https://github.com/sahoo00/BooleanNet

Perl code for StepMiner :

stepminer.pm

stepminer.pl

LICENSE

StepMiner is a computational tool that identifies step-wise transitions in a time-series data (Sahoo et al., 2007). StepMiner performs an adaptive regression scheme to identify the best possible step up or down based on sum-of-square errors. The steps are placed between time points at the sharpest change between low expression and high expression levels, which gives insight into the timing of the gene expression-switching event. To fit a step function, the algorithm evaluates all possible step positions, and for each position, it computes the average of the values on both side of the step for the constant segments. An adaptive regression scheme is used that chooses the step positions that minimize the square error with the fitted data.

Boolean Implication Network

https://github.com/sahoo00/BooleanNet

http://genepyramid.ucsd.edu/microarray/Dynamic/

Boolean analysis is performed to determine the relationship between the expression levels of pairs of genes. The StepMiner algorithm is applied to gene expression levels to convert them into Boolean values (high and low). In a scatter plot, there are four possible quadrants based on Boolean values: (low, low), (low, high), (high, low), (high, high). A Boolean implication relationship (Sahoo et al., 2008) is observed if any one of the four possible quadrants or two diagonally opposite quadrants are sparsely populated. Based on this rule, there are six different kinds of Boolean implication relationships. Two of them are symmetric: equivalent (corresponding to the highly positively correlated genes), opposite (corresponding to the highly negatively correlated genes). Four of the Boolean relationships are asymmetric and each corresponds to one sparse quadrant: (low, low), (high, low), (low, high), (high, high).

Mining Developmentally Regulated Genes

http://genepyramid.ucsd.edu/microarray/MiDReG/

MiDReG is a method of mining developmentally regulated genes whose expression is either activated or repressed as precursor cells differentiate (Sahoo et al., 2010). MiDReG does not require gene expression data from intermediate stages of development. MiDReG is based on the gene expression patterns between the initial and terminal stages of the differentiation pathway, coupled with "if-then" rules (Boolean implications) mined from large-scale microarray databases. MiDReG uses two gene expression-based seed conditions that mark the initial and the terminal stages of a given differentiation pathway and combines the statistically inferred Boolean implications from these seed conditions to identify the relevant genes.

Hierarchical Exploration of Gene Expression Microarrays Online

https://github.com/sahoo00/Hegemon

http://hegemon.ucsd.edu/Tools/

Hegemon is a web-based visualization software for rapidly analyzing gene expression data using scatterplots.

Boolean Equivalent Correlated Clusters

http://hegemon.ucsd.edu/CellCycle/

BECC algorithm only focuses on Boolean equivalent relationships to identify potentially functionally related gene sets (Dabydeen et al., 2019, Dang et al., 2020).

Boolean Network Explorer

git clone https://github.com/sahoo00/BoNE

BoNE is a toolkit to explore and analyze the directed graph representation of biological datasets (Boolean Implication Network) and integrated with machine learning for computing predictive models. BoNE simplifies the Boolean implication network by clustering them first using Boolean Equivalent relationships. The edges between clusters are defined using overwhelming relationships observed between them. BoNE traverse the graph to discover different directed paths and chooses them using a machine learning framework to build predictive models.

git clone https://github.com/sahoo00/grade

SEG is a web based tool to automatically grade exam papers.