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
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.
45158244
Discovering pattern-based subspace clusters by pattern tree
Traditional clustering models based on distance similarity are not always effective in capturing correlation among data objects, while pattern-based clustering can do well in identifying correlation hidden among data objects. However, the state-of-the-art pattern-based clustering methods are inefficient and provide no metric to measure the clustering quality. This paper presents a new pattern-based subspace clustering method, which can tackle the problems mentioned above. Observing the analogy between mining frequent itemsets and discovering subspace clusters, we apply pattern tree - a structure used in frequent itemsets mining to determining the target subspaces by scanning the database once, which can be done efficiently in large datasets. Furthermore, we introduce a general clustering quality evaluation model to guide the identifying of meaningful clusters. The proposed new method enables the users to set flexibly proper quality-control parameters to meet different needs. Experimental results on synthetic and real datasets show that our method outperforms the existing methods in both efficiency and effectiveness.
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.
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.
17705372
1D Bar Code Reading on Camera Phones
The availability of camera phones provides people with a mobile platform for decoding bar codes, whereas conventional scanners lack mobility. However, using a normal camera phone in such applications is challenging due to the out-of-focus problem. In this paper, we present the research effort on the bar code reading algorithms using a VGA camera phone, NOKIA 7650. EAN-13, a widely used 1D bar code standard, is taken as an example to show the efficiency of the method. A wavelet-based bar code region location and knowledge-based bar code segmentation scheme is applied to extract bar code characters from poor-quality images. All the segmented bar code characters are input to the recognition engine, and based on the recognition distance, the bar code character string with the smallest total distance is output as the final recognition result of the bar code. In order to train an efficient recognition engine, the modified Generalized Learning Vector Quantization (GLVQ) method is designed for optimizing a feature extraction matrix and the class reference vectors. 19 584 samples segmented from more than 1000 bar code images captured by NOKIA 7650 are involved in the training process. Testing on 292 bar code images taken by the same phone, the correct recognition rate of the entire bar code set reaches 85.62%. We are confident that auto focus or macro modes on camera phones will bring the presented method into real world mobile use.
14445176
Dynamics of Stoichiometric Bacteria-Algae Interactions in the Epilimnion
Bacteria-algae interaction in the epilimnion is modeled with the explicit consideration of carbon (energy) and phosphorus (nutrient). Global qualitative analysis and bifurcation diagrams of this model are presented. We hypothesize that there are three dynamical scenarios determined by the basic reproductive numbers of bacteria and algae. Effects of key environmental conditions are examined through these scenarios and from systematic and extensive simulations. It is also shown that excessive sunlight will destroy bacterial communities. Bifurcation diagrams for the depth of epilimnion mimic the profile of Lake Biwa, Japan. Competition of bacterial strains are modeled to examine Nishimura's hypothesis that in severely P-limited environments such as Lake Biwa, P-limitation exerts more severe constraints on the growth of bacterial groups with higher nucleic acid contents, which allows low nucleic acid bacteria to be competitive.