CpGPAP: CpG island predictor analysis platform.

BACKGROUND: Genomic islands play an important role in medical, methylation and biological studies. To explore the region, we propose a CpG islands prediction analysis platform for genome sequence exploration (CpGPAP). RESULTS: CpGPAP is a web-based application that provides a user-friendly interface for predicting CpG islands in genome sequences or in user input sequences. The prediction algorithms supported in CpGPAP include complementary particle swarm optimization (CPSO), a complementary genetic algorithm (CGA) and other methods (CpGPlot, CpGProD and CpGIS) found in the literature. The CpGPAP platform is easy to use and has three main features (1) selection of the prediction algorithm; (2) graphic visualization of results; and (3) application of related tools and dataset downloads. These features allow the user to easily view CpG island results and download the relevant island data. CpGPAP is freely available at http://bio.kuas.edu.tw/CpGPAP/. CONCLUSIONS: The platform's supported algorithms (CPSO and CGA) provide a higher sensitivity and a higher correlation coefficient when compared to CpGPlot, CpGProD, CpGIS, and CpGcluster over an entire chromosome.

Development of an enhanced leg muscle rehabilitation system.

This paper aims to develop an enhanced rehabilitation and assessment system for people with impaired leg muscles, and for people who need to improve their leg muscle function. Through interactive design and real time evaluation, medical staff can totally control the training situation for patients and therefore provide a better training program, so that overall a better treatment performance can be achieved. The system consists of four major parts. Sensory and signal conversion circuits convert the lever arm lengths and muscle strengths of the leg into a proper electronic signal and then deliver this to the computer. Then, the intelligent and interactive interface design lets a trainee complete the training process independently without the involvement of medical staff. In addition, the trainee can see the training results at the end of the training process on the computer screen. The training protection and evaluation mechanism effectively monitors the training situation, based on the individual status settings by the medical staff, and thus any further impairment can be avoided. The database management system is developed to store related personal data, system settings and training results, which can then be retrieved for control and assessment. In comparison to similar equipment the proposed system demonstrates a much better performance, particularly in system functions, accuracy, operation and costs.

Morse code application for wireless environmental control systems for severely disabled individuals.

Some physically-disabled people with neuromuscular diseases such as amyotrophic lateral sclerosis, multiple sclerosis, muscular dystrophy, or other conditions that hinder their ability to write, type, and speak, require an assistive tool for purposes of augmentative and alternative communication in their daily lives. In this paper, we designed and implemented a wireless environmental control system using Morse code as an adapted access communication tool. The proposed system includes four parts: input-control module; recognition module; wireless-control module; and electronic-equipment-control module. The signals are transmitted using adopted radio frequencies, which permits long distance transmission without space limitation. Experimental results revealed that three participants with physical handicaps were able to gain access to electronic facilities after two months' practice with the new system.

Counter-propagation network with variable degree variable step size LMS for single switch typing recognition.

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, including mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for disabled persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. This restriction is a major hindrance. Therefore, a switch adaptive automatic recognition method with a high recognition rate is needed. The proposed system combines counter-propagation networks with a variable degree variable step size LMS algorithm. It is divided into five stages: space recognition, tone recognition, learning process, adaptive processing, and character recognition. Statistical analyses demonstrated that the proposed method elicited a better recognition rate in comparison to alternative methods in the literature.

Operon prediction using chaos embedded particle swarm optimization.

Operons contain valuable information for drug design and determining protein functions. Genes within an operon are co-transcribed to a single-strand mRNA and must be coregulated. The identification of operons is, thus, critical for a detailed understanding of the gene regulations. However, currently used experimental methods for operon detection are generally difficult to implement and time consuming. In this paper, we propose a chaotic binary particle swarm optimization (CBPSO) to predict operons in bacterial genomes. The intergenic distance, participation in the same metabolic pathway and the cluster of orthologous groups (COG) properties of the Escherichia coli genome are used to design a fitness function. Furthermore, the Bacillus subtilis, Pseudomonas aeruginosa PA01, Staphylococcus aureus and Mycobacterium tuberculosis genomes are tested and evaluated for accuracy, sensitivity, and specificity. The computational results indicate that the proposed method works effectively in terms of enhancing the performance of the operon prediction. The proposed method also achieved a good balance between sensitivity and specificity when compared to methods from the literature.

Associate PCR-RFLP assay design with SNPs based on genetic algorithm in appropriate parameters estimation.

Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) is a commonly used laboratory technique and useful in small-scale basic research studies of complex genetic diseases that are associated with single nucleotide polymorphisms (SNPs). Before PCR-RFLP assay for SNP genotyping can be performed, a feasible primer pair observes numerous constraints and an available restriction enzyme for discriminating a target SNP, are required. The computation of feasible PCR-RFLP primers and find available restriction enzymes simultaneously aim at a target SNP is a challenging problem. Here, we propose an available method which combines the updated core of SNP-RFLPing with a genetic algorithm to reliably mine available restriction enzymes and search for feasible PCR-RFLP primers. We have in silico simulated the method in the SLC6A4 gene under different parameter settings and provided an appropriate parameter setting. The wet laboratory validation showed that it indeed usable in providing the available restriction enzymes and designing feasible primers that fit the common primer constraints. We have provided an easy and kindly interface to assist the researchers designing their PCR-RFLP assay for SNP genotyping. The program is implemented in JAVA and is freely available at http://bio.kuas.edu.tw/ganpd/.

Mutagenic primer design for mismatch PCR-RFLP SNP genotyping using a genetic algorithm.

Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) is useful in small-scale basic research studies of complex genetic diseases that are associated with single nucleotide polymorphism (SNP). Designing a feasible primer pair is an important work before performing PCR-RFLP for SNP genotyping. However, in many cases, restriction enzymes to discriminate the target SNP resulting in the primer design is not applicable. A mutagenic primer is introduced to solve this problem. GA-based Mismatch PCR-RFLP Primers Design (GAMPD) provides a method that uses a genetic algorithm to search for optimal mutagenic primers and available restriction enzymes from REBASE. In order to improve the efficiency of the proposed method, a mutagenic matrix is employed to judge whether a hypothetical mutagenic primer can discriminate the target SNP by digestion with available restriction enzymes. The available restriction enzymes for the target SNP are mined by the updated core of SNP-RFLPing. GAMPD has been used to simulate the SNPs in the human SLC6A4 gene under different parameter settings and compared with SNP Cutter for mismatch PCR-RFLP primer design. The in silico simulation of the proposed GAMPD program showed that it designs mismatch PCR-RFLP primers. The GAMPD program is implemented in JAVA and is freely available at http://bio.kuas.edu.tw/gampd/.

A hybrid BPSO-CGA approach for gene selection and classification of microarray data.

Microarray analysis promises to detect variations in gene expressions, and changes in the transcription rates of an entire genome in vivo. Microarray gene expression profiles indicate the relative abundance of mRNA corresponding to the genes. The selection of relevant genes from microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, multiclass categories being involved, and the usually small sample size. A classification process is often employed which decreases the dimensionality of the microarray data. In order to correctly analyze microarray data, the goal is to find an optimal subset of features (genes) which adequately represents the original set of features. A hybrid method of binary particle swarm optimization (BPSO) and a combat genetic algorithm (CGA) is to perform the microarray data selection. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) served as a classifier. The proposed BPSO-CGA approach is compared to ten microarray data sets from the literature. The experimental results indicate that the proposed method not only effectively reduce the number of genes expression level, but also achieves a low classification error rate.

A hybrid feature selection method for DNA microarray data.

Gene expression profiles, which represent the state of a cell at a molecular level, have great potential as a medical diagnosis tool. In cancer classification, available training data sets are generally of a fairly small sample size compared to the number of genes involved. Along with training data limitations, this constitutes a challenge to certain classification methods. Feature (gene) selection can be used to successfully extract those genes that directly influence classification accuracy and to eliminate genes which have no influence on it. This significantly improves calculation performance and classification accuracy. In this paper, correlation-based feature selection (CFS) and the Taguchi-genetic algorithm (TGA) method were combined into a hybrid method, and the K-nearest neighbor (KNN) with the leave-one-out cross-validation (LOOCV) method served as a classifier for eleven classification profiles to calculate the classification accuracy. Experimental results show that the proposed method reduced redundant features effectively and achieved superior classification accuracy. The classification accuracy obtained by the proposed method was higher in ten out of the eleven gene expression data set test problems when compared to other classification methods from the literature.

Tabu search and binary particle swarm optimization for feature selection using microarray data.

Gene expression profiles have great potential as a medical diagnosis tool because they represent the state of a cell at the molecular level. In the classification of cancer type research, available training datasets generally have a fairly small sample size compared to the number of genes involved. This fact poses an unprecedented challenge to some classification methodologies due to training data limitations. Therefore, a good selection method for genes relevant for sample classification is needed to improve the predictive accuracy, and to avoid incomprehensibility due to the large number of genes investigated. In this article, we propose to combine tabu search (TS) and binary particle swarm optimization (BPSO) for feature selection. BPSO acts as a local optimizer each time the TS has been run for a single generation. The K-nearest neighbor method with leave-one-out cross-validation and support vector machine with one-versus-rest serve as evaluators of the TS and BPSO. The proposed method is applied and compared to the 11 classification problems taken from the literature. Experimental results show that our method simplifies features effectively and either obtains higher classification accuracy or uses fewer features compared to other feature selection methods.