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1.
Neural Netw ; 119: 261-272, 2019 Aug 17.
Artigo em Inglês | MEDLINE | ID: mdl-31473577

RESUMO

In this paper, we propose a distributed semi-supervised learning (DSSL) algorithm based on the extreme learning machine (ELM) algorithm over communication network using the event-triggered (ET) communication scheme. In DSSL problems, training data consisting of labeled and unlabeled samples are distributed over a communication network. Traditional semi-supervised learning (SSL) algorithms cannot be used to solve DSSL problems. The proposed algorithm, denoted as ET-DSS-ELM, is based on the semi-supervised ELM (SS-ELM) algorithm, the zero gradient sum (ZGS) distributed optimization strategy and the ET communication scheme. Correspondingly, the SS-ELM algorithm is used to calculate the local initial value, the ZGS strategy is used to calculate the globally optimal value and the ET scheme is used to reduce communication times during the learning process. According to the ET scheme, each node over the communication network broadcasts its updated information only when the event occurs. Therefore, the proposed ET-DSS-ELM algorithm not only takes the advantages of traditional DSSL algorithms, but also saves network resources by reducing communication times. The convergence of the proposed ET-DSS-ELM algorithm is guaranteed by using the Lyapunov method. Finally, some simulations are given to show the efficiency of the proposed algorithm.

2.
Neural Netw ; 118: 300-309, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31330270

RESUMO

This paper aims to propose a distributed semi-supervised learning (D-SSL) algorithm to solve D-SSL problems, where training samples are often extremely large-scale and located on distributed nodes over communication networks. Training data of each node consists of labeled and unlabeled samples whose output values or labels are unknown. These nodes communicate in a distributed way, where each node has only access to its own data and can only exchange local information with its neighboring nodes. In some scenarios, these distributed data cannot be processed centrally. As a result, D-SSL problems cannot be centrally solved by using traditional semi-supervised learning (SSL) algorithms. The state-of-the-art D-SSL algorithm, denoted as Distributed Laplacian Regularization Least Square (D-LapRLS), is a kernel based algorithm. It is essential for the D-LapRLS algorithm to estimate the global Euclidian Distance Matrix (EDM) with respect to total samples, which is time-consuming especially when the scale of training data is large. In order to solve D-SSL problems and overcome the common drawback of kernel based D-SSL algorithms, we propose a novel Manifold Regularization (MR) based D-SSL algorithm using Wavelet Neural Network (WNN) and Zero-Gradient-Sum (ZGS) distributed optimization strategy. Accordingly, each node is assigned an individual WNN with the same basis functions. In order to initialize the proposed D-SSL algorithm, we propose a centralized MR based SSL algorithm using WNN. We denote the proposed SSL and D-SSL algorithms as Laplacian WNN (LapWNN) and distributed LapWNN (D-LapWNN), respectively. The D-LapWNN algorithm works in a fully distributed fashion by using ZGS strategy, whose convergence is guaranteed by the Lyapunov method. During the learning process, each node only exchanges local coefficients with its neighbors rather than raw data. It means that the D-LapWNN algorithm is a privacy preserving method. At last, several illustrative simulations are presented to show the efficiency and advantage of the proposed algorithm.

3.
Anal Biochem ; 583: 113362, 2019 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-31310738

RESUMO

At present, the identification of amyloid becomes more and more essential and meaningful. Because its mis-aggregation may cause some diseases such as Alzheimer's and Parkinson's diseases. This paper focus on the classification of amyloidogenic peptides and a novel feature representation called PhyAve_PSSMDwt is proposed. It includes two parts. One is based on physicochemical properties involving hydrophilicity, hydrophobicity, aggregation tendency, packing density and H-bonding which extracts 15-dimensional features in total. And the other is 60-dimensional features through recursive feature elimination from PSSM by discrete wavelet transform. In this period, sliding window is introduced to reconstruct PSSM so that the evolutionary information of short sequences can still be extracted. At last, the support vector machine is adopted as a classifier. The experimental result on Pep424 dataset shows that PSSM's information makes a great contribution on performance. And compared with other existing methods, our results after cross-validation increase by 3.1%, 3.3%, 0.136 and 0.007 in accuracy, specificity, Matthew's correlation coefficient and AUC value, respectively. It indicates that our method is effective and competitive.

4.
J Inequal Appl ; 2018(1): 178, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30137906

RESUMO

To globally solve a nonconvex quadratic programming problem, this paper presents an accelerating linearizing algorithm based on the framework of the branch-and-bound method. By utilizing a new linear relaxation approach, the initial quadratic programming problem is reduced to a sequence of linear relaxation programming problems, which is used to obtain a lower bound of the optimal value of this problem. Then, by using the deleting operation of the investigated regions, we can improve the convergent speed of the proposed algorithm. The proposed algorithm is proved to be convergent, and some experiments are reported to show higher feasibility and efficiency of the proposed algorithm.

5.
Math Biosci ; 282: 61-67, 2016 12.
Artigo em Inglês | MEDLINE | ID: mdl-27720879

RESUMO

Apoptosis, or programed cell death, plays a central role in the development and homeostasis of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful for understanding the apoptosis mechanism. The prediction of subcellular localization of an apoptosis protein is still a challenging task, and existing methods mainly based on protein primary sequences. In this paper, we introduce a new position-specific scoring matrix (PSSM)-based method by using detrended cross-correlation (DCCA) coefficient of non-overlapping windows. Then a 190-dimensional (190D) feature vector is constructed on two widely used datasets: CL317 and ZD98, and support vector machine is adopted as classifier. To evaluate the proposed method, objective and rigorous jackknife cross-validation tests are performed on the two datasets. The results show that our approach offers a novel and reliable PSSM-based tool for prediction of apoptosis protein subcellular localization.


Assuntos
Proteínas Reguladoras de Apoptose , Apoptose , Modelos Teóricos , Matrizes de Pontuação de Posição Específica , Máquina de Vetores de Suporte
6.
Springerplus ; 5(1): 1447, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27652023

RESUMO

A multiple dependent state (MDS) sampling plan is developed based on the coefficient of variation of the quality characteristic which follows a normal distribution with unknown mean and variance. The optimal plan parameters of the proposed plan are solved by a nonlinear optimization model, which satisfies the given producer's risk and consumer's risk at the same time and minimizes the sample size required for inspection. The advantages of the proposed MDS sampling plan over the existing single sampling plan are discussed. Finally an example is given to illustrate the proposed plan.

7.
Springerplus ; 5(1): 1302, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27547676

RESUMO

We present a practical branch and bound algorithm for globally solving generalized linear multiplicative programming problem with multiplicative constraints. To solve the problem, a relaxation programming problem which is equivalent to a linear programming is proposed by utilizing a new two-phase relaxation technique. In the algorithm, lower and upper bounds are simultaneously obtained by solving some linear relaxation programming problems. Global convergence has been proved and results of some sample examples and a small random experiment show that the proposed algorithm is feasible and efficient.

8.
Springerplus ; 5(1): 865, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27386314

RESUMO

In this paper, a new Sumudu transform iterative method is established and successfully applied to find the approximate analytical solutions for time-fractional Cauchy reaction-diffusion equations. The approach is easy to implement and understand. The numerical results show that the proposed method is very simple and efficient.

9.
Springerplus ; 5(1): 941, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27386385

RESUMO

The aim of this paper is to study a finite family of H-accretive operators and prove common zero point theorems of them in Banach space. The results presented in this paper extend and improve the corresponding results of Zegeye and Shahzad (Nonlinear Anal 66:1161-1169, 2007), Liu and He (J Math Anal Appl 385:466-476, 2012) and the related results.

10.
Springerplus ; 5(1): 1042, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27462490

RESUMO

In this paper, we suggest and analyze an improved generalized Newton method for solving the NP-hard absolute value equations [Formula: see text] when the singular values of A exceed 1. We show that the global and local quadratic convergence of the proposed method. Numerical experiments show the efficiency of the method and the high accuracy of calculation.

12.
IEEE Trans Cybern ; 45(12): 2827-39, 2015 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-25594992

RESUMO

Inspired by the fact that the division of labor and cooperation play extremely important roles in the human history development, this paper develops a novel artificial bee colony algorithm based on information learning (ILABC, for short). In ILABC, at each generation, the whole population is divided into several subpopulations by the clustering partition and the size of subpopulation is dynamically adjusted based on the last search experience, which results in a clear division of labor. Furthermore, the two search mechanisms are designed to facilitate the exchange of information in each subpopulation and between different subpopulations, respectively, which acts as the cooperation. Finally, the comparison results on a number of benchmark functions demonstrate that the proposed method performs competitively and effectively when compared to the selected state-of-the-art algorithms.


Assuntos
Algoritmos , Aprendizado de Máquina , Modelos Biológicos , Animais , Comportamento Apetitivo , Abelhas , Análise por Conglomerados
14.
IEEE Trans Cybern ; 45(5): 1094-107, 2015 May.
Artigo em Inglês | MEDLINE | ID: mdl-25137739

RESUMO

Inspired by the fact that in modern society, team cooperation and the division of labor play important roles in accomplishing a task, this paper proposes a dual-population differential evolution (DPDE) with coevolution for constrained optimization problems (COPs). The COP is treated as a bi-objective optimization problem where the first objective is the actual cost or reward function to be optimized, while the second objective accounts for the degree of constraint violations. At each generation during the evolution process, the whole population is divided into two based on the solution's feasibility to treat the both objectives separately. Each subpopulation focuses on only optimizing the corresponding objective which leads to a clear division of work. Furthermore, DPDE makes use of an information-sharing strategy to exchange search information between the different subpopulations similar to the team cooperation. The comparison of the proposed method on a number of benchmark functions with selected state-of-the-art constraint-handling algorithms indicates that the proposed technique performs competitively and effectively.

15.
Comput Math Methods Med ; 2015: 370756, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-26788119

RESUMO

Prediction of protein structural classes for low-similarity sequences is useful for understanding fold patterns, regulation, functions, and interactions of proteins. It is well known that feature extraction is significant to prediction of protein structural class and it mainly uses protein primary sequence, predicted secondary structure sequence, and position-specific scoring matrix (PSSM). Currently, prediction solely based on the PSSM has played a key role in improving the prediction accuracy. In this paper, we propose a novel method called CSP-SegPseP-SegACP by fusing consensus sequence (CS), segmented PsePSSM, and segmented autocovariance transformation (ACT) based on PSSM. Three widely used low-similarity datasets (1189, 25PDB, and 640) are adopted in this paper. Then a 700-dimensional (700D) feature vector is constructed and the dimension is decreased to 224D by using principal component analysis (PCA). To verify the performance of our method, rigorous jackknife cross-validation tests are performed on 1189, 25PDB, and 640 datasets. Comparison of our results with the existing PSSM-based methods demonstrates that our method achieves the favorable and competitive performance. This will offer an important complementary to other PSSM-based methods for prediction of protein structural classes for low-similarity sequences.


Assuntos
Sequência Consenso , Matrizes de Pontuação de Posição Específica , Proteínas/química , Proteínas/genética , Biologia Computacional , Bases de Dados de Proteínas/estatística & dados numéricos , Análise de Componente Principal , Estrutura Secundária de Proteína , Máquina de Vetores de Suporte
16.
Nat Commun ; 5: 5188, 2014 Oct 21.
Artigo em Inglês | MEDLINE | ID: mdl-25333821

RESUMO

Bactrian camel (Camelus bactrianus), dromedary (Camelus dromedarius) and alpaca (Vicugna pacos) are economically important livestock. Although the Bactrian camel and dromedary are large, typically arid-desert-adapted mammals, alpacas are adapted to plateaus. Here we present high-quality genome sequences of these three species. Our analysis reveals the demographic history of these species since the Tortonian Stage of the Miocene and uncovers a striking correlation between large fluctuations in population size and geological time boundaries. Comparative genomic analysis reveals complex features related to desert adaptations, including fat and water metabolism, stress responses to heat, aridity, intense ultraviolet radiation and choking dust. Transcriptomic analysis of Bactrian camels further reveals unique osmoregulation, osmoprotection and compensatory mechanisms for water reservation underpinned by high blood glucose levels. We hypothesize that these physiological mechanisms represent kidney evolutionary adaptations to the desert environment. This study advances our understanding of camelid evolution and the adaptation of camels to arid-desert environments.


Assuntos
Adaptação Fisiológica/genética , Evolução Biológica , Camelus/genética , Genoma , Transcriptoma , Tecido Adiposo/metabolismo , Animais , Glicemia/química , Clima Desértico , Meio Ambiente , Feminino , Perfilação da Expressão Gênica , Humanos , Masculino , Dados de Sequência Molecular , Osmorregulação , Filogenia , Sódio/metabolismo , Especificidade da Espécie , Transcrição Genética , Raios Ultravioleta , Água/química
17.
Nat Commun ; 5: 3966, 2014 Jun 03.
Artigo em Inglês | MEDLINE | ID: mdl-24892994

RESUMO

The blind mole rat (BMR), Spalax galili, is an excellent model for studying mammalian adaptation to life underground and medical applications. The BMR spends its entire life underground, protecting itself from predators and climatic fluctuations while challenging it with multiple stressors such as darkness, hypoxia, hypercapnia, energetics and high pathonecity. Here we sequence and analyse the BMR genome and transcriptome, highlighting the possible genomic adaptive responses to the underground stressors. Our results show high rates of RNA/DNA editing, reduced chromosome rearrangements, an over-representation of short interspersed elements (SINEs) probably linked to hypoxia tolerance, degeneration of vision and progression of photoperiodic perception, tolerance to hypercapnia and hypoxia and resistance to cancer. The remarkable traits of the BMR, together with its genomic and transcriptomic information, enhance our understanding of adaptation to extreme environments and will enable the utilization of BMR models for biomedical research in the fight against cancer, stroke and cardiovascular diseases.


Assuntos
Adaptação Fisiológica/genética , Evolução Molecular , Genoma , Hipercapnia , Hipóxia , Spalax/genética , Estresse Fisiológico , Transcriptoma/genética , Animais , Escuridão , Perfilação da Expressão Gênica , Edição de RNA/genética , Elementos Nucleotídeos Curtos e Dispersos
18.
IEEE Trans Cybern ; 44(8): 1314-27, 2014 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-24108755

RESUMO

Multimodal optimization is one of the most challenging tasks for optimization. It requires an algorithm to effectively locate multiple global and local optima, not just single optimum as in a single objective global optimization problem. To address this objective, this paper first investigates a cluster-based differential evolution (DE) for multimodal optimization problems. The clustering partition is used to divide the whole population into subpopulations so that different subpopulations can locate different optima. Furthermore, the self-adaptive parameter control is employed to enhance the search ability of DE. In this paper, the proposed multipopulation strategy and the self-adaptive parameter control technique are applied to two versions of DE, crowding DE (CDE) and species-based DE (SDE), which yield self-CCDE and self-CSDE, respectively. The new algorithms are tested on two different sets of benchmark functions and are compared with several state-of-the-art designs. The experiment results demonstrate the effectiveness and efficiency of the proposed multipopulation strategy and the self-adaptive parameter control technique. The proposed algorithms consistently rank top among all the competing state-of-the-art algorithms.

19.
Nat Commun ; 4: 2433, 2013.
Artigo em Inglês | MEDLINE | ID: mdl-24045858

RESUMO

Tigers and their close relatives (Panthera) are some of the world's most endangered species. Here we report the de novo assembly of an Amur tiger whole-genome sequence as well as the genomic sequences of a white Bengal tiger, African lion, white African lion and snow leopard. Through comparative genetic analyses of these genomes, we find genetic signatures that may reflect molecular adaptations consistent with the big cats' hypercarnivorous diet and muscle strength. We report a snow leopard-specific genetic determinant in EGLN1 (Met39>Lys39), which is likely to be associated with adaptation to high altitude. We also detect a TYR260G>A mutation likely responsible for the white lion coat colour. Tiger and cat genomes show similar repeat composition and an appreciably conserved synteny. Genomic data from the five big cats provide an invaluable resource for resolving easily identifiable phenotypes evident in very close, but distinct, species.


Assuntos
Genoma/genética , Leões/genética , Panthera/genética , Tigres/genética , Adaptação Fisiológica/genética , Sequência de Aminoácidos , Animais , Variação Genética , Dados de Sequência Molecular , Mutação/genética , Densidade Demográfica , Sintenia/genética
20.
IEEE Trans Cybern ; 43(3): 1011-24, 2013 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-23086528

RESUMO

The artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, ABC has an insufficiency regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose an improved ABC method called as CABC where a modified search equation is applied to generate a candidate solution to improve the search ability of ABC. Furthermore, we use the orthogonal experimental design (OED) to form an orthogonal learning (OL) strategy for variant ABCs to discover more useful information from the search experiences. Owing to OED's good character of sampling a small number of well representative combinations for testing, the OL strategy can construct a more promising and efficient candidate solution. In this paper, the OL strategy is applied to three versions of ABC, i.e., the standard ABC, global-best-guided ABC (GABC), and CABC, which yields OABC, OGABC, and OCABC, respectively. The experimental results on a set of 22 benchmark functions demonstrate the effectiveness and efficiency of the modified search equation and the OL strategy. The comparisons with some other ABCs and several state-of-the-art algorithms show that the proposed algorithms significantly improve the performance of ABC. Moreover, OCABC offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all the test functions.


Assuntos
Algoritmos , Inteligência Artificial , Abelhas/fisiologia , Comportamento Animal/fisiologia , Biomimética/métodos , Reconhecimento Automatizado de Padrão/métodos , Animais , Humanos
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