6280619
Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machine
Predicting the destination of a protein in a cell is important for annotating the function of the protein. Recent advances have allowed us to develop more accurate methods for predicting the subcellular localization of proteins. One of the most important factors for improving the accuracy of these methods is related to the introduction of new useful features for protein sequences. In this paper we present a new method for extracting appropriate features from the sequence data by computing pairwise sequence alignment scores. As a classifier, support vector machine (SVM) is used. The overall prediction accuracy evaluated by the jackknife validation technique reached 94.70% for the eukaryotic non-plant data set and 92.10% for the eukaryotic plant data set, which is the highest prediction accuracy among the methods reported so far with such data sets. Our experimental results confirm that our feature extraction method based on pairwise sequence alignment is useful for this classification problem.
16441621
Tree-Dependent Components of Gene Expression Data for Clustering
Tree-dependent component analysis (TCA) is a generalization of independent component analysis (ICA), the goal of which is to model the multivariate data by a linear transformation of latent variables, while latent variables fit by a tree-structured graphical model. In contrast to ICA, TCA allows dependent structure of latent variables and also consider non-spanning trees (forests). In this paper, we present a TCA-based method of clustering gene expression data. Empirical study with yeast cell cycle-related data, yeast metabolic shift data, and yeast sporulation data; shows that TCA is more suitable for gene clustering, compared to principal component analysis (PCA) as well as ICA.
2891290
Sequence-driven features for prediction of subcellular localization of proteins
Prediction of the cellular location of a protein plays an important role in inferring the function of the protein. Feature extraction is a critical part in prediction systems, requiring raw sequence data to be transformed into appropriate numerical feature vectors while minimizing information loss. In this paper, we present a method for extracting useful features from protein sequence data. The method employs local and global pairwise sequence alignment scores as well as composition-based features. Five different features are used for training support vector machines (SVMs) separately and a weighted majority voting makes a final decision. The overall prediction accuracy evaluated by the 5-fold cross-validation reached 88.53% for the eukaryotic animal data set. Comparing the prediction accuracy of various feature extraction methods, provides a biological insight into the location of targeting information. Our experimental results confirm that our feature extraction methods are very useful for predicting subcellular localization of proteins.
9445026
Efficient Uplink Scheduler Architecture of Subscriber Station in IEEE 802.16 System
The IEEE 802.16, broadband fixed wireless access standard, defines four service classes, USG, rtPS, nrtPS and BE on high speed wireless networks. To guarantee the QoS requirement of these classes, the subscriber station and base station require scheduling architecture and algorithm. However, the IEEE 802.16 does not define any scheduling architecture or algorithm, and the most existing scheduling mechanisms only focus on working at the BS. In this paper, we propose two types of scheduling architecture working at the SS. In the one-level scheduler, we use a flow queue and class queue by differentiating flows with their class priority. The two-level scheduler can provide more organized QoS service with complementing the one-level scheduler. Adapting these architectures makes scheduler efficiently control all types of traffic defined in the IEEE 802.16. In the proposed architecture, any scheduling algorithms such as SCFQ and EDF can be applied. We evaluate the proposed scheduling architecture by simulation. The results of the simulation show that our proposed architecture can use the bandwidth efficiently.
3492450
A framework for data prefetching using off-line training of Markovian predictors
An important technique for alleviating the memory bottleneck is data prefetching. Data prefetching solutions ranging from pure software approach by inserting prefetch instructions through program analysis to purely hardware mechanisms have been proposed. The degrees of success of those techniques are dependent on the nature of the applications. The need for innovative approach is rapidly growing with the introduction of applications such as object-oriented applications that show dynamically changing memory access behavior In this paper, we propose a novel framework for the use of data prefetchers that are trained off-line using smart learning algorithms to produce prediction models which captures hidden memory access patterns. Once built, those prediction models are loaded into a data prefetching unit in the CPU at the appropriate point during the runtime to drive the prefetching. On average by using table size of about 8KB size, we were able to achieve prediction accuracy of about 68% through our own proposed learning method and performance was boosted about 37% on average on the benchmarks we tested. Furthermore, we believe our proposed framework is amenable to other predictors and can be done as a phase of the profiling-optimizing-compiler.
3979458
Video-based document tracking: unifying your physical and electronic desktops
This paper presents an approach for tracking paper documents on the desk over time and automatically linking them to the corresponding electronic documents using an overhead video camera. We demonstrate our system in the context of two scenarios, <i>paper tracking</i> and <i>photo sorting</i>. In the paper tracking scenario, the system tracks changes in the stacks of printed documents and books on the desk and builds a complete representation of the spatial structure of the desktop. When users want to find a printed document buried in the stacks, they can query the system based on appearance, keywords, or access time. The system also provides a <i>remote desktop</i> interface for directly browsing the physical desktop from a remote location. In the photo sorting scenario, users sort printed photographs into physical stacks on the desk. The systemautomatically recognizes the photographs and organizes the corresponding digital photographs into separate folders according to the physical arrangement. Our framework provides a way to unify the physical and electronic desktops without the need for a specialized physical infrastructure except for a video camera.
2306091
Classifying Protein Fingerprints
Protein fingerprints are groups of conserved motifs which can be used as diagnostic signatures to identify and characterize collections of protein sequences. These fingerprints are stored in the prints database after time-consuming annotation by domain experts who must first of all determine the fingerprint type, i.e., whether a fingerprint depicts a protein family, superfamily or domain. To alleviate the annotation bottleneck, a system called PRECIS has been developed which automatically generates prints records, provisionally stored in a supplement called preprints. One limitation of PRECIS is that its classification heuristics, handcoded by proteomics experts, often misclassify fingerprint type; their error rate has been estimated at 40%. This paper reports on an attempt to build more accurate classifiers based on information drawn from the fingerprints themselves and from the SWISS-PROT database. Extensive experimentation using 10-fold cross-validation led to the selection of a model combiing the ReliefF feature selector with an SVM-RBF learner. The final models error rate was estimated at 14.1% on a blind test set, representing a 26% accuracy gain over PRECIS handcrafted rules.
3207967
ArrayXPath: mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics
Biological pathways can provide key information on the organization of biological systems. ArrayXPath (http://www.snubi.org/software/ArrayXPath/) is a web-based service for mapping and visualizing microarray gene-expression data for integrated biological pathway resources using Scalable Vector Graphics (SVG). By integrating major bio-databases and searching pathway resources, ArrayXPath automatically maps different types of identifiers from microarray probes and pathway elements. When one inputs gene-expression clusters, ArrayXPath produces a list of the best matching pathways for each cluster. We applied Fisher's exact test and the false discovery rate (FDR) to evaluate the statistical significance of the association between a cluster and a pathway while correcting the multiple-comparison problem. ArrayXPath produces Javascript-enabled SVGs for web-enabled interactive visualization of pathways integrated with gene-expression profiles.