Aiming at the problem that Protein-Protein Interaction (PPI) network functional module mining method based on spectral clustering and Fuzzy C-Means (FCM) clustering has low accuracy and low running efficiency, and is susceptible to false positive, a method for Functional Module mining in uncertain PPI network based on Fuzzy Spectral Clustering (FSC-FM) was proposed. Firstly, in order to overcome the effect of false positives, an uncertain PPI network was constructed, in which every protein-protein interaction was endowed with a existence probability measure by using edge aggregation coefficient. Secondly, based on edge aggregation coefficient and flow distance, the similarity calculation of spectral clustering was modified using Flow distance of Edge Clustering coefficient (FEC) strategy to overcome the sensitivity problem of the spectral clustering to the scaling parameters. Then the spectral clustering algorithm was used to preprocess the uncertain PPI network data, reducing the dimension of the data and improving the accuracy of clustering. Thirdly, Density-based Probability Center Selection (DPCS) strategy was designed to solve the problem that FCM algorithm was sensitive to the initial cluster center and clustering numbers, and the processed PPI data was clustered by using FCM algorithm to improve the running efficiency and sensitivity of the clustering. Finally, the mined functional module was filtered by Edge-Expected Density (EED) strategy. Experiments on yeast DIP dataset show that, compared with Detecting protein Complexes based on Uncertain graph model (DCU) algorithm, FSC-FM has F-measure increased by 27.92%, running efficiency increased by 27.92%; compared with an uncertain model-based approach for identifying Dynamic protein Complexes in Uncertain protein-protein interaction Networks (CDUN), Evolutionary Algorithm (EA) and Medical Gene or Protein Prediction Algorithm (MGPPA), FSC-FM also has higher F-measure and running efficiency. The experimental results show that FSC-FM is suitable for the functional module mining in the uncertain PPI network.

Concerning the problem of insufficient accuracy of the traditional image segmentation algorithm in segmentation of blue-green alga images, a new network structure named Deep Generative Adversarial Net (DGAN) based on Deep Neural Network (DNN) and Generative Adversarial Net (GAN) was proposed. Firstly, based on Fully Convolutional neural Network (FCN), a 12-layer FCN was constructed as the Generater ( G), which was used to study the distribution of data and generate the segmentation result of blue-green alga images ( Fake). Secondly, a 5-layer Convolutional Neural Network (CNN) was constructed as the Discriminator ( D), which was used to distinguish the segmentation result generated by the generated network ( Fake) and the true segmentation result with manual annotation ( Label), G tried to generate Fake and deceive D, D tried to find out Fake and punish G. Finally, through the adversarial training of two networks, a better segmentation result was obtained because Fake generated by G could cheat D. The training and test results on image sets with 3075 blue-green alga images show that, the proposed DGAN is far ahead of the iterative threshold segmentation algorithm in precision, recall and F ₁ score, which are increased by more than 4 percentage points than other DNN algorithms such as FCNNet (SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651) and Deeplab (CHEN L C, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs. Computer Science, 2014(4):357-361). The proposed DGAN has obtained more accurate segmentation results. In the aspect of segmentation speed, the DGAN needs 0.63 s per image, which is slightly slower than the traditional FCNNet with 0.46 s, but much faster than Deeplab with 1.31 s. The balanced segmentation accuracy and speed of DGAN can provide a feasible technical scheme for image-based semantic segmentation of blue-green algae.

Considering the high computation complexity and storage requirement of Naive Bayes (NB) based on Parzen Window Estimation (PWE), especially for classification on interval uncertain data, an improved method named IU-PNBC was proposed for classifying the interval uncertain data. Firstly, Class-Conditional Probability Density Function (CCPDF) was estimated by using PWE. Secondly, an approximate function for CCPDF was obtained by using algebraic interpolation. Finally, the posterior probability was computed and used for classification by using the approximate interpolation function. Artificial simulation data and UCI standard dataset were used to assume the rationality of the proposed algorithm and the affection of the interpolation points to classification accuracy of IU-PNBC. The experimental results show that: when the interpolation points are more than 15, the accuracy of IU-PNBC tends to be stable, and the accuracy increases with the increase of the interpolation points; IU-PNBC can avoid the dependence on the training samples and improve the computation efficiency effectively. Thus, IU-PNBC is suitable for classification on large interval uncertain data with lower computation complexity and storage requirement than NB based on Parzen window estimation.