.

Methodology:

Integrates experimental data files with NCBI nucleotide/protein, KEGG, TRANSFAC, and DrugBank for data mining and visualization of pathways, transcriptional regulation, and drug-target associations.