SpaCEM3: a software for biological module detection when data is incomplete, high dimensional and dependent

Area of Science:

• Bioinformatics
• Computational Biology
• Systems Biology

Background:

• Module detection is crucial for understanding complex biological systems.
• Classical methods often struggle with the inherent characteristics of biological data.

Purpose of the Study:

• To introduce and evaluate the SpaCEM(3) software for biological module detection.
• To highlight the software's ability to address challenges in high-dimensional biological data.

Main Methods:

• SpaCEM(3) employs ad hoc algorithms tailored for biological data.
• The software integrates methods for handling high-dimensionality and missing observations.
• SpaCEM(3) considers interactions between biological components, such as genes.

Main Results:

• SpaCEM(3) algorithms are well-adapted to specific features of biological data.
• The software effectively manages high-dimensionality and missing data.
• Gene interactions are integrated into the module detection process.

Conclusions:

• SpaCEM(3) provides advanced algorithms for biological module detection.
• The software is particularly effective for datasets with high dimensionality and missing values.
• SpaCEM(3) is available as version 2.0, developed in C++, with command-line and GUI options for Linux and Windows.