dSimer

dSimer computes disease-disease similarity using nine methods to quantify relationships among diseases for genomics and molecular biology research.

Key Features:

• Nine similarity methods: Implements nine distinct methods for computing disease-disease similarity.
• Standard cosine similarity: Includes a cosine similarity measure for vector-space comparison of disease feature vectors.
• Function-based methods: Provides eight function-based methods to capture different biological dimensions of disease relationships.
• Visualization: Produces heatmaps and network representations of disease similarity results.
• Biological data integration: Integrates with extensive biological datasets commonly used in disease-disease association studies.

Scientific Applications:

• Disease association studies: Quantifies and visualizes similarities to identify potential genetic or environmental links between diseases.
• Interdisciplinary research: Facilitates analyses that combine genomic, molecular, and other biological data through statistical methods and visualizations.
• Software development within Bioconductor: Supports interoperability and package-based development workflows in the Bioconductor ecosystem.

Methodology:

Implemented in the R programming language and distributed as a Bioconductor package; follows Bioconductor standards including package interoperability, formal initial review, and automated testing.