MOTIVATION: With the growing availability of high-throughput protein-protein interaction (PPI) data, it has become possible to consider how a protein's local or global network characteristics predict its function. RESULTS: We introduce a graph-theoretic approach that identifies key regulatory proteins in an organism by analyzing proteins' local PPI network structure. We apply the method to the yeast genome and describe several properties of the resulting set of regulatory hubs. Finally, we demonstrate how the identified hubs and putative target gene sets can be used to identify causative, functional regulators of differential gene expression linked to human disease. AVAILABILITY: Code is available at http://bcb.cs.tufts.edu/hubcomps. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Motivation: Modularity analysis is a powerful tool for studying the design of biological networks, offering potential clues for relating the biochemical function(s) of a network with the ‘wiring’ of its components. Relatively little work has been done to examine whether the modularity of a network depends on the physiological perturbations that influence its biochemical state. Here, we present a novel modularity analysis algorithm based on edge-betweenness centrality, which facilitates the use of directional information and measurable biochemical data. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Valiant's learnability model is extended to learning classes of concepts defined by regions in Euclidean space E n . The methods in this paper lead to a unified treatment of some of Valiant's results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufficient conditions are provided for feasible learnability.

Huffman's algorithm gives optimal codes, as measured by average codeword length, and the redundancy can be measured as the difference between the average codeword length and Shannon's entropy. If the objective function is replaced by an exponentially weighted average, then a simple modification of Huffman's algorithm gives optimal codes. The redundancy can now be measured as the difference between this new average and A. Renyi's (1961) generalization of Shannon's entropy. By decreasing some of the codeword lengths in a Shannon code, the upper bound on the redundancy given in the standard proof of the noiseless source coding theorem is improved. The lower bound is improved by randomizing between codeword lengths, allowing linear programming techniques to be used on an integer programming problem. These bounds are shown to be asymptotically equal. The results are generalized to the Renyi case and are related to R.G. Gallager's (1978) bound on the redundancy of Huffman codes.>

Given a finite set of texts S = { w 1, … , w k } over some fixed finite alphabet Σ, a complete inverted file for S is an abstract data type that provides the functions find ( w ), which returns the longest prefix of w that occurs (as a subword of a word) in S ; freq ( w ), which returns the number of times w occurs in S ; and locations ( w ), which returns the set of positions where w occurs in S . A data structure that implements a complete inverted file for S that occupies linear space and can be built in linear time, using the uniform-cost RAM model, is given. Using this data structure, the time for each of the above query functions is optimal. To accomplish this, techniques from the theory of finite automata and the work on suffix trees are used to build a deterministic finite automaton that recognizes the set of all subwords of the set S . This automaton is then annotated with additional information and compacted to facilitate the desired query functions. The result is a data structure that is smaller and more flexible than the suffix tree.

Constructive upper bounds are presented for minimax universal noiseless coding of unifilar sources without any ergodicity assumptionS. These bounds are obtained by quantizing the estimated probability distribution of source letters with respect to the relative entropy. They apply both to fixed-length to variable-length (FV) and variable-length to fixed-length (VF) codes. Unifilar sources are a generalization of the usual definition of Markov sources, so these results apply to Markov sources as well. These upper bounds agree asymptotically with the lower bounds given by Davisson for FV coding of stationary ergodic Markov sources.

Article Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension Share on Authors: A Blumer University of California at Santa Cruz and Department of Mathematics and Computer Science, University of Denver, Denver, Colorado University of California at Santa Cruz and Department of Mathematics and Computer Science, University of Denver, Denver, ColoradoView Profile , A Ehrenfeucht Department of Computer Science, University of Colorado, Boulder, Colorado Department of Computer Science, University of Colorado, Boulder, ColoradoView Profile , D Haussler Department of Mathematics and Computer Science, University of Denver, Denver, Colorado Department of Mathematics and Computer Science, University of Denver, Denver, ColoradoView Profile , M Warmuth Department of Computer and Information Sciences, University of California, Santa Cruz, California Department of Computer and Information Sciences, University of California, Santa Cruz, CaliforniaView Profile Authors Info & Claims STOC '86: Proceedings of the eighteenth annual ACM symposium on Theory of computingNovember 1986 Pages 273–282https://doi.org/10.1145/12130.12158Online:01 November 1986Publication History 83citation790DownloadsMetricsTotal Citations83Total Downloads790Last 12 Months58Last 6 weeks16 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Given a finite set of texts S = {ω1, ..., ωk} over some fixed finite alphabet Σ, a complete inverted file for S is an abstract data type that provides the functions find(ω), which returns the longest prefix of ω which occurs in S; freq(ω), which returns the number of times ω occurs in S; and locations(ω) which returns the set of positions at which ω occurs. We give a data structure to implement a complete inverted file for S which occupies linear space and can be built in linear time, using the uniform cost RAM model. Using this data structure, the time for each of the above query functions is optimal. To accomplish this, we use techniques from the theory of finite automata to build a deterministic finite automaton which recognizes the set of all sub words of the set S. This automaton is then annotated with additional information and compacted to facilitate the desired query functions.