GNIAP

GNIAP infers and assesses gene co-expression networks from mRNA gene expression, RNA-Seq, and miRNA-target gene datasets to analyze molecular interactions in cancers such as breast and prostate cancer.

Key Features:

• Gene Co-expression Network Inference: Applies multiple gene network inference (GNI) algorithms to mRNA gene expression, RNA-Seq, and miRNA-target gene datasets.
• Overlap Analysis via Literature Data: Compares inferred gene–gene interactions with literature-derived data to assess overlap and validate interactions.
• Topological Assessment: Evaluates network topology, including assessment of scale-free properties in networks derived from RNA-Seq and gene expression datasets.
• Gene Ontology-based Biological Assessment: Performs Gene Ontology-based evaluations to assess the biological relevance of inferred networks.
• Algorithm Comparison: Compares performance of different GNI algorithms across datasets and criteria, noting that microarray gene expression data yields superior overlap analysis while biological assessment results are comparable across datasets.

Scientific Applications:

• Cancer research: Supports analysis of gene interactions and molecular mechanisms in cancer, with applications in breast and prostate cancers for identification of potential therapeutic targets or biomarkers.

Methodology:

Apply selected GNI algorithms to mRNA gene expression, RNA-Seq, and miRNA-target gene datasets, followed by performance evaluations using literature overlap analysis, topological assessment (including scale-free property analysis), and Gene Ontology-based biological assessment.