Zhao Shufang, He Yichao, Zhang Xinlu, Xu Wen, Wu Weili, Gao Suogang
1 Scientific and Educational Department, Hebei General Hospital , Shijiazhuang, China .
2 College of Information Engineering, Shijiazhuang University of Economics , Shijiazhuang, China .
J Comput Biol. 2016 Oct;23(10):821-9. doi: 10.1089/cmb.2014.0202. Epub 2016 Jul 7.
In this article, we advance a new group testing model [Formula: see text] with multiple inhibitor sets and error-tolerant and propose decoding algorithms for it to identify all its positives by using [Formula: see text]-disjunct matrix. The decoding complexity for it is [Formula: see text], where [Formula: see text]. Moreover, we extend this new group testing to threshold group testing and give the threshold group testing model [Formula: see text] with multiple inhibitor sets and error-tolerant. By using [Formula: see text]-disjunct matrix, we propose its decoding algorithms for gap g = 0 and g > 0, respectively. Finally, we point out that the new group testing is the natural generalization for the clone model.
