A Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing Under the Probabilistic Framework

Here I study noisy group testing under the probabilistic framework by modelling the infection vector as a random vector with Bernoulli entries. The main contributions include a practical one-stage group testing protocol guided by maximizing pool entropy and a maximum-likelihood recovery algorithm under the probabilistic framework. The findings highlight the implications of introducing randomness to the infection vectors – that the combinatorial structure of the pooling designs plays a less important role than the parameters such as pool size and redundancy.

Venue: AlCoB 2021 [ International Conference on Algorithms for Computational Biology ]
Publication: Springer [External Link]
Paper: [Link]
Citation: Liu, Y., Kadyan, S., Pe’er, I. (2021). A Recovery Algorithm and Pooling Designs for One-Stage Noisy Group Testing Under the Probabilistic Framework. In: Martín-Vide, C., Vega-Rodríguez, M.A., Wheeler, T. (eds) Algorithms for Computational Biology. AlCoB 2021. Lecture Notes in Computer Science(), vol 12715. Springer, Cham. https://doi.org/10.1007/978-3-030-74432-8_4