ConSIG

ConSIG identifies consistent molecular signatures from transcriptomic and proteomic OMIC datasets to improve robustness and biological relevance of gene and protein signatures.

Key Features:

• Consistency Enhancement: Applies consistency-enhancing algorithms to improve reproducibility of discovered gene and protein signatures.
• Optimal Signature Determination: Uses collective assessment approaches to aggregate multiple evaluations and determine an optimal molecular signature.
• Biological Relevance Confirmation: Enriches discovered signatures with disease/gene ontology annotations to confirm relevance to physiological conditions and disease etiologies.

Scientific Applications:

• Biological state characterization: Determining molecular signatures that distinguish biological states and physiological conditions.
• Disease etiology investigation: Identifying signatures relevant to disease etiology.
• Therapeutic response assessment: Deriving signatures associated with therapeutic responses.

Methodology:

Processes transcriptomic and proteomic data using consistency-enhancing algorithms and collective assessments to identify optimal signatures, followed by enrichment with disease/gene ontology annotations.