1047063
Generalization of matching extensions in graphs
Abstract Let G be a graph with vertex set V(G) . Let n, k and d be non-negative integers such that n+2k+d⩽|V(G)|−2 and |V(G)|−n−d is even. A matching which covers exactly |V(G)|−d vertices of G is called a defect - d matching of G . If when deleting any n vertices of G the remaining subgraph contains a matching of k edges and every k -matching can be extended to a defect- d matching, then G is called a (n,k,d) - graph . In this paper a characterization of (n,k,d) -graphs is given and several properties (such as connectivity, minimum degree, hierarchy, etc.) of (n,k,d) -graphs are investigated.
5451258
Toughness and Perfect Matchings in Graphs
6438586
A mobile virtual-distributed system architecture for supporting wireless mobile computing and communications
This paper describes a novel mobile virtual-distrubuted system architecture for supporting global mobile computing and communications. the principal contribution of this paper is to innovatively apply virtual memory concepts to mobile systems by deploying mobile-floating agents to de-couple services and resources from the underlying network and allow them to move around following their mobile users. The mobile-floating agents maintain data structures associated with a mobile user. By combining the mobile-floating agent functions with a predictive mobility management algorithm and location-aware caching and prefetching, services and user data structure are pre-connected and pre-assigned at the locations to which the user is moving. Thus, the users can maintain their data structures or immediately receive service with virtually the same efficiency as at the previous location.
14839466
Algorithms for large-scale genotyping microarrays
MOTIVATION
Analysis of many thousands of single nucleotide polymorphisms (SNPs) across whole genome is crucial to efficiently map disease genes and understanding susceptibility to diseases, drug efficacy and side effects for different populations and individuals. High density oligonucleotide microarrays provide the possibility for such analysis with reasonable cost. Such analysis requires accurate, reliable methods for feature extraction, classification, statistical modeling and filtering.


RESULTS
We propose the modified partitioning around medoids as a classification method for relative allele signals. We use the average silhouette width, separation and other quantities as quality measures for genotyping classification. We form robust statistical models based on the classification results and use these models to make genotype calls and calculate quality measures of calls. We apply our algorithms to several different genotyping microarrays. We use reference types, informative Mendelian relationship in families, and leave-one-out cross validation to verify our results. The concordance rates with the single base extension reference types are 99.36% for the SNPs on autosomes and 99.64% for the SNPs on sex chromosomes. The concordance of the leave-one-out test is over 99.5% and is 99.9% higher for AA, AB and BB cells. We also provide a method to determine the gender of a sample based on the heterozygous call rate of SNPs on the X chromosome. See http://www.affymetrix.com for further information. The microarray data will also be available from the Affymetrix web site.


AVAILABILITY
The algorithms will be available commercially in the Affymetrix software package.
171789
A database and tool, IM Browser, for exploring and integrating emerging gene and protein interaction data for Drosophila
BackgroundBiological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets.DescriptionHere we describe a comprehensive database of Drosophila gene and protein interactions collected from a variety of sources, including low and high throughput screens, genetic interactions, and computational predictions. We also present a program for exploring multiple interaction data sets and for combining data from different sources. The program, referred to as the Interaction Map (IM) Browser, is a web-based application for searching and visualizing interaction data stored in a relational database system. Use of the application requires no downloads and minimal user configuration or training, thereby enabling rapid initial access to interaction data. IM Browser was designed to readily accommodate and integrate new types of interaction data as it becomes available. Moreover, all information associated with interaction measurements or predictions and the genes or proteins involved are accessible to the user. This allows combined searches and analyses based on either common or technique-specific attributes. The data can be visualized as an editable graph and all or part of the data can be downloaded for further analysis with other tools for specific applications. The database is available at http://proteome.wayne.edu/PIMdb.htmlConclusionThe Drosophila Interactions Database described here places a variety of disparate data into one easily accessible location. The database has a simple structure that maintains all relevant information about how each interaction was determined. The IM Browser provides easy, complete access to this database and could readily be used to publish other sets of interaction data. By providing access to all of the available information from a variety of data types, the program will also facilitate advanced computational analyses.
26898961
A mobile virtual-distributed system architecture for supporting wireless mobile computing and communications
This paper describes a novel mobile virtual-distrubuted system architecture for supporting global mobile computing and communications. the principal contribution of this paper is to innovatively apply virtual memory concepts to mobile systems by deploying mobile-floating agents to de-couple services and resources from the underlying network and allow them to move around following their mobile users. The mobile-floating agents maintain data structures associated with a mobile user. By combining the mobile-floating agent functions with a predictive mobility management algorithm and location-aware caching and prefetching, services and user data structure are pre-connected and pre-assigned at the locations to which the user is moving. Thus, the users can maintain their data structures or immediately receive service with virtually the same efficiency as at the previous location.
11364817
A class of mobile motion prediction algorithms for wireless mobile computing and communications
This paper describes a class of novel mobile motion prediction algorithms for supporting global mobile data accessing. Traditionally, mobility and routing management includes functions to passively keep track of the location of the users/terminals and to maintain connections to the terminals belonging to the system. To maintain uninterrupted high-quality service for distributed applications, it is important that a mobile system be more intelligent and can anticipate the change of the location of its user. We propose an aggressive mobility and routing management scheme, called predictive mobility management. A class of mobile motion prediction algorithms predicts the “future” location of a mobile user according to the user's movement history, i.e., previous movement patterns. By combining this scheme with mobility agent functions, the service and user routing data are actually pre-connected and pre-assigned at the locations to which the user is moving. Thus, the user can immediately receive service or data with virtually the same efficiency as at the previous location, i.e., without encountering a large “data structure handover” delay before service or data is available.