2024/05/15 by Hiroyasu Matsushima, Yusuke Tajima, Matsushima, Hiroyasu +5
Biochemistry, Genetics and Molecular Biology · Immunology and Microbiology · Medicine · #Advanced biosensing and bioanalysis techniques #Computation (stat.CO) #FOS: Computer and information sciences #Immunodeficiency and Autoimmune Disorders #Information Theory (cs.IT) #SARS-CoV-2 detection and testing
paper · pdf · doi:10.48550/arxiv.2405.09455
openalex publication_date 2024/05/15 · openalex created_date 2024/05/17 · openalex updated_date 2026/07/28
Group testing is utilized in the case when we want to find a few defectives among large amount of items. Testing n items one by one requires n tests, but if the ratio of defectives is small, group testing is an efficient way to reduce the number of tests. Many research have been developed for group testing for a single type of defectives. In this paper, we consider the case where two types of defective A and B exist. For two types of defectives, we develop a belief propagation algorithm to compute marginal posterior probability of defectives. Furthermore, we construct several kinds of collections of pools in order to test for A and B. And by utilizing our belief propagation algorithm, we evaluate the performance of group testing by conducting simulations.