14181398
A rule-based approach for RNA pseudoknot prediction
RNA plays a critical role in mediating every step of cellular information transfer from genes to functional proteins. Pseudoknots are functionally important and widely occurring structural motifs found in all types of RNA. Therefore predicting their structures is an important problem. In this paper, we present a new RNA pseudoknot structure prediction method based on term rewriting. The method is implemented using the Mfold RNA/DNA folding package and the term rewriting language Maude. In our method, RNA structures are treated as terms and rules are discovered for predicting pseudoknots. Our method was tested on 211 pseudoknots in PseudoBase and achieves an average accuracy of 74.085% compared to the experimentally determined structure. In fact, most pseudoknots discovered by our method achieve an accuracy of above 90%. These results indicate that term rewriting has a broad potential in RNA applications ranging from prediction of pseudoknots to discovery of higher level RNA structures involving complex RNA tertiary interactions.
15500084
Understanding the Prediction of Transmembrane Proteins by Support Vector Machine using Association Rule Mining
With the efforts to understand protein structure, many computational approaches have been made recently. Among them, the support vector machine (SVM) methods have been recently applied and showed successful performance compared with other machine learning schemes. However, despite the high performance, the SVM approaches suffer from the problem of understandability since it is a black-box model. To overcome this limitation, this study attempted to combine the SVM with the association rule based classifier which can present the meaningful explanation about the prediction. To perform this task, a new association rule based classifier (PCPAR) was devised based on the existing classifier, CPAR, to handle the sequential data. PCPAR creates the patterns by merging the generated rules and then classifies the sequential data based on the pattern match. The experimental result presents the following: with sequential data, the PCPAR scheme shows better performance with respect to the accuracy and the number of generated patterns than CPAR method whether applied alone or combined with SVM. The combined scheme of SVMPCPAR generates more compact patterns than the combined scheme of SVM with decision tree, SVM DT, with similar performance. These patterns are easily understandable and biologically meaningful
9889663
Feature Similarity Based Redundancy Reduction for Gene Selection
this paper we propose a feature similarity based redundancy reduction (FSRR) algorithm for high-dimensional gene expression data analysis. FSRR has two steps. First, the relevance of each feature is evaluated. Second, based on the relevance, the redundant features are removed by feature similarity. The efficiency and effectiveness of our algorithm is established through an experimental study using gene expression data. Four state-of-art feature ranking algorithms and three feature similarity measures are compared and discussed in our work. The results indicate that our algorithm has the capability of finding a well-suited feature set and improving the classification accuracy.
9363924
RNA pseudoknot prediction using term rewriting
RNA plays a critical role in mediating every step of cellular information transfer from genes to functional proteins. Pseudoknots are widely occurring structural motifs found in all types of RNA and are also functionally important. Therefore predicting their structures is an important problem. In this paper, we present a new RNA pseudoknot prediction method based on term rewriting rather than on dynamic programming, comparative sequence analysis, or context-free grammars. The method we describe is implemented using the Mfold RNA/DNA folding package and the term rewriting language Maude. Our method was tested on 211 pseudoknots in PseudoBase and achieves an average accuracy of 74.085% compared to the experimentally determined structure. In fact, most pseudoknots discovered by our method achieve an accuracy of above 90%. These results indicate that term rewriting has a broad potential in RNA applications from prediction of pseudoknots to higher level RNA structures involving complex RNA tertiary interactions.
38177882
A Hybrid Feature Selection Approach for Microarray Gene Expression Data
Due to the huge number of genes and comparatively small number of samples from microarray gene expression data, accurate classification of diseases becomes challenging. Feature selection techniques can improve the classification accuracy by removing irrelevant and redundant genes. However, the performance of different feature selection algorithms based on different theoretic arguments varies even when they are applied to the same data set. In this paper, we propose a hybrid approach to combine useful outcomes from different feature selection methods through a genetic algorithm. The experimental results demonstrate that our approach can achieve better classification accuracy with a smaller gene subset than each individual feature selection algorithm does.
24555513
Semantic Annotation for CSSCI Academic Resources Based on Ontology
The paper emphatically elaborates the process and method of realizing semantic annotation for domain data based on the CSSCI conceptualization which constructed before. The essence is that extracting the academic resource instances from CSSCI data under the guidance of the object-oriented knowledge organization model, setting values for properties of the instances by methods of directly extracting based on relational data model, directly statistic calculating assisted by TF-IDF algorithm, synthesis algorithm combined criterion weighting with formal concept analysis and so on, discovering and analyzing semantic association between instances, so as to integrate all CSSCI academic resources into a huge knowledge map based on ontology and to support the knowledge retrieval and citation analysis.
10702245
A wireless LAN-based indoor positioning technology
Context-aware computing is an emerging computing paradigm that can provide new or improved services by exploiting user context information. In this paper, we present a wireless-local-area-network-based (WLAN-based) indoor positioning technology. The wireless device deploys a position-determination model to gather location information from collected WLAN signals. A model-based signal distribution training scheme is proposed to trade off the accuracy of signal distribution and training workload. A tracking-assistant positioning algorithm is presented to employ knowledge of the area topology to assist the procedure of position determination. We have set up a positioning system at the IBM China Research Laboratory. Our experimental results indicate an accuracy of 2 m with a 90% probability for static devices and, for moving (walking) devices, an accuracy of 5 m with a 90% probability. Moreover, the complexity of the training procedure is greatly reduced compared with other positioning algorithms.
14899060
Automatic model-based service hosting environment migration
The proper operation of Service-Oriented Architecture (SOA) depends on underlying system services of operating systems, so efficient and effective migration of Service Hosting Environment is critical to cope with intrinsic-changed nature of SOA. However, due to the large amount of configuration items, complicated mapping and complex dependency relationship among system services, migrating into a new Service Hosting Environment satisfying the operation requirement of SOA becomes an error-prone and time-consuming task. The SCM project in IBM develops a novel approach to migrate Service Hosting Environment shaped in Unix-like systems. Firstly, this approach builds a set of configuration models to describe various system services. Then based on models, it presents knowledge based mapping to translate system service configurations between Service Hosting Environments. Finally, it designs a dependency hierarchy deducting algorithm to compute the dependency relationship among system services for migration traceability and error determination. A SCM prototype has performed well on largely reducing time, labor and errors in real migration cases.
18986616
Verifying Real-Time Systems using Explicit-time Description Methods
Timed model checking has been extensively researched in recent years. Many new formalisms with time extensions and tools based on them have been presented. On the other hand, Explicit-Time Description Methods aim to verify real-time systems with general untimed model checkers. Lamport presented an explicit-time description method using a clock-ticking process (Tick) to simulate the passage of time together with a group of global variables for time requirements. This paper proposes a new explicit-time description method with no reliance on global variables. Instead, it uses rendezvous synchronization steps between the Tick process and each system process to simulate time. This new method achieves better modularity and facilitates usage of more complex timing constraints. The two explicit-time description methods are implemented in DIVINE, a well-known distributed-memory model checker. Preliminary experiment results show that our new method, with better modularity, is comparable to Lamport's method with respect to time and memory efficiency.
15968114
An Efficient Explicit-time Description Method for Timed Model Checking
Timed modelchecking,the methodtoformallyverifyreal-timesystems, is attractingincreasingatten-tion from both the model checking community and the real-time community. Explicit-time descrip-tion methods verify real-time systems using general model constructs found in standard un-timedmodel checkers. Lamport proposed an explicit-time description method [17] using a clock-tickingprocess (Tick) to simulate the passage of time together with a group of global variables to model timerequirements. Two methods, the Sync-based Explicit-time Description Methodusing rendezvoussynchronization steps and the Semaphore-based Explicit-time Description Methodusing only oneglobal variable were proposed [27, 26]; they both achieve better modularity than Lamport’s methodin modelingthe real-time systems. In contrast to timed automatabased model checkerslike UPPAAL[7], explicit-time description methods can access and store the current time instant for future calcula-tions necessary for many real-time systems, especially those with pre-emptive scheduling. However,the Tick process in the above three methods increments the time by one unit in each tick; the statespaces therefore grow relatively fast as the time parameters increase, a problem when the system’stime period is relatively long. In this paper, we propose a more efﬁcient method which enables theTick process to leap multiple time units in one tick. Preliminary experimentalresults in a high perfor-mance computing environment show that this new method signiﬁcantly reduces the state space andimproves both the time and memory efﬁciency.