11334584
The rapid generation of mutation data matrices from protein sequences
An efficient means for generating mutation data matrices from large numbers of protein sequences is presented here. By means of an approximate peptide-based sequence comparison algorithm, the set sequences are clustered at the 85% identity level. The closest relating pairs of sequences are aligned, and observed amino acid exchanges tallied in a matrix. The raw mutation frequency matrix is processed in a similar way to that described by Dayhoff et al. (1978), and so the resulting matrices may be easily used in current sequence analysis applications, in place of the standard mutation data matrices, which have not been updated for 13 years. The method is fast enough to process the entire SWISS-PROT databank in 20 h on a Sun SPARCstation 1, and is fast enough to generate a matrix from a specific family or class of proteins in minutes. Differences observed between our 250 PAM mutation data matrix and the matrix calculated by Dayhoff et al. are briefly discussed.
31768671
Protein fold recognition
SummaryAn important, yet seemingly unattainable, goal in structural molecular biology is to be able to predict the native three-dimensional structure of a protein entirely from its amino acid sequence. Prediction methods based on rigorous energy calculations have not yet been successful, and best results have been obtained from homology modelling and statistical secondary structure prediction. Homology modelling is limited to cases where significant sequence similarity is shared between a protein of known structure and the unknown. Secondary structure prediction methods are not only unreliable, but also do not offer any obvious route to the full tertiary structure. Recently, methods have been developed whereby entire protein folds are recognized from sequence, even where little or no sequence similarity is shared between the proteins under consideration. In this paper we review the current methods, including our own, and in particular offer a historical background to their development. In addition, we also discuss the future of these methods and outline the developments under investigation in our laboratory.
1963957
Fast and efficient searches for effective optimization-phase sequences
It has long been known that a fixed ordering of optimization phases will not produce the best code for every application. One approach for addressing this phase-ordering problem is to use an evolutionary algorithm to search for a specific sequence of phases for each module or function. While such searches have been shown to produce more efficient code, the approach can be extremely slow because the application is compiled and possibly executed to evaluate each sequence's effectiveness. Consequently, evolutionary or iterative compilation schemes have been promoted for compilation systems targeting embedded applications where meeting strict constraints on execution time, code size, and power consumption is paramount and longer compilation times may be tolerated in the final stage of development, when an application is compiled one last time and embedded in a product. Unfortunately, even for small embedded applications, the search process can take many hours or even days making the approach less attractive to developers. In this paper, we describe two complementary general approaches for achieving faster searches for effective optimization sequences when using a genetic algorithm. The first approach reduces the search time by avoiding unnecessary executions of the application when possible. Results indicate search time reductions of 62%, on average, often reducing searches from hours to minutes. The second approach modifies the search so fewer generations are required to achieve the same results. Measurements show this approach decreases the average number of required generations by 59%. These improvements have the potential for making evolutionary compilation a viable choice for tuning embedded applications.
26900731
A new organization for a perceptron-based branch predictor and its FPGA implementation
An unaltered rearrangement of the original computation of a neural based predictor at the algorithmic level is introduced as a new organization. Its FPGA implementation generates circuits that are 1.7 faster than a direct implementation of the original algorithm. This faster clock rate allows to implement predictors with longer history lengths using the nearly the same hardware budget.
12941967
Application of splay trees to data compression
The splay-prefix algorithm is one of the simplest and fastest adaptive data compression algorithms based on the use of a prefix code. The data structures used in the splay-prefix algorithm can also be applied to arithmetic data compression. Applications of these algorithms to encryption and image processing are suggested.
7107822
Minimax disappointment criterion for video broadcasting
Users of media broadcasting services generally experience different grades of overall performance because of the non-uniform quality of their channel conditions. In order to provide all users equally satisfactory performance, we propose a new performance criterion named "minimax disappointment", based on layered service levels and the basic principle of joint source-channel coding. This criterion minimizes for all users the maximum value of performance degradation between the received performance and the expected optimal performance, given each user's individual channel situation. In support of this criterion, we develop two broadcasting systems and a gradient-based optimization scheme. Our systems achieve universally near-maximal performance for multiple user classes by providing layered service.
37553153
MultiGen: an integrated multiple-objective solution system
This paper presents an overview of a global multiple-objective system capable of rigourously handling a wide variety of multiple-objective and goal programming (GP) models. The system has two efficient solution engines that complement one another. These are an intelligent conventional optimisation system and a genetically driven heuristic search-based solver. The development of a linear and nonlinear sparse storage system capable of handling additional multiple-objective factors such as priority levels, goals, objectives, and upper and lower bounds is described. An appropriate input/output style for multiple-objective programmes is given. The resulting package provides an efficient and user-friendly environment for the interactive solution and analysis of a wide variety of multiple-objective programming (MOP) models.