Abstract: The present invention provides a system (200) for reducing a number of testings for a high-dimensional assay for detecting, identifying, and quantifying a plurality of analytes in a plurality of biological samples. The system is configured to (i) generate a pooling matrix for pooling and testing the plurality of biological samples, (ii) obtain an output data on completing the high-dimensional assay in each of the plurality of pools, (iii) generate a set of linear equations based on the output data and the generated pooling matrix, and (iv) convert the set of linear equations using a compressed sensing algorithm and at least one regularity condition to detect, identify, and quantify the plurality of analytes in the plurality of biological samples. The regulatory condition is sparsity with respect to a presence or an absence of each analyte separately, or a disproportionate number of samples having disproportionately high values for a particular analyte.

Abstract: Embodiments disclosed herein relate to sensing matrices and more particularly to easy-implementable sensing matrices with good recovery properties. Embodiments herein disclose methods and systems for constructing sensing matrices, wherein the sensing matrices are easy-to-implement and have good recovery properties.

Abstract: There is provided a system for detecting and quantifying a plurality of molecules in a plurality of biological samples based on a noisy output data from an assay on each pool. The system (i) generates a sensing matrix with a plurality of rows (m) and a plurality of columns (n) based on at least one input, (ii) obtains a noisy output data after completing the assay in each pool (iii) generates a probabilistic graphical model based on a non-linear equation, and (iv) detects and quantifies the molecules in the plurality of biological samples by providing the noisy output data from each pool to the probabilistic graphical model and identifying and quantifying the presence of the molecules in the plurality of biological samples by executing exact or approximate Bayesian inference for the probabilistic graphical model along with the noisy output data.

Abstract: Methods and systems for determining viruses in biological samples using a single round based pooling. Embodiments disclosed herein relate to quantitative testing of biological samples, and more particularly to a quantitative, non-adaptive and single round pooling method for testing of viruses (for example: Coronavirus disease of 2019 (COVID-19), Severe Acute Respiratory Syndrome (SARS), or the like) in biological samples of individuals.