oalib
Search Results: 1 - 10 of 100 matches for " "
All listed articles are free for downloading (OA Articles)
Page 1 /100
Display every page Item
Knowledge Combination in Graphical Multiagent Model  [PDF]
Quang Duong,Michael P. Wellman,Satinder Singh
Computer Science , 2012,
Abstract: A graphical multiagent model (GMM) represents a joint distribution over the behavior of a set of agents. One source of knowledge about agents' behavior may come from gametheoretic analysis, as captured by several graphical game representations developed in recent years. GMMs generalize this approach to express arbitrary distributions, based on game descriptions or other sources of knowledge bearing on beliefs about agent behavior. To illustrate the flexibility of GMMs, we exhibit game-derived models that allow probabilistic deviation from equilibrium, as well as models based on heuristic action choice. We investigate three different methods of integrating these models into a single model representing the combined knowledge sources. To evaluate the predictive performance of the combined model, we treat as actual outcome the behavior produced by a reinforcement learning process. We find that combining the two knowledge sources, using any of the methods, provides better predictions than either source alone. Among the combination methods, mixing data outperforms the opinion pool and direct update methods investigated in this empirical trial.
Combination of scoring schemes for protein docking
Philipp Heuser, Dietmar Schomburg
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-279
Abstract: The scoring with the atom specific weighting factors yields better results than the amino acid specific scoring. In combination with SVM-based scoring functions the percentage of complexes for which a near native structure can be predicted within the top 100 ranks increased from 14% with the geometric scoring to 54% with the combination of all scoring functions. Especially for the enzyme-inhibitor complexes the results of the ranking are excellent. For half of these complexes a near-native structure can be predicted within the first 10 proposed structures and for more than 86% of all enzyme-inhibitor complexes within the first 50 predicted structures.We were able to develop a combination of different scoring schemes which considers a series of previously described and some new scoring criteria yielding a remarkable improvement of prediction quality.Protein-protein interactions and complex formation play a central role in a broad range of biological processes, including hormone-receptor binding, protease inhibition, antibody-antigen interaction and signal transduction [1]. As structural genomics projects proceed, we are confronted with an increasing number of proteins with a characterised 3D structure but without a known function. To identify how two proteins are interacting will be particularly important for elucidating functions and designing inhibitors [2]. Although predicting around 50 percent false positive interactions [3], high throughput interaction discovery methods, such as the yeast two hybrid system, suggest thousands of protein-protein interactions and therefore also imply that a large fraction of all proteins interact with other proteins [4].Since many biological interactions occur in transient complexes whose structures often cannot be determined experimentally, it is important to develop computational docking methods which can predict the structure of complexes with a proper accuracy [5].Docking algorithms are developed to predict in which orientation
Protein Based Drug Discovery
Joshi B.,Gupta G.,Gupta N.,Gupta M.
International Journal of Drug Discovery , 2009,
Abstract: New drug target discovery is currently very popular with a great potential for advancingbiomedical research and chemical genomics. Drug discovery is the process of discovering and designingdrugs that includes target identification, target validation, lead identification, lead optimization andintroduction of the new drugs to the public. G protein-coupled receptors are one of the most important drugtargets. In the current scenario of drug research, approximately 60% of drug target molecules are located atthe cell surface, and half of them are GPCRs. Fragment-based drug discovery is established as analternative approach to high-throughput screening for generating novel small molecule drug candidates.Nanotechnology-based drug delivery systems have seen recent popularity due to their favorable physical,chemical, and biological properties, and great efforts have been made to target nanoDDSs to specificcellular receptors. Protein-protein interactions regulate a wide variety of important cellular pathways, andtherefore represent a highly populated class of targets for drug discovery. An analysis of individual proteinproteininteraction systems has recently yielded success in the discovery of drug-like inhibitors.
Discovery of Linguistic Relations Using Lexical Attraction  [PDF]
Deniz Yuret
Computer Science , 1998,
Abstract: This work has been motivated by two long term goals: to understand how humans learn language and to build programs that can understand language. Using a representation that makes the relevant features explicit is a prerequisite for successful learning and understanding. Therefore, I chose to represent relations between individual words explicitly in my model. Lexical attraction is defined as the likelihood of such relations. I introduce a new class of probabilistic language models named lexical attraction models which can represent long distance relations between words and I formalize this new class of models using information theory. Within the framework of lexical attraction, I developed an unsupervised language acquisition program that learns to identify linguistic relations in a given sentence. The only explicitly represented linguistic knowledge in the program is lexical attraction. There is no initial grammar or lexicon built in and the only input is raw text. Learning and processing are interdigitated. The processor uses the regularities detected by the learner to impose structure on the input. This structure enables the learner to detect higher level regularities. Using this bootstrapping procedure, the program was trained on 100 million words of Associated Press material and was able to achieve 60% precision and 50% recall in finding relations between content-words. Using knowledge of lexical attraction, the program can identify the correct relations in syntactically ambiguous sentences such as ``I saw the Statue of Liberty flying over New York.''
Gaussian Graphical Model Estimation with False Discovery Rate Control  [PDF]
Weidong Liu
Statistics , 2013,
Abstract: This paper studies the estimation of high dimensional Gaussian graphical model (GGM). Typically, the existing methods depend on regularization techniques. As a result, it is necessary to choose the regularized parameter. However, the precise relationship between the regularized parameter and the number of false edges in GGM estimation is unclear. Hence, it is impossible to evaluate their performance rigorously. In this paper, we propose an alternative method by a multiple testing procedure. Based on our new test statistics for conditional dependence, we propose a simultaneous testing procedure for conditional dependence in GGM. Our method can control the false discovery rate (FDR) asymptotically. The numerical performance of the proposed method shows that our method works quite well.
Combination of Qualitative Information with 2-Tuple Linguistic Representation in DSmT
Xin-De Li,Florentin Smarandache,Jean Dezert,Xian-Zhong Dai,
Xin-De
,Li

计算机科学技术学报 , 2009,
Abstract: Modern systems for information retrieval, fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels. In this paper, we propose and use two new 2-Tuple linguistic representation models (i.e., a distribution function model (DFM) and an improved Herrera-Martínez’s model) jointly with the fusion rules developed in Dezert-Smarandache Theory (DSmT), in order to combine efficiently qualitative information expressed in term of qualitative belief functions. The two models both preserve the precision and improve the efficiency of the fusion of linguistic information expressing the global expert’s opinion. However, DFM is more general and efficient than the latter, especially for unbalanced linguistic labels. Some simple examples are also provided to show how the 2-Tuple qualitative fusion rules are performed and their advantages.
Discriminative motif discovery in DNA and protein sequences using the DEME algorithm
Emma Redhead, Timothy L Bailey
BMC Bioinformatics , 2007, DOI: 10.1186/1471-2105-8-385
Abstract: We describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.Using artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at http://bioinformatics.org.au/deme/ webciteSequence motif discovery has been applied to discover many types of patterns in DNA and amino acid sequences. For example, motif discovery has been used extensively to elucidate putative transcription factor binding sites [1,2] and to discover protein-protein interaction domains [3]. In most cases, motif discovery algorithms take as input only a set of sequences hypothesized to contain a biologically important sequence pattern, and search f
Approach to the correlation discovery of Chinese linguistic parameters based on Bayesian method
Approach to the Correlation Discovery of Chinese Linguistic Parameters Based on Bayesian Method

Wang Wei,and Cai LianHong,
王玮
,蔡莲红

计算机科学技术学报 , 2003,
Abstract: Bayesian approach is an important method in statistics. The Bayesian belief network is a powerful knowledge representation and reasoning tool under the conditions of uncertainty. It is a graphics model that encodes probabilistic relationships among variables of interest. In this paper, an approach to Bayesian network construction is given for discovering the Chinese linguistic parameter relationship in the corpus.
Linguistic feature analysis for protein interaction extraction
Timur Fayruzov, Martine De Cock, Chris Cornelis, Veronique Hoste
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-374
Abstract: Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared.Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches.Nowadays, an overwhelming amount of experimental studies on gene and protein interactions are being conducted. The results of these experiments are most often described as scientific reports or articles and published in public knowledge repositories, such as Medline http://www.ncbi.nlm.nih.gov/ webcite. This literature database grows at a rate of 2000 publications per week, which makes it impossible for a human to track every new experiment performed in the field.Therefore, the need for automated information extraction methods in biomedicine becomes critical, and a lot of efforts are invested in creating such methods. Recently proposed approaches for interaction extraction are based not only on explicit textual information that is contained in publications, but also on a comprehensive language analysis that includes part-of-speech (POS tags) and deep syntactic structure detection. To achieve state-of-the-art performance, researchers employ lexical information (words) along with shallow syntactic information (POS) and/or deep syntactic features (grammatical structures) (see for example [1-10]).As a consequence, extraction methods tend to become more complex, use more featur
Investigating the Combination of Text and Graphical Passwords for a more secure and usable experience
C Singh,L Singh,E Marks
International Journal of Network Security & Its Applications , 2011,
Abstract: Security has been an issue from the inception of computer systems and experts have related securityissues with usability. Secured systems must be usable to maintain intended security. PasswordAuthentication Systems have either been usable and not secure, or secure and not usable. Increasingeither tends to complicate the other.Text passwords are widely used but suffer from poor usability, reducing its security. GraphicalPasswords, while usable, does not seem to have the security necessary to replace text passwords.Attempts using text or graphics only have mixed results. A combination password is proposed as apotential solution to the problem.This paper explores combination as a means of solving this password problem. We implemented threepassword systems: Text only, Graphics only and a Combination of Text and Graphics. Remoteevaluations were conducted with 105 computer science students. Results from our evaluations, thoughnot conclusive, suggest promise for combination passwords.
Page 1 /100
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.