Optimal Structured Matrix Approximation for Robustness to Incomplete Biosequence Data

We propose a general method for optimally approximating an arbitrary matrix $\mathbf {M}$ by a structured matrix $\mathbf {T}$ (circulant, Toeplitz/Hankel, etc.) and examine its use for estimating the spectra of genomic linkage disequilibrium matrices. This application is prototypical of a variety of genomic and proteomic problems that demand robustness to incomplete biosequence information. We perform a simulation study and corroborative test of our method using real genomic data from the Mouse Genome Database (Bult et al., 2019). The results confirm the predicted utility of the method and provide strong evidence of its potential value to a wide range of bioinformatics applications. Our optimal general matrix approximation method is expected to be of independent interest to an even broader range of applications in applied mathematics and engineering.