Biomarker Identification Using Higher-Order Cumulant Networks

The patent segments the complex genotype-phenotype analysis into distinct computational components: generating higher-order joint cumulants from genotype data, filtering significant cumulant groups through statistical thresholds, and embedding these into a network structure. This segmentation allows the system to handle complex multivariate interactions systematically while maintaining analytical precision in biomarker identification.

The patent transitions from traditional pairwise association analysis to higher-order joint cumulant analysis, adding dimensional depth to the analysis. By computing cumulants of order 3 and higher, the system captures complex epistatic interactions and non-linear relationships that traditional methods miss, thereby improving biomarker identification accuracy for complex diseases without being constrained by conventional analytical dimensions.

The patent extracts only the significant higher-order joint cumulant groups from the full set of computed cumulants by applying statistical significance thresholds (e.g., p-value cutoffs). This extraction step filters out noise and redundant computations, retaining only the biologically relevant cumulant groups that contribute to biomarker identification, thereby reducing the computational burden while preserving detection accuracy.

The patent computes higher-order joint cumulants up to a predetermined order (e.g., order 4 or 5) rather than exhaustively analyzing all possible interaction orders. This partial action approach captures the most biologically relevant epistatic interactions (which typically occur at lower orders) while avoiding the exponential computational cost of higher-order analyses, thus achieving a practical balance between accuracy and computational feasibility.

The patent preserves the local quality and specificity of significant cumulant groups during network embedding by maintaining the unique interaction patterns and statistical properties of each cumulant group in the resulting network structure. Each node or edge in the embedded network retains the characteristic biological signal of its source cumulant group, ensuring that localized biological insights are not homogenized or lost in the dimensionality reduction process.

The patent performs preliminary filtering and selection of significant cumulant groups before embedding them into the lower-dimensional network. By pre-filtering cumulant groups based on statistical significance and biological relevance criteria, the system ensures that only high-quality, information-rich cumulants are embedded, thereby minimizing information loss while achieving interpretable network structures that highlight the most biologically meaningful associations.