一种改进的图象模糊增强技术

本文简要地讨论了传统模糊增强算法的原理,并详细讨论了这种算法所存在的缺陷。针对传统模糊增强算法的缺陷,本文提出了一种改进的模糊增强算法。实验证明,改进的的算法在图象的处理质量上得到了提高。     

基于蚁群遗传算法的DNA序列比对方法

蚁群遗传算法是在蚁群算法的基础上用遗传算法对其参数进行优化而产生的一种改进算法。把蚁群遗传算法应用于DNA序列比对上,结果表明这种新的序列比对算法是非常有效的。     

A new multi-population artificial bee algorithm based on global and local optima for numerical optimization

Artificial Bee Colony (ABC) algorithm is a nature-inspired algorithm that showed its efficiency for optimizations. However, the ABC algorithm showed some imbalances between exploration and exploitation. In order to improve the exploitation and enhance the convergence speed, a multi-population ABC algorithm based on global and local optimum (namely MPGABC) is proposed in this paper. First, in MPGABC, the initial population is generated using both chaotic systems and opposition-based learning methods. The colony in MPGABC is divided into several sub-populations to increase diversity. Moreover, the solution search mechanism is modified by introducing global and local optima in the solution search equations of both employed and onlookers. The scout bees in the proposed algorithm are generated similarly to the initial population. Finally, the proposed algorithm is compared with several state-of-art ABC algorithm variants on a set of 13 classical benchmark functions. The experimental results show that MPGABC competes and outperforms other ABC algorithm variants.

     

分子对接的随机聚点搜索算法

随机聚点搜索算法是一种普遍的全局极小化方法,在目标函数自变量数目不很大时,计算效率较高。将该算法应用于分子对接,首先要通过模型分子对接,反复调整算法各控制参数使效率最高。对于HIV－1蛋白酶与苯甲醚配体的刚性对接,算法成功的找到了相互作用能量全局极小,与晶体结构的均方根偏差(RMSD)仅0.2?。这表明,该算法可高效率找到分子对接的能量最适构型。     

Spaceborne SAR Imaging Algorithm for Coherence Optimized

This paper proposes SAR imaging algorithm with largest coherence based on the existing SAR imaging algorithm. The basic idea of SAR imaging algorithm in imaging processing is that output signal can have maximum signal-to-noise ratio (SNR) by using the optimal imaging parameters. Traditional imaging algorithm can acquire the best focusing effect, but would bring the decoherence phenomenon in subsequent interference process. Algorithm proposed in this paper is that SAR echo adopts consistent imaging parameters in focusing processing. Although the SNR of the output signal is reduced slightly, their coherence is ensured greatly, and finally the interferogram with high quality is obtained. In this paper, two scenes of Envisat ASAR data in Zhangbei are employed to conduct experiment for this algorithm. Compared with the interferogram from the traditional algorithm, the results show that this algorithm is more suitable for SAR interferometry (InSAR) research and application.     

An Improved Method with High Anti-interference Ability for R Peak Detection in Wearable Devices

The rapid development of the wearable electrocardiogram monitoring equipment increases the requirements for R peak detection in wearable devices. An improved method called ISC algorithm is proposed with high anti-interference ability for R peak detection in wearable devices based on a simple basic algorithm called SC algorithm. The proposed method is characterized by using the updated amplitude selection threshold, updated slope comparison threshold and RR interval judgement to reduce false positives and false negatives. For data from MIT-BIH Arrhythmia Database, the positive predictivity P+ of ISC algorithm can reach 99.12%, and the sensitivity Se of ISC algorithm is more than 95%. For MIT-BIH Noise Stress Test Database, the accuracy of ISC algorithm for both sensitivity Se and positive predictivity P+ can exceed 94% under three common noise, baseline wander, muscle artifact, and electrode motion artifact, where the positive predictivity P+ of ISC algorithm is 44.46% higher than that of SC algorithm on average. For wearable devices in exercise, even under the exercise intensity of 7 km per hour, the average positive predictivity P+ of ISC algorithm is 99.32%, which is 60.93% higher than that of SC algorithm. The high anti-interference ability shows that ISC algorithm is suitable for R peak detection in wearable devices.     

The merits of a parallel genetic algorithm in solving hard optimization problems

A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical and biomechanical optimization problems is compared to a sequential quadratic programming algorithm, a downhill simplex algorithm and a simulated annealing algorithm. When high-dimensional non-smooth or discontinuous problems with numerous local optima are considered, only the simulated annealing and the genetic algorithm, which are both characterized by a weak search heuristic, are successful in finding the optimal region in parameter space. The key advantage of the genetic algorithm is that it can easily be parallelized at negligible overhead.     

A novel algorithm for finding interspersed repeat regions

The analysis of repeats in the DNA sequences is an important subject in bioinformatics. In this paper, we propose a novel projection-assemble algorithm to find unknown interspersed repeats in DNA sequences. The algorithm employs random projection algorithm to obtain a candidate fragment set, and exhaustive search algorithm to search each pair of fragments from the candidate fragment set to find potential linkage, and then assemble them together. The complexity of our projection-assemble algorithm is nearly linear to the length of the genome sequence, and its memory usage is limited by the hardware. We tested our algorithm with both simulated data and real biology data, and the results show that our projection-assemble algorithm is efficient. By means of this algorithm, we found an un-labeled repeat region that occurs five times in Escherichia coil genome, with its length more than 5,000 bp, and a mismatch probability less than 4%.     

An Improved Artificial Immune Algorithm with a Dynamic Threshold

An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody＇s fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.     

OPTSDNA: Performance evaluation of an efficient distributed bioinformatics system for DNA sequence analysis

Storage of sequence data is a big concern as the amount of data generated is exponential in nature at several locations. Therefore, there is a need to develop techniques to store data using compression algorithm. Here we describe optimal storage algorithm (OPTSDNA) for storing large amount of DNA sequences of varying length. This paper provides performance analysis of optimal storage algorithm (OPTSDNA) of a distributed bioinformatics computing system for analysis of DNA sequences. OPTSDNA algorithm is used for storing various sizes of DNA sequences into database. DNA sequences of different lengths were stored by using this algorithm. These input DNA sequences are varied in size from very small to very large. Storage size is calculated by this algorithm. Response time is also calculated in this work. The efficiency and performance of the algorithm is high (in size calculation with percentage) when compared with other known with sequential approach.     

A sequence alignment algorithm with an arbitrary gap penalty function.

An algorithm for aligning biological sequences is presented that is an adaptation of the sequence generating function approach used in the statistical mechanics of biopolymers. This algorithm uses recursion relationships developed from a partition function formalism of alignment probabilities. It is implemented within a dynamic programming format that closely resembles the forward algorithm used in hidden Markov models (HMM). The algorithm aligns sequences or structures according to the statistically dominant alignment path and will be referred to as the SDP algorithm. An advantage of this method over previous ones is that it allows more complicated and physically realistic gap penalty functions to be incorporated into the algorithm in a facile manner. The performance of this algorithm in a case study of aligning the heavy and light chain from the variable region of an immunoglobulin is investigated.     

A method for prediction of conserved RNA secondary structures

The RNA secondary structure prediction is a classical problem in bioinformatics. The most efficient approach to this problem is based on the idea of a comparative analysis. In this approach the algorithms utilize multiple alignment of the RNA sequences and find common RNA structure. This paper describes a new algorithm for this task. This algorithm does not require predefined multiple alignment. The main idea of the algorithm is based on MEME-like iterative searching of abstract profile on different levels. On the first level the algorithm searches the common blocks in the RNA sequences and creates chain of this blocks. On the next step the algorithm refines the chain of common blocks. On the last stage the algorithm searches sets of common helices that have consistent locations relative to common blocks. The algorithm was tested on sets of tRNA with a subset of junk sequences and on RFN riboswitches. The algorithm is implemented as a web server (http://bioinf.fbb.msu.ru/RNAAlign/).     

基于新杂合进化算法的蛋白质折叠计算

蛋白质能量最小化是蛋白质折叠的重要内容。用于蛋白质折叠的新的杂合进化算法结合了交叉和柯西变异。基于toy模型的蛋白质能量最小化算例表明,这个新的杂合进化算法是有效的。     

A novel approach to the detection of genomic approximate tandem repeats in the Levenshtein metric.

An efficient algorithm for detecting approximate tandem repeats in genomic sequences is presented. The algorithm is based on innovative statistical criteria to detect candidate regions which may include tandem repeats; these regions are subsequently verified by alignments based on dynamic programming. No prior information about the period size or pattern is needed. Also, the algorithm is virtually capable of detecting repeats with any period. An implementation of the algorithm is compared with the two state-of-the-art tandem repeats detection tools to demonstrate its effectiveness both on natural and synthetic data. The algorithm is available at www.cs.brown.edu/people/domanic/tandem/.     

Construction of the full local similarity map for two biopolymers

A novel algorithm for construction of complete maps of local similarity for two biopolymer sequences is described. The algorithm is much faster than the related Altschul-Erickson procedure. it is implemented as the Dot-Helix module within the GeneBee package. Performance of the algorithm is exemplified with the analysis of the polyproteins of two poliovirus type 3 strains and its effectivity is compared to the Staden method. Possible applications of the algorithm are briefly discussed.     

A fast reconstruction algorithm for electron microscope tomography

We have implemented a Fast Fourier Summation algorithm for tomographic reconstruction of three-dimensional biological data sets obtained via transmission electron microscopy. We designed the fast algorithm to reproduce results obtained by the direct summation algorithm (also known as filtered or R-weighted backprojection). For two-dimensional images, the new algorithm scales as O(N(theta)M log M)+O(MN log N) operations, where N(theta) is the number of projection angles and M x N is the size of the reconstructed image. Three-dimensional reconstructions are constructed from sequences of two-dimensional reconstructions. We demonstrate the algorithm on real data sets. For typical sizes of data sets, the new algorithm is 1.5-2.5 times faster than using direct summation in the space domain. The speed advantage is even greater as the size of the data sets grows. The new algorithm allows us to use higher order spline interpolation of the data without additional computational cost. The algorithm has been incorporated into a commonly used package for tomographic reconstruction.     

Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.     

Algorithms for isolating regulatory signals in DNA sequences]

An algorithm is proposed for extracting regulatory signals from DNA sequences. The algorithm complexity is nearly quadratic. The results of testing the algorithm on artificial and natural sequences are presented.     

On random minimization of functions

A non-deterministic minimization algorithm recently proposed is analyzed. Some characteristics are analytically derived from the analysis of positive definite quadratic forms. An improvement is proposed and compared with the basic algorithm. Different variants of the basic algorithm are finally compared to a standard Conjugate Gradient minimization algorithm in the computation of the Rayleigh coefficient of a positive definite symmetric matrix.     

Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model

The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.