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Book ChapterDOI

Soft Computing in Bioinformatics

01 Jan 2021-pp 431-446
TL;DR: In this paper, the authors explored the soft computing based techniques for bioinformatics and discussed the necessity of soft computing techniques and their compatibility for solving wide spectrum of bio-informatic related problems.
Abstract: In this chapter, we explored the soft computing based techniques for bioinformatics. Necessity of soft computing techniques and their compatibility for solving wide spectrum of bioinformatics related problems is reviewed. Basics of soft computing techniques are discussed and their relevancy in solving many bioinformatics based problems is also elaborated. Actual experimental results on two real world bioinformatics data demonstrated the efficacy of soft computing techniques over conventional one for biological data problems.
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Journal ArticleDOI
TL;DR: Depression, physical health, cigarette usage, education level, and sleep time play an important role in cognitive decline, which is consistent with the previous discovery and it is determined that risk factors significantly correlated with diseases.
Abstract: Cognitive decline has emerged as a significant threat to both public health and personal welfare, and mild cognitive decline/impairment (MCI) can further develop into Dementia/Alzheimer’s disease. While treatment of Dementia/Alzheimer’s disease can be expensive and ineffective sometimes, the prevention of MCI by identifying modifiable risk factors is a complementary and effective strategy. In this study, based on the data collected by Centers for Disease Control and Prevention (CDC) through the nationwide telephone survey, we apply a data-driven approach to re-exam the previously founded risk factors and discover new risk factors. We found that depression, physical health, cigarette usage, education level, and sleep time play an important role in cognitive decline, which is consistent with the previous discovery. Besides that, the first time, we point out that other factors such as arthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is less exploited previously. We also incorporate some machine learning and deep learning algorithms to weigh the importance of various factors contributed to MCI and predicted cognitive declined. By incorporating the data-driven approach, we can determine that risk factors significantly correlated with diseases. These correlations could also be expanded to another medical diagnosis besides MCI.

11 citations

Proceedings ArticleDOI
18 Jul 2010
TL;DR: In this system, the human iris database is enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the irIS.
Abstract: This paper presents the modular neural network architecture as a system for recognizing persons based on the iris biometric measurement of humans. In this system, the human iris database is enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the iris. The inputs to the modular neural network are the processed iris images and the output is the number of the person identified. The integration of the modules was done with a gating network method.

11 citations

Journal ArticleDOI
TL;DR: A hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization is proposed, which could select functional gene subsets which are significantly sensitive to the samples’ classes.
Abstract: Gene selection is one of the critical steps in the course of the classification of microarray data. Since particle swarm optimization has no complicated evolutionary operators and fewer parameters need to be adjusted, it has been used increasingly as an effective technique for gene selection. Since particle swarm optimization is apt to converge to local minima which lead to premature convergence, some particle swarm optimization based gene selection methods may select non-optimal genes with high probability. To select predictive genes with low redundancy as well as not filtering out key genes is still a challenge. To obtain predictive genes with lower redundancy as well as overcome the deficiencies of traditional particle swarm optimization based gene selection methods, a hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization is proposed in this paper. To select the genes highly related to out samples’ classes, a gene scoring strategy based on randomization and extreme learning machine is proposed to filter much irrelevant genes. With the third-level gene pool established by multiple filter strategy, an improved particle swarm optimization is proposed to perform gene selection. In the improved particle swarm optimization, to decrease the likelihood of the premature of the swarm the Metropolis criterion of simulated annealing algorithm is introduced to update the particles, and the half of the swarm are reinitialized when the swarm is trapped into local minima. Combining the gene scoring strategy with the improved particle swarm optimization, the new method could select functional gene subsets which are significantly sensitive to the samples’ classes. With the few discriminative genes selected by the proposed method, extreme learning machine and support vector machine classifiers achieve much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

10 citations

Book ChapterDOI
01 Jan 2010
TL;DR: Several real-world applications are described, in which the use of high-dimensional neural networks really helps in achieving the goals of intelligent system.
Abstract: Intelligent systems are emerging computing systems developed based on intelligent techniques. These techniques take advantage of artificial neural networks to emulate intelligent behavior. Extensive studies carried out during the past several years have revealed that neural networks enjoy numerous practical advantages over conventional methods. They are more fault-tolerant, less sensitive to noise and mostly used for their human-like characteristics (learning and generalization). They have been accepted as powerful tools for correlating data without making strong assumptions about the problems. Traditional neural networks’s parameters are usually real numbers for dealing with real-valued data. However, high-dimensional data also appear in practical applications and consequently, high-dimensional neural networks have been proposed. They have also presented improved results even in case of real-valued problems. As a prelude, we provide a brief overview of the existing methodologies in high-dimensional neural computation. Our particular point of view is to describe several real-world applications, in which the use of these techniques really helps in achieving the goals of intelligent system.

9 citations

Journal ArticleDOI
TL;DR: Numerical results on BAliBASE benchmark have shown the effectiveness of the proposed PSOSA method and its ability to achieve good quality solutions when compared with those given by other existing methods.
Abstract: In this work, a novel hybrid model called PSOSA for solving multiple sequence alignment (MSA) problem is proposed. The developed approach is a combination between particle swarm optimization (PSO) ...

9 citations