scholarly journals An Entropy-Based Position Projection Algorithm for Motif Discovery

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yipu Zhang ◽  
Ping Wang ◽  
Maode Yan
Keyword(s):  
Dna Sequences ◽  
Motif Discovery ◽  
Optimal Solution ◽  
Training Model ◽  
Projection Algorithm ◽  
Local Optimum ◽  
The Past ◽  
Local Optimal Solution ◽  
And Function ◽  
Discovery Algorithms

Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an entropy-based position projection algorithm, called EPP, which designs a projection process to divide the dataset and explores the best local optimal solution. The experimental results on real DNA sequences, Tompa data, and ChIP-seq data show that EPP is advantageous in dealing with the motif discovery problem and outperforms current widely used algorithms.

Comparative Analysis of DNA Motif Discovery Algorithms: A Systemic Review

2019 ◽  
Vol 15 (1) ◽  
pp. 4-26
Author(s):  
Fatma A. Hashim ◽  
Mai S. Mabrouk ◽  
Walid A.L. Atabany
Keyword(s):  
Dna Sequences ◽  
Motif Discovery ◽  
Probabilistic Approach ◽  
Biological Data ◽  
Systemic Review ◽  
Local Optimum ◽  
Dna Motif ◽  
Functional Features ◽  
Dna Motif Discovery ◽  
Discovery Algorithms

Background: Bioinformatics is an interdisciplinary field that combines biology and information technology to study how to deal with the biological data. The DNA motif discovery problem is the main challenge of genome biology and its importance is directly proportional to increasing sequencing technologies which produce large amounts of data. DNA motif is a repeated portion of DNA sequences of major biological interest with important structural and functional features. Motif discovery plays a vital role in the antibody-biomarker identification which is useful for diagnosis of disease and to identify Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Recently, scientists discovered that the TFs have a mutation rate five times higher than the flanking sequences, so motif discovery also has a crucial role in cancer discovery. Methods: Over the past decades, many attempts use different algorithms to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approach. Results: Many of DNA motif discovery algorithms are time-consuming and easily trapped in a local optimum. Conclusion: Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome the problems of consensus and probabilistic approaches. This paper presents a general classification of motif discovery algorithms with new sub-categories. It also presents a summary comparison between them.

Motif discovery algorithms in static and temporal networks: A survey

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Ali Jazayeri ◽  
Christopher C Yang
Keyword(s):  
Complex Systems ◽  
Motif Discovery ◽  
Temporal Networks ◽  
Frequent Subgraph Mining ◽  
The Core ◽  
The Past ◽  
Frequent Subgraph ◽  
Social Media Platforms ◽  
Discovery Algorithms ◽  
Big Data Technology

Abstract Motifs are the fundamental components of complex systems. The topological structure of networks representing complex systems and the frequency and distribution of motifs in these networks are intertwined. The complexities associated with graph and subgraph isomorphism problems, as the core of frequent subgraph mining, directly impact the performance of motif discovery algorithms. Researchers have adopted different strategies for candidate generation and enumeration and frequency computation to cope with these complexities. Besides, in the past few years, there has been an increasing interest in the analysis and mining of temporal networks. In contrast to their static counterparts, these networks change over time in the form of insertion, deletion or substitution of edges or vertices or their attributes. In this article, we provide a survey of motif discovery algorithms proposed in the literature for mining static and temporal networks and review the corresponding algorithms based on their adopted strategies for candidate generation and frequency computation. As we witness the generation of a large amount of network data in social media platforms, bioinformatics applications and communication and transportation networks and the advance in distributed computing and big data technology, we also conduct a survey on the algorithms proposed to resolve the CPU-bound and I/O bound problems in mining static and temporal networks.

scholarly journals An Affinity Propagation-Based DNA Motif Discovery Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chunxiao Sun ◽  
Hongwei Huo ◽  
Qiang Yu ◽  
Haitao Guo ◽  
Zhigang Sun
Keyword(s):  
Dna Sequences ◽  
Motif Discovery ◽  
Simulated Data ◽  
Biological Data ◽  
Affinity Propagation ◽  
Local Optimum ◽  
Data Sets ◽  
Dna Motif ◽  
Challenging Tasks ◽  
Dna Motif Discovery

The planted(l,d)motif search (PMS) is one of the fundamental problems in bioinformatics, which plays an important role in locating transcription factor binding sites (TFBSs) in DNA sequences. Nowadays, identifying weak motifs and reducing the effect of local optimum are still important but challenging tasks for motif discovery. To solve the tasks, we propose a new algorithm, APMotif, which first applies the Affinity Propagation (AP) clustering in DNA sequences to produce informative and good candidate motifs and then employs Expectation Maximization (EM) refinement to obtain the optimal motifs from the candidate motifs. Experimental results both on simulated data sets and real biological data sets show that APMotif usually outperforms four other widely used algorithms in terms of high prediction accuracy.

scholarly journals Multiobjective Cognitive Cooperative Jamming Decision-Making Method Based on Tabu Search-Artificial Bee Colony Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Fang Ye ◽  
Fei Che ◽  
Lipeng Gao
Keyword(s):  
Tabu Search ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimal Solution ◽  
Local Optimum ◽  
Tabu List ◽  
Cooperative Jamming ◽  
Bee Colony ◽  
Local Optimal Solution ◽  
The One

For the future information confrontation, a single jamming mode is not effective due to the complex electromagnetic environment. Selecting the appropriate jamming decision to coordinately allocate the jamming resources is the development direction of the electronic countermeasures. Most of the existing studies about jamming decision only pay attention to the jamming benefits, while ignoring the jamming cost. In addition, the conventional artificial bee colony algorithm takes too many iterations, and the improved ant colony (IAC) algorithm is easy to fall into the local optimal solution. Against the issue, this paper introduces the concept of jamming cost in the cognitive collaborative jamming decision model and refines it as a multiobjective one. Furthermore, this paper proposes a tabu search-artificial bee colony (TSABC) algorithm to cognitive cooperative-jamming decision. It introduces the tabu list into the artificial bee colony (ABC) algorithm and stores the solution that has not been updated after a certain number of searches into the tabu list to avoid meeting them when generating a new solution, so that this algorithm reduces the unnecessary iterative process, and it is not easy to fall into a local optimum. Simulation results show that the search ability and probability of finding the optimal solution of the new algorithm are better than the other two. It has better robustness, which is better in the “one-to-many” jamming mode.

scholarly journals An Improved Particle Swarm Optimization with Gaussian Disturbance

2018 ◽  
Vol 232 ◽  
pp. 03015
Author(s):  
Changjun Wen ◽  
Changlian Liu ◽  
Heng Zhang ◽  
Hongliang Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Local Optimal Solution ◽  
Gaussian Disturbance ◽  
Griewank Function

The particle swarm optimization (PSO) is a widely used tool for solving optimization problems in the field of engineering technology. However, PSO is likely to fall into local optimum, which has the disadvantages of slow convergence speed and low convergence precision. In view of the above shortcomings, a particle swarm optimization with Gaussian disturbance is proposed. With introducing the Gaussian disturbance in the self-cognition part and social cognition part of the algorithm, this method can improve the convergence speed and precision of the algorithm, which can also improve the ability of the algorithm to escape the local optimal solution. The algorithm is simulated by Griewank function after the several evolutionary modes of GDPSO algorithm are analyzed. The experimental results show that the convergence speed and the optimization precision of the GDPSO is better than that of PSO.

Dynamic Neighborhood Particle Swarm Optimization Based on External Archive

2013 ◽  
Vol 333-335 ◽  
pp. 1374-1378
Author(s):  
Shu Xia Dong ◽  
Liang Tang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Optimal Position ◽  
Von Neumann ◽  
External Archive ◽  
Local Optimal Solution ◽  
Multimodal Problems

According to the defect of falling into a local optimum when dealing with multimodal problems with basic particle swarm optimization, a dynamic neighborhood particle swarm optimization with external archive (EA-DPSO) is proposed. The Ring topology, All topology and Von Neumann topology are adopted, and dynamically refining particle history optimal position, and then store them on the external archive. In terms of particles characteristics in the external archive, a kind of effective extract mechanism method is designed to choose learning sample. Three peak problems as simulation function are chosen and the results show that EA-DPSO can effectively jump out of local optimal solution. Therefore, it can be seen as an effective algorithm for solving multimodal problems.

Osseointegration interface of calcified bone and endosseous implants

1992 ◽  
Vol 50 (2) ◽  
pp. 1096-1097
Author(s):  
K.E. Krizan ◽  
J.E. Laffoon ◽  
M.J. Buckley
Keyword(s):  
Structure And Function ◽  
Titanium Implants ◽  
En Bloc ◽  
Endosseous Implants ◽  
Ultrastructural Studies ◽  
The Past ◽  
Cacodylate Buffer ◽  
One Year ◽  
And Function

With increase use of tissue-integrated prostheses in recent years it is a goal to understand what is happening at the interface between haversion bone and bulk metal. This study uses electron microscopy (EM) techniques to establish parameters for osseointegration (structure and function between bone and nonload-carrying implants) in an animal model. In the past the interface has been evaluated extensively with light microscopy methods. Today researchers are using the EM for ultrastructural studies of the bone tissue and implant responses to an in vivo environment. Under general anesthesia nine adult mongrel dogs received three Brånemark (Nobelpharma) 3.75 × 7 mm titanium implants surgical placed in their left zygomatic arch. After a one year healing period the animals were injected with a routine bone marker (oxytetracycline), euthanized and perfused via aortic cannulation with 3% glutaraldehyde in 0.1M cacodylate buffer pH 7.2. Implants were retrieved en bloc, harvest radiographs made (Fig. 1), and routinely embedded in plastic. Tissue and implants were cut into 300 micron thick wafers, longitudinally to the implant with an Isomet saw and diamond wafering blade [Beuhler] until the center of the implant was reached.

Kedudukan dan Fungsi Bahasa dalam Permuseuman

Metahumaniora ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 54
Author(s):  
Erlina Zulkifli Mahmud ◽  
Taufik Ampera ◽  
Yuyu Yohana Risagarniwa ◽  
Inu Isnaeni Sidiq
Keyword(s):  
Human Life ◽  
Field Research ◽  
Communicative Function ◽  
Language Functions ◽  
The Past ◽  
Library Research ◽  
History Of ◽  
And Function ◽  
Museum Guides ◽  
Bahasa Indonesia

Kedudukan dan fungsi bahasa sebagai alat komunikasi manusia mencakup seluruh bidang kehidupan termasuk ilmu pengetahuan antara lain terkait sejarah peradaban manusia; bagaimana manusia mempertahankan hidupnya, bagaimana manusia memperlakukan alam, bagaimana alam menyediakan segala kebutuhan manusia. Apa yang dilakukan manusia saat ini, saat lampau, dan apa yang dilakukan manusia jauh di masa prasejarah, bagaimana kondisi alam di masa-masa tersebut, apa perubahan dan perkembangannya, dapat didokumentasikan melalui bahasa, divisualisasikan kembali, lalu dipajang sebagai salah satu upaya konversai dan preservasi dalam satu institusi yang disebut museum. Penelitian ini membahas kedudukan dan fungsi bahasa dalam permuseuman. Bagaimana kedudukan dan fungsi bahasa dalam permuseuman baik dalam informasi yang disampaikan oleh pemandu wisata museumnya maupun yang terpajang menyertai benda-benda dan gambar-gambar merupakan tujuan dari penelitian ini. Metode penelitian yang digunakan adalah gabungan antara metode lapangan dan metode literatur. Hasil penelitian menunjukkan bahwa secara umum kedudukan bahasa Indonesia berada pada urutan pertama setelah Bahasa Inggris dan keberadaan kedua bahasa dalam permuseuman ini melibatkan dua fungsi utama bahasa, yakni fungsi komunikatif dan fungsi informatif.The existence and function of language  as a medium of communication covers all fields of human life including knowledge, one of them is the history of human civilization; how humans survived, how human utilized nature for their lives, and how nature provides all the necessities for humans. What humans have been doing now, what they have done in the past and far before that in the pre-history time, how the conditions of the nature at those times were and what changes as well as progresses occurred are documented using language, then re-visualized,  displayed as one of conservation and preservation acts in an institution called museum. This research discusess the existence and function of language in museums. How important the existence of a language in museums and what language functions used in museums both in informations given by the museum guides and on the displays accompanying objects and pictures are the aims of this research. The methods used are the combination between field research and library research. The results show that generally the existence of Indonesian language plays more important role than English and both languages have two main functions; communicative function and informative function.     

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

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