Xianjun Shen

Particle Swarm Optimization with Dynamic Adaptive Inertia Weight

Aiming at the premature convergence problem of particle swarm optimization algorithm, a new particle swarm Optimization algorithm with dynamic adaptive inertia weigh was presented to solve the typical multi-peak, high dimensional function optimization problems. The dynamic adaptive strategy was introduced in this new algorithm and the change of inertia weight was formulated as an adjust function of this factor according to its impact on the search performance of the swarm. In each iteration process, the inertia weight was timely changed based on the current the swarm diversity and congregate degree, which provides the algorithm with effective dynamic adaptability. The experiments show that the proposed strategy is effectiveness.

Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network

Detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Most existing clustering algorithms for discovering protein complexes are based on static protein interaction networks in which the inherent dynamics are often overlooked. We propose a novel algorithm DPC-NADPIN (Discovering Protein Complexes based on Neighbor Affinity and Dynamic Protein Interaction Network) to identify temporal protein complexes from the time course protein interaction networks. Inspired by the idea of that the tighter a proteins neighbors inside a module connect, the greater the possibility that the protein belongs to the module, DPC-NADPIN algorithm first chooses each of the proteins with high clustering coefficient and its neighbors to consolidate into an initial cluster, and then the initial cluster becomes a protein complex by appending its neighbor proteins according to the relationship between the affinity among neighbors inside the cluster and that outside the cluster. In our experiments, DPC-NADPIN algorithm is proved to be reasonable and it has better performance on discovering protein complexes than the following state-of-the-art algorithms: Hunter, MCODE, CFinder, SPICI, and ClusterONE; Meanwhile, it obtains many protein complexes with strong biological significance, which provide helpful biological knowledge to the related researchers. Moreover, we find that proteins are assembled coordinately to form protein complexes with characteristics of temporality and spatiality, thereby performing specific biological functions.

Mining Temporal Protein Complex Based on the Dynamic PIN Weighted with Connected Affinity and Gene Co-Expression

The identification of temporal protein complexes would make great contribution to our knowledge of the dynamic organization characteristics in protein interaction networks (PINs). Recent studies have focused on integrating gene expression data into static PIN to construct dynamic PIN which reveals the dynamic evolutionary procedure of protein interactions, but they fail in practice for recognizing the active time points of proteins with low or high expression levels. We construct a Time-Evolving PIN (TEPIN) with a novel method called Deviation Degree, which is designed to identify the active time points of proteins based on the deviation degree of their own expression values. Owing to the differences between protein interactions, moreover, we weight TEPIN with connected affinity and gene co-expression to quantify the degree of these interactions. To validate the efficiencies of our methods, ClusterONE, CAMSE and MCL algorithms are applied on the TEPIN, DPIN (a dynamic PIN constructed with state-of-the-art three-sigma method) and SPIN (the original static PIN) to detect temporal protein complexes. Each algorithm on our TEPIN outperforms that on other networks in terms of match degree, sensitivity, specificity, F-measure and function enrichment etc. In conclusion, our Deviation Degree method successfully eliminates the disadvantages which exist in the previous state-of-the-art dynamic PIN construction methods. Moreover, the biological nature of protein interactions can be well described in our weighted network. Weighted TEPIN is a useful approach for detecting temporal protein complexes and revealing the dynamic protein assembly process for cellular organization.

Detecting temporal protein complexes based on Neighbor Closeness and time course protein interaction networks

The detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Inspired by the idea of that the tighter a proteins neighbors inside a module connect, the greater the possibility that the protein belongs to the module, we propose a novel clustering algorithm CNC (Clustering based on Neighbor Closeness) and apply it to the time course protein interaction networks (TCPINs) to detect temporal protein complexes. Our novel algorithm has better performance on identifying protein complexes than five state-of-the-art algorithms-Hunter, MCODE, CFinder, SPICI, and ClusterONE-in terms of matching degree and accuracy metric, meanwhile it obtains many protein complexes with strong biological significance.

An Edge-based Protein Complex Identification Algorithm With Gene Co-expression Data (PCIA-GeCo)

Recent studies have shown that protein complex is composed of core proteins and attachment proteins, and proteins inside the core are highly co-expressed. Based on this new concept, we reconstruct weighted PPI network by using gene expression data, and develop a novel protein complex identification algorithm from the angle of edge (PCIA-GeCo). First, we select the edge with high co-expressed coefficient as seed to form the preliminary cores. Then, the preliminary cores are filtered according to the weighted density of complex core to obtain the unique core. Finally, the protein complexes are generated by identifying attachment proteins for each core. A comprehensive comparison in term of F-measure, Coverage rate, P-value between our method and three other existing algorithms HUNTER, COACH and CORE has been made by comparing the predicted complexes against benchmark complexes. The evaluation results show our method PCIA-GeCo is effective; it can identify protein complexes more accurately.

Prioritizing disease-causing microbes based on random walking on the heterogeneous network

As we all know, the microbiota show remarkable variability within individuals. At the same time, those microorganisms living in the human body play a very important role in our health and disease, so the identification of the relationships between microbes and diseases will contribute to better understanding of microbes interactions, mechanism of functions. However, the microbial data which are obtained through the related technical sequencing is too much, but the known associations between the diseases and microbes are very less. In bioinformatics, many researchers choose the network topology analysis to solve these problems. Inspired by this idea, we proposed a new method for prioritization of candidate microbes to predict potential disease-microbe association. First of all, we connected the disease network and microbe network based on the known disease-microbe relationships information to construct a heterogeneous network, then we extended the random walk to the heterogeneous network, and used leave-one-out cross-validation and ROC curve to evaluate the method. In conclusion, the algorithm could be effective to disclose some potential associations between diseases and microbes that cannot be found by microbe network or disease network only. Furthermore, we studied three representative diseases, Type 2 diabetes, Asthma and Psoriasis, and finally presented the potential microbes associated with these diseases by ranking candidate disease-causing microbes, respectively. We confirmed that the discovery of the new associations will be a good clinical solution for disease mechanism understanding, diagnosis and therapy.

Mining protein complexes based on connected affinity clique extension

A novel algorithm based on Connected Affinity Clique Extension (CACE) for mining overlapping functional modules in protein interaction network is proposed in this paper. In this approach, the value of protein connected affinity is interpreted as the reliability and possibility of interaction which is inferred from protein complexes. The protein interaction network is constructed as a weighted graph, and the weigh is dependent on the connected affinity coefficient. The experimental results of our CACE in two test data sets show that the CACE can detect the functional modules much more effective and accurate compared with other state-of-art algorithms CPM and IPC-MCE.

Predicting disease-microbe association by random walking on the heterogeneous network

The microbiota living in the human body plays a very important role in our health and disease, so the identification of microbes associated with diseases will contribute to improving medical care and to better understanding of microbe functions, interactions. However, the known associations between the diseases and microbes are very less. We proposed a new method for prioritization of candidate microbes to predict disease-microbe relationships that based on the random walking on the heterogeneous network. Here, we first constructed a heterogeneous network by connecting the disease network and microbe network using the disease-microbe relationship information, then extended the random walk to the heterogeneous network, finally we used leave-one-out cross-validation to evaluate the method and ranked the candidate disease-causing microbes. We used the algorithm to disclose some potential association between disease and microbe that cannot be found by microbe network or disease network alone. Furthermore, we studied three representative diseases, Type 2 diabetes, Asthma and Psoriasis, and presented the potential microbes associated with these diseases, respectively. We confirmed that the discovery of the associations will be a good clinical solution for disease mechanism understanding, diagnosis and therapy.

A novel proteins complex identification based on connected affinity and multi-level seed extension

The identification of modules in complex networks is important for the understanding of systems. Recent studies have shown those functional modules can be identified from the protein interaction a network, whats more, the complex modules have not only relatively high density, but also have high coefficient of affinity. However, these analyses are challenging because of the presence of unreliable interactions in PPT network. In this paper, in order to mine overlapping functional modules with various and effective biological characteristics, we propose a novel algorithm based on Connected Affinity and Multi-level Seed Extension (CAMSE). First, CAMSE integrates protein-protein interactions (PPI) with the protein-protein Connected Coefficient (CC) inferred from protein complexes collected in the MIPS database to enhance the modularization and biological character of the interaction network. Then we complete the seed selection, inner kernel extensions and outer extension to get core candidate function modules step by step. Finally, we integrated the modules with high repeat rate. The experimental results show that CAMSE can detect the functional modules much more effectively and accurately when it compared with other state-of-art algorithms CPM, CACE and IPC-MCE.

Prioritizing disease-causing genes based on network diffusion and rank concordance

Disease-causing genes prioritization is very important for understanding mechanisms of diseases and biomedical applications, such as drug design. Previous studies have shown that promising candidate genes are mostly ranked according to their relatedness to known disease genes or closely related disease processes. Therefore, a dangling gene (isolated gene) with no edges in the network can not be effectively prioritized. These approaches tend to prioritize those genes that are highly connected in the PPI network and perform poorly when they are applied to loosely connected disease genes. Motivated by this observation, we propose a new disease-causing genes prioritization method that based on network diffusion and rank concordance (NDRC). The method is evaluated by leave-one-out cross validation on 1931 diseases in which at least one gene is known to be involved, and it is able to rank the true causal gene first in 849 of all 2542 cases, and as the experimental results suggest that NDRC significantly outperforms other existing methods such as RWR, VAVIEN, DADA and PRINCE on identifying loosely connected disease genes and which successfully put dangling genes as potential candidate disease genes. Furthermore, we apply NDRC method to study two representative diseases, Meckel syndrome 1 and Peroxisome biogenesis disorder 1A (Zellweger). Our study has also found that the complex disease-causing genes are divided into several modules that are closely associated with different disease phenotype.