16609037
DELPHI: A pattern-based method for detecting sequence similarity
We describe DELPHI, a new computational tool for identifying sequence similarity between a query sequence and a database of proteins. Use is made of a set of patterns obtained from the underlying database through a one-time computation. The patterns are subsequently matched against every query sequence presented to the system. A pattern matched by a region of the query pinpoints a potential local similarity between that region and all of the database sequences also matching that pattern. In a final step, all such local similarities are examined more closely by aligning and scoring the corresponding query and database regions. By prudently choosing a set of patterns, the method can be used to discover weak but biologically important similarities. We provide a number of examples using both classified and unclassified proteins that corroborate this claim.
18781820
QSAR in grossly underdetermined systems: Opportunities and issues
Regression in grossly underdetermined systems has emerged as an important means for understanding molecular activity via comparative molecular field analysis (CoMFA) and other quantitative structure activity relationship (QSAR) studies. But this methodology has applications in much broader areas; for example, near-infrared spectroscopy, mutational enzyme activity studies including protein folding rates to determine which sites are important for determining conformation, and analyses of gene expression data from chip arrays. An error analysis which answers questions concerning the quality of the predictivity, the relative importance of each descriptor, the quality of the estimates of the contribution by each descriptor, and the number of independent components expressed by the associated data is indispensable in understanding whether some particular set of structure variables is important in defining the mechanisms driving the chemical or biological activities. This paper reviews opportunities for QSAR studies. It also considers the analytical aspects of error analysis in least-squares regression, and contrasts principal component regression (PCR) and partial least-squares (PLS) procedures with cross-validation on the issues of error analysis (e.g., the quality of the contribution estimates for each structure descriptor). Further, a methodology for selecting optimal subsets of components in PCR is presented.
45061344
Space Complexity of Estimation of Distribution Algorithms
In this paper, we investigate the space complexity of the Estimation of Distribution Algorithms (EDAs), a class of sampling-based variants of the genetic algorithm. By analyzing the nature of EDAs, we identify criteria that characterize the space complexity of two typical implementation schemes of EDAs, the factorized distribution algorithm and Bayesian network-based algorithms. Using random additive functions as the prototype, we prove that the space complexity of the factorized distribution algorithm and Bayesian network-based algorithms is exponential in the problem size even if the optimization problem has a very sparse interaction structure.
11821573
Phase Transition of Tractability in Constraint Satisfaction and Bayesian Network Inference
There has been great interest in identifying tractable subclasses of NP complete problems and designing efficient algorithms for these tractable classes. Constraint satisfaction and Bayesian network inference are two examples of such problems that are of great importance in AI and algorithms. In this paper we study, under the frameworks of random constraint satisfaction problems and random Bayesian networks, a typical tractable subclass characterized by the treewidth of the problems. We show that the property of having a bounded treewidth for CSPs and Bayesian network inference problem has a phase transition that occurs while the underlying structures of problems are still sparse. This implies that algorithms making use of treewidth based structural knowledge only work efficiently in a limited range of random instance.
15321155
Line-Based Affine Invariant Object Location Using Transformation Space Decomposition
This paper presents a novel line-based affine invariant object location methodology. Our algorithm employs a new line-based transformation space decomposition technique to exploit intrinsic structural information provided by line features. Furthermore, we propose a new line-based distance transform to integrate with our algorithm to provide efficient transformation cell evaluation and subdivision in a coarse to fine manner. The algorithm is able to rapidly accelerate the searching process while maintaining high discriminative power and minimal storage requirement. The efficiency and discriminative power of this methodology are demonstrated using real-world examples with promising results
32735043
Mining Web Sequential Patterns Using Reinforcement Learning
In this paper, the problem of discovering sequential patterns from the Web log is discussed and an algorithm of sequential patterns mining is brought forward. First, the data in the Web server log file is cleaned and the temporal set about every user is constructed. After the analysis of the temporal set, Markov decision process is applied to model Web log. Then, the sequential patterns of user behaviors are mined by means of reinforcement learning technology. Finally the experiment shows that our mining algorithm is effective.
15420304
Incremental gradient descent imputation method for missing data in learning classifier systems
Learning with incomplete or missing data has been a major challenge in learning classifier system. One method for covering missing data is imputing missing values based on the statistic of known values. Another is marking them matching arbitrary case. A new approach using incremental gradient descent imputation model is proposed in this paper, which use the relationship among variables to estimate the missing value. And, some experiments are conducted in order to compare the performance of new approach and other classical covering methods.