Parallel evolution super-network DNA micro array gene data sorting system and method based on GPU

Abstract

The invention provides a parallel evolution super-network DNA micro array gene data sorting system and method based on a GPU and relates to the technical field of intelligent information processing. After preprocessing on DNA micro array data is carried out, a processed binary string is used as input information of a super-network, the super-network is initialized on a CUP of a host computer, the initialized super-network is transferred to the GPU, a hyperedge bank is divided into a plurality of sets of hyperedges, evolution studies based on a genetic algorithm are executed in parallel on the GPU respectively to acquire priori knowledge, the best hyperedge with decision-making ability is searched, and the super-network after evolution carries out classification on input samples by utilizing the hyperedges together. According to the parallel evolution super-network DNA micro array gene data sorting system and method based on the GPU, super-network parallel evolution studies based on the genetic algorithm are achieved on the GPU, study time and recognition time are short, and system execution efficiency is high. The super-network can classify the samples by utilizing a plurality of singles with the decision-making ability together, and therefore system recognition rates and generalization ability are high.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to a DNA microarray biological information classification system realized by adopting a GPU-based parallel evolution supernetwork pattern recognition technology. Background technique [0002] Cancer treatment is a difficult problem for human beings to overcome. A large number of studies in recent years have shown that cancer is a multifactorial disease, which is not only related to the patient's environment and physical signs, but also a progressive accumulation and transformation disease involving multiple genes. The occurrence of tumors is due to gene mutations in the relevant genes of diseased tissues, and the expression level of mutated genes is different from that of normal genes. In 1999, Golub et al. demonstrated differences in gene expression between tumor subtypes. Early diagnosis of tumors can be made by using gene expression profiles, and the accuracy of tum...

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Problems solved by technology

[0008] The technical problem to be solved by the present invention is: the traditional pattern recognition method cannot adapt to the classification of microarray data with high dimensions, high noise, and small samples, and the learning and recognition speed of the traditional evolutionary hypernetwork pattern recognition method is slow.

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