Jiaogen Zhou

CODEM: A Novel Spatial Co-location and De-location Patterns Mining Algorithm

Spatial co-location and de-location patterns represent subsets of Boolean spatial feature types whose instances are often located in close/separate geographic proximity. Existing literatures pay more attention on mining colocation patterns based on distance threshold spatial relation. In this paper, we proposed a novel co-location and de-location patterns mining algorithm (CODEM) to discover useful co-location and de-location patterns in large spatial datasets. We used k nearest features (k-NF) to measure spatial close/separate relationships of colocation/de-location patterns in spatial datasets. The k-NF set of one feature types instances was used to evaluate the close/separation relationship between other features and one feature. Then, a correlation checking operation was adopted to filter the uninteresting patterns, and moreover a grid index method was used to accelerate the k nearest features query, while a T-tree (Total support tree) structure was also used to compress the candidate frequent and infrequent item sets, and generate patterns efficiently. Experimental results prove that the algorithm is accurate and efficient, has a time complexity of O(n).

Storing and querying GML in object-relational databases

GML has become the de facto standard for electronic spatial data exchange among the applications of Web and distributed geographic information systems (GISs). As more and more geographical data is presented in GML, it is necessary to develop techniques for managing GML documents in databases. A possible solution is to store GML data into object-relational databases from which the users can retrieve the interested data. In this paper, we propose an approach to map GML schema to object-relational database schema by using GML schema graph, and algorithms for storing / querying valid GML documents into / from the relations generated by the corresponding object-relational schema. Spatial and non-spatial features embedded in GML document are stored in object-oriented relations, and the structures and constrains defined in GML schema are also well preserved. A prototype for GML documents storing and querying based on the proposed method is implemented on the basis of the Oracle/Spatial system. Preliminary experiment results shows that our method is feasible and efficient.

A distributed inverse distance weighted interpolation algorithm based on the cloud computing platform of Hadoop and its implementation

A centralized inverse distance weighted interpolation (IDW) method is simple and widely used, but it is difficult to meet the requirements of mass data processing. The cloud computing technology of Hadoop has the advantages of simple application portability, high system reliability and node dynamic load balancing. The extension of the centralized IDW to the distributed version based on Hadoop is one of the effective ways to deal with massive data processing requirements. This paper presented a distributed algorithm IDW under the MapReduce framework of the Hadoop technology. The core ideas of the algorithm are: (1) the data set to be interpolated is divided into multiple sub-data sets, and each of Map tasks run the serial IDW interpolation algorithm to interpolation a subset of the data set; (2) the Reduce task merges the interpolation results by all map tasks, and outputs the final result. Experimental results shown that the distributed IDW algorithm had good acceleration performance for large-scale data sets, and significantly improve the computational efficiency of spatial interpolation.

An effective approach to detecting both small and large complexes from protein-protein interaction networks

BackgroundPredicting protein complexes from protein-protein interaction (PPI) networks has been studied for decade. Various methods have been proposed to address some challenging issues of this problem, including overlapping clusters, high false positive/negative rates of PPI data and diverse complex structures. It is well known that most current methods can detect effectively only complexes of size ≥3, which account for only about half of the total existing complexes. Recently, a method was proposed specifically for finding small complexes (size = 2 and 3) from PPI networks. However, up to now there is no effective approach that can predict both small (size ≤ 3) and large (size >3) complexes from PPI networks.ResultsIn this paper, we propose a novel method, called CPredictor2.0, that can detect both small and large complexes under a unified framework. Concretely, we first group proteins of similar functions. Then, the Markov clustering algorithm is employed to discover clusters in each group. Finally, we merge all discovered clusters that overlap with each other to a certain degree, and the merged clusters as well as the remaining clusters constitute the set of detected complexes. Extensive experiments have shown that the new method can more effectively predict both small and large complexes, in comparison with the state-of-the-art methods.ConclusionsThe proposed method, CPredictor2.0, can be applied to accurately predict both small and large protein complexes.

Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions

BackgroundLong non-coding RNA (lncRNA) plays important roles in many biological and pathological processes, including transcriptional regulation and gene regulation. As lncRNA interacts with multiple proteins, predicting lncRNA-protein interactions (lncRPIs) is an important way to study the functions of lncRNA. Up to now, there have been a few works that exploit protein-protein interactions (PPIs) to help the prediction of new lncRPIs.ResultsIn this paper, we propose to boost the prediction of lncRPIs by fusing multiple protein-protein similarity networks (PPSNs). Concretely, we first construct four PPSNs based on protein sequences, protein domains, protein GO terms and the STRING database respectively, then build a more informative PPSN by fusing these four constructed PPSNs. Finally, we predict new lncRPIs by a random walk method with the fused PPSN and known lncRPIs. Our experimental results show that the new approach outperforms the existing methods.ConclusionFusing multiple protein-protein similarity networks can effectively boost the performance of predicting lncRPIs.

Ontology-based semantic integration of GML spatial information

GML is an XML encoding for the modeling, transport and storage of geographic information, which has been making a significant influential on the ability of organizations to share geographic information among their Web based GIS applications. Ontology has been acknowledged to be the kernel methodology for capturing and sharing semantics of spatial information. This paper proposes a framework for integrating spatial information based on GML schema matching by using ontology technologies, in which GML is adopted as the common format for spatial information wrapping and mediation, and ontology is used to overcome the heterogeneity when matching different GML schemas. A prototype is also implemented based on the proposed framework.

CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations

BackgroundEffectively predicting protein complexes not only helps to understand the structures and functions of proteins and their complexes, but also is useful for diagnosing disease and developing new drugs. Up to now, many methods have been developed to detect complexes by mining dense subgraphs from static protein-protein interaction (PPI) networks, while ignoring the value of other biological information and the dynamic properties of cellular systems.ResultsIn this paper, based on our previous works CPredictor and CPredictor2.0, we present a new method for predicting complexes from PPI networks with both gene expression data and protein functional annotations, which is called CPredictor3.0. This new method follows the viewpoint that proteins in the same complex should roughly have similar functions and are active at the same time and place in cellular systems. We first detect active proteins by using gene express data of different time points and cluster proteins by using gene ontology (GO) functional annotations, respectively. Then, for each time point, we do set intersections with one set corresponding to active proteins generated from expression data and the other set corresponding to a protein cluster generated from functional annotations. Each resulting unique set indicates a cluster of proteins that have similar function(s) and are active at that time point. Following that, we map each cluster of active proteins of similar function onto a static PPI network, and get a series of induced connected subgraphs. We treat these subgraphs as candidate complexes. Finally, by expanding and merging these candidate complexes, the predicted complexes are obtained.We evaluate CPredictor3.0 and compare it with a number of existing methods on several PPI networks and benchmarking complex datasets. The experimental results show that CPredictor3.0 achieves the highest F1-measure, which indicates that CPredictor3.0 outperforms these existing method in overall.ConclusionCPredictor3.0 can serve as a promising tool of protein complex prediction.

Side-chain dynamics analysis of KE07 series

The significant improvement of KE07 series in catalytic activities shows the great success of computational design approaches combined with directed evolution in protein design. Understanding the protein dynamics in the evolutionary optimization process of computationally designed enzyme will provide profound implication to study enzyme function and guide protein design. Here, side chain squared generalized order parameters and entropy of each protein are calculated using 50ns molecular dynamics simulation data in both apo and bound states. Our results show a correlation between the increase of side chain motion amplitude and catalytic efficiency. By analyzing the relationship between these two values, we find side chain squared generalized order parameter is linearly related to side chain entropy, which indicates the computationally designed KE07 series have similar dynamics property with natural enzymes.