Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype SNP barcodes

Discovering SNP-disease relationships in genome-wide SNP data using an improved harmony search based on SNP locus and genetic inheritance patterns

Advances in high-throughput sequencing technologies have made it possible to access millions of measurements from thousands of people. Single nucleotide polymorphisms (SNPs), the most common type of mutation in the human genome, have been shown to play a significant role in the development of complex and multifactorial diseases. However, studying the synergistic interactions between different SNPs in explaining multifactorial diseases is challenging due to the high dimensionality of the data and methodological complexities. Existing solutions often use a multi-objective approach based on metaheuristic optimization algorithms such as harmony search. However, previous studies have shown that using a multi-objective approach is not sufficient to address complex disease models with no or low marginal effect. In this research, we introduce a locus-driven harmony search (LDHS), an improved harmony search algorithm that focuses on using SNP locus information and genetic inheritance patterns to initialize harmony memories. The proposed method integrates biological knowledge to improve harmony memory initialization by adding SNP combinations that are likely candidates for interaction and disease causation. Using a SNP grouping process, LDHS generates harmonies that include SNPs with a higher potential for interaction, resulting in greater power in detecting disease-causing SNP combinations. The performance of the proposed algorithm was evaluated on 200 synthesized datasets for disease models with and without marginal effect. The results show significant improvement in the power of the algorithm to find disease-related SNP sets while decreasing computational cost compared to state-of-the-art algorithms. The proposed algorithm also demonstrated notable performance on real breast cancer data, showing that integrating prior knowledge can significantly improve the process of detecting disease-related SNPs in both real and synthesized data.

Hybrid algorithm for predicting the temperature-variation-induced wavelength drift of DBR semiconductor lasers

In this paper, a hybrid algorithm to predict the wavelength drift induced by ambient temperature variation in distributed Bragg reflector semiconductor lasers is proposed. This algorithm combines the global search capability of a genetic algorithm (GA) and the supermapping ability of an extreme learning machine (ELM), which not only avoids the randomness of ELM but also improves its generalization performance. In addition, a tenfold cross-validation method is employed to determine the optimal activation function and the number of hidden layer nodes for ELM to construct the most suitable model. After applying multiple sets of test data, the results demonstrate that GA-ELM can quickly and accurately predict the wavelength drift, with an average rms error of 4.09×10^-4nm and average mean absolute percentage error of 0.21 %. This model is expected to combine the temperature and current tuning models for a wavelength in follow-up research to achieve rapid tuning and high stability of a wavelength without additional devices.

An Approach of Epistasis Detection Using Integer Linear Programming Optimizing Bayesian Network

Proposing a more effective and accurate epistatic loci detection method in large-scale genomic data has important research significance for improving crop quality, disease treatment, etc. Due to the characteristics of high accuracy and processing non-linear relationship, Bayesian network (BN) has been widely used in constructing the network of SNPs and phenotype traits and thus to mine epistatic loci. However, the shortcoming of BN is that it is easy to fall into local optimum and unable to process large-scale of SNPs. In this work, we transform the problem of learning Bayesian network into the optimization of integer linear programming (ILP). We use the algorithms of branch-and-bound and cutting planes to get the global optimal Bayesian network (ILPBN), and thus to get epistatic loci influencing specific phenotype traits. In order to handle large-scale of SNP loci and further to improve efficiency, we use the method of optimizing Markov blanket to reduce the number of candidate parent nodes for each node. In addition, we use α-BIC that is suitable for processing the epistatis mining to calculate the BN score. We use four properties of BN decomposable scoring functions to further reduce the number of candidate parent sets for each node. Experiment results show that ILPBN can not only process 2-locus and 3-locus epistasis mining, but also realize multi-locus epistasis detection. Finally, we compare ILPBN with several popular epistasis mining algorithms by using simulated and real Age-related macular disease (AMD) dataset. Experiment results show that ILPBN has better epistasis detection accuracy, F1-score and false positive rate in premise of ensuring the efficiency compared with other methods. Availability: Codes and dataset are available at: http://122.205.95.139/ILPBN/.

DeepBarcoding: Deep Learning for Species Classification Using DNA Barcoding

DNA barcodes with short sequence fragments are used for species identification. Because of advances in sequencing technologies, DNA barcodes have gradually been emphasized. DNA sequences from different organisms are easily and rapidly acquired. Therefore, DNA sequence analysis tools play an increasingly crucial role in species identification. This study proposed deep barcoding, a deep learning framework for species classification by using DNA barcodes. Deep barcoding uses raw sequence data as the input to represent one-hot encoding as a one-dimensional image and uses a deep convolutional neural network with a fully connected deep neural network for sequence analysis. It can achieve an average accuracy of >90 percent for both simulation and real datasets. Although deep learning yields outstanding performance for species classification with DNA sequences, its application remains a challenge. The deep barcoding model can be a potential tool for species classification and can elucidate DNA barcode-based species identification.

Optimized Radial Basis Neural Network for Classification of Breast Cancer Images

BACKGROUND

Breast cancer is a curable disease if diagnosed at an early stage. The chances of having breast cancer are the lowest in married women after the breast-feeding phase because the cancer is formed from the blocked milk ducts.

INTRODUCTION

Nowadays, cancer is considered the leading cause of death globally. Breast cancer is the most common cancer among females. It is possible to develop breast cancer while breast-feeding a baby, but it is rare. Mammography is one of the most effective methods used in hospitals and clinics for early detection of breast cancer. Various researchers are used in artificial intelligence- based mammogram techniques. This process of mammography will reduce the death rate of the patients affected by breast cancer. This process is improved by the image analysing, detection, screening, diagnosing, and other performance measures.

METHODS

The radial basis neural network will be used for classification purposes. The radial basis neural network is designed with the help of the optimization algorithm. The optimization is to tune the classifier to reduce the error rate with the minimum time for the training process. The cuckoo search algorithm will be used for this purpose.

RESULTS

Thus, the proposed optimum RBNN is determined to classify breast cancer images. In this, the three sets of properties were classified by performing the feature extraction and feature reduction. In this breast cancer MRI image, the normal, benign, and malignant is taken to perform the classification. The minimum fitness value is determined to evaluate the optimum value of possible locations. The radial basis function is evaluated with the cuckoo search algorithm to optimize the feature reduction process. The proposed methodology is compared with the traditional radial basis neural network using the evaluation parameter like accuracy, precision, recall and f1-score. The whole system model is done by using Matrix Laboratory (MATLAB) with the adaptation of 2018a since the proposed system is most efficient than most recent related literature.

CONCLUSION

Thus, it concluded with the efficient classification process of RBNN using a cuckoo search algorithm for breast cancer images. The mammogram images are taken into recent research because breast cancer is a major issue for women. This process is carried to classify the various features for three sets of properties. The optimized classifier improves performance and provides a better result. In this proposed research work, the input image is filtered using a wiener filter, and the classifier extracts the feature based on the breast image.

Multipopulation harmony search algorithm for the detection of high-order SNP interactions

MOTIVATION

Recently, multiobjective swarm intelligence optimization (SIO) algorithms have attracted considerable attention as disease model-free methods for detecting high-order single nucleotide polymorphism (SNP) interactions. However, a strict Pareto optimal set may filter out some of the SNP combinations associated with disease status. Furthermore, the lack of heuristic factors for finding SNP interactions and the preference for discrimination approaches to disease models are considerable challenges for SIO.

In this study, we propose a multipopulation harmony search (HS) algorithm dedicated to the detection of high-order SNP interactions (MP-HS-DHSI). This method consists of three stages. In the first stage, HS with multipopulation (multiharmony memories) is used to discover a set of candidate high-order SNP combinations having an association with disease status. In HS, multiple criteria [Bayesian network-based K2-score, Jensen-Shannon divergence, likelihood ratio and normalized distance with joint entropy (ND-JE)] are adopted by four harmony memories to improve the ability to discriminate diverse disease models. A novel evaluation criterion named ND-JE is proposed to guide HS to explore clues for high-order SNP interactions. In the second and third stages, the G-test statistical method and multifactor dimensionality reduction are employed to verify the authenticity of the candidate solutions, respectively.

RESULTS

We compared MP-HS-DHSI with four state-of-the-art SIO algorithms for detecting high-order SNP interactions for 20 simulation disease models and a real dataset of age-related macular degeneration. The experimental results revealed that our proposed method can accelerate the search speed efficiently and enhance the discrimination ability of diverse epistasis models.

AVAILABILITY AND IMPLEMENTATION

https://github.com/shouhengtuo/MP-HS-DHSI.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Class Balanced Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions

Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach for the multifactor dimensionality reduction (BMDR) method to increase the accuracy of estimates of the prediction error rate in small samples. BMDR explicitly selects the best model by evaluating the average of prediction error rates over k-fold cross-validation without cross-validation consistency selection. In this study, we used several epistatic models with and without marginal effects under different parameter settings (heritability and minor allele frequencies) to evaluate the performance of existing approaches. Using simulated data sets, BMDR successfully detected gene-gene interactions, particularly for data sets with small sample sizes. A large data set was obtained from the Wellcome Trust Case Control Consortium, and results indicated that BMDR could effectively detect significant gene-gene interactions.

Nature-Inspired Multiobjective Epistasis Elucidation from Genome-Wide Association Studies

In recent years, the detection of epistatic interactions of multiple genetic variants on the causes of complex diseases brings a significant challenge in genome-wide association studies (GWAS). However, most of the existing methods still suffer from algorithmic limitations such as single-objective optimization, intensive computational requirement, and premature convergence. In this paper, we propose and formulate an epistatic interaction multi-objective artificial bee colony algorithm based on decomposition (EIMOABC/D) to address those problems for genetic interaction detection in genome-wide association studies. First, to direct the genetic interaction detection, two objective functions are formulated to characterize various epistatic models; rank probability model is proposed to sort each population into different nondomination levels based on the fast nondominated sorting approach. After that, the mutual information based local search algorithm is proposed to guide the population search for disease model evaluations in an unbiased manner. To validate the effectiveness of EIMOABC/D, we compare EIMOABC/D against seven state-of-the-art methods on 77 epistatic models including eight small-scale epistatic models with marginal effects, eight large-scale epistatic models with marginal effects, 60 large-scale epistatic models without any marginal effect, and one case study. The experimental results indicate that our proposed algorithm EIMOABC/D outperforms seven state-of-the-art methods on those epistatic models. Furthermore, time complexity analysis and parameter analysis are conducted to demonstrate various properties of our proposed algorithm.

Application of simulation-based CYP26 SNP-environment barcodes for evaluating the occurrence of oral malignant disorders by odds ratio-based binary particle swarm optimization: A case-control study in the Taiwanese population

Introduction

Genetic polymorphisms and social factors (alcohol consumption, betel quid (BQ) usage, and cigarette consumption), both separately or jointly, play a crucial role in the occurrence of oral malignant disorders such as oral and pharyngeal cancers and oral potentially malignant disorders (OPMD).

Material and methods

Simultaneous analyses of multiple single nucleotide polymorphisms (SNPs) and environmental effects on oral malignant disorders are essential to examine, albeit challenging. Thus, we conducted a case-control study (N = 576) to analyze the risk of occurrence of oral malignant disorders by using binary particle swarm optimization (BPSO) with an odds ratio (OR)-based method.

Results

We demonstrated that a combination of SNPs (CYP26B1 rs887844 and CYP26C1 rs12256889) and socio-demographic factors (age, ethnicity, and BQ chewing), referred to as the combined effects of SNP-environment, correlated with maximal risk diversity of occurrence observed between the oral malignant disorder group and the control group. The risks were more prominent in the oral and pharyngeal cancers group (OR = 10.30; 95% confidence interval (CI) = 4.58–23.15) than in the OPMD group (OR = 5.42; 95% CI = 1.94–15.12).

Conclusions

Simulation-based “SNP-environment barcodes” may be used to predict the risk of occurrence of oral malignant disorders. Applying simulation-based “SNP-environment barcodes” may provide insight into the importance of screening tests in preventing oral and pharyngeal cancers and OPMD.

Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network

Background

Mining epistatic loci which affects specific phenotypic traits is an important research issue in the field of biology. Bayesian network (BN) is a graphical model which can express the relationship between genetic loci and phenotype. Until now, it has been widely used into epistasis mining in many research work. However, this method has two disadvantages: low learning efficiency and easy to fall into local optimum. Genetic algorithm has the excellence of rapid global search and avoiding falling into local optimum. It is scalable and easy to integrate with other algorithms. This work proposes an epistasis mining approach based on genetic tabu algorithm and Bayesian network (Epi-GTBN). It uses genetic algorithm into the heuristic search strategy of Bayesian network. The individual structure can be evolved through the genetic operations of selection, crossover and mutation. It can help to find the optimal network structure, and then further to mine the epistasis loci effectively. In order to enhance the diversity of the population and obtain a more effective global optimal solution, we use the tabu search strategy into the operations of crossover and mutation in genetic algorithm. It can help to accelerate the convergence of the algorithm.

Results

We compared Epi-GTBN with other recent algorithms using both simulated and real datasets. The experimental results demonstrate that our method has much better epistasis detection accuracy in the case of not affecting the efficiency for different datasets.

Conclusions

The presented methodology (Epi-GTBN) is an effective method for epistasis detection, and it can be seen as an interesting addition to the arsenal used in complex traits analyses.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3022-z) contains supplementary material, which is available to authorized users.

A Method for Analyzing Two-locus Epistasis of Complex Diseases based on Decision Tree and Mutual Entropy

Background:

Epistasis makes complex diseases difficult to understand, especially when heterogeneity also exists. Heterogeneity of complex diseases makes the distribution of case population more confused. However, the traditional methods proposed to detect epistasis often ignore heterogeneity, resulting in low power of association studies.

Methods:

In this study, we firstly use rank information in the Classification Decision Tree and Mutual Entropy (CTME) to construct two different evaluation scores, namely multiple objectives. In addition, we improve the calculation of joint entropy between SNPs and disease label, which elevates the efficiency of CTME. Then, the ant colony algorithm is applied to search two-locus epistatic combination space. To handle the potential heterogeneity, all candidate two-locus SNPs are merged to recognize multiple different epistatic combinations. Finally, all these solutions are tested by χ2 test.

Results and Conclusion:

Experiments show that our method CTME improves the power of association study. More importantly, CTME also detects multiple epistatic SNPs contributing to heterogeneity. The experimental results show that CTME has advantages on power and efficiency.

Relationship between Clinicopathologic Variables in Breast Cancer Overall Survival Using Biogeography-Based Optimization Algorithm

Breast cancer is the most common cancer among women and is considered a major public health concern worldwide. Biogeography-based optimization (BBO) is a novel metaheuristic algorithm. This study analyzed the relationship between the clinicopathologic variables of breast cancer using Cox proportional hazard (PH) regression on the basis of the BBO algorithm. The dataset is prospectively maintained by the Division of Breast Surgery at Kaohsiung Medical University Hospital. A total of 1896 patients with breast cancer were included and tracked from 2005 to 2017. Fifteen general breast cancer clinicopathologic variables were collected. We used the BBO algorithm to select the clinicopathologic variables that could potentially contribute to predicting breast cancer prognosis. Subsequently, Cox PH regression analysis was used to demonstrate the association between overall survival and the selected clinicopathologic variables. C-statistics were used to test predictive accuracy and the concordance of various survival models. The BBO-selected clinicopathologic variables model obtained the highest C-statistic value (80%) for predicting the overall survival of patients with breast cancer. The selected clinicopathologic variables included tumor size (hazard ratio [HR] 2.372, p = 0.006), lymph node metastasis (HR 1.301, p = 0.038), lymphovascular invasion (HR 1.606, p = 0.096), perineural invasion (HR 1.546, p = 0.168), dermal invasion (HR 1.548, p = 0.028), total mastectomy (HR 1.633, p = 0.092), without hormone therapy (HR 2.178, p = 0.003), and without chemotherapy (HR 1.234, p = 0.491). This number was the minimum number of discriminators required for optimal discrimination in the breast cancer overall survival model with acceptable prediction ability. Therefore, on the basis of the clinicopathologic variables, the survival prediction model in this study could contribute to breast cancer follow-up and management.

Modeling and Optimization of Dual Active Bridge DC-DC Converter with Dead-Time Effect under Triple-Phase-Shift Control

Dead-time effect has become an apparent issue in high-switching-frequency high-power dual active bridge (DAB) DC-DC converter. This paper gives a detailed analysis of phase-shift errors effect caused by dead time, including output voltage offset, soft-switching failure, optimal scheme failure, etc. Phase-shift errors effect will invalidate traditional analyses of optimal control and mislead the design of DAB converter. To overcome these drawbacks, various operating modes and an accurate transmission power model incorporating dead time under triple-phase-shift (TPS) control are developed. On this basis, an optimal TPS incorporating dead time (TPSiDT) scheme is further proposed to minimize the current stress, while guaranteeing soft-switching operation by using Lagrange multiplier method (LMM) and Genetic Algorithm (GA). The novel transmission power model can provide accurate power flow computation to avoid phase-shift errors. Therefore, in practical applications, the minimum current stress and soft-switching operation can be guaranteed, and the efficiency of DAB converter can be improved. Finally, the experimental results verify the feasibility of the proposed TPSiDT scheme.

Statistical Association Mapping of Population-Structured Genetic Data

Association mapping of genetic diseases has attracted extensive research interest during the recent years. However, most of the methodologies introduced so far suffer from spurious inference of the associated sites due to population inhomogeneities. In this paper, we introduce a statistical framework to compensate for this shortcoming by equipping the current methodologies with a state-of-the-art clustering algorithm being widely used in population genetics applications. The proposed framework jointly infers the disease-associated factors and the hidden population structures. In this regard, a Markov Chain-Monte Carlo (MCMC) procedure has been employed to assess the posterior probability distribution of the model parameters. We have implemented our proposed framework on a software package whose performance is extensively evaluated on a number of synthetic datasets, and compared to some of the well-known existing methods such as STRUCTURE. It has been shown that in extreme scenarios, up to $10-15$10-15 percent of improvement in the inference accuracy is achieved with a moderate increase in computational complexity.

Catfish Taguchi-Based Binary Differential Evolution Algorithm for Analyzing Single Nucleotide Polymorphism Interactions in Chronic Dialysis

Single-nucleotide polymorphism (SNP)-SNP interactions are crucial for understanding the association between disease-related multifactorials for disease analysis. Existing statistical methods for determining such interactions are limited by the considerable computation required for evaluating all potential associations between disease-related multifactorials. Identifying SNP-SNP interactions is thus a major challenge in genetic association studies. This paper proposes a catfish Taguchi-based binary differential evolution (CT-BDE) algorithm for identifying SNP-SNP interactions. In the search space, the catfish effect prevents the premature convergence of the population, and the Taguchi method improves the search ability of the BDE algorithm. Hence, the proposed algorithm enables obtaining a favorable solution regarding the identification of high-order SNP-SNP interactions. Additionally, the proposed algorithm applies an effective fitness function derived from a multifactor dimensionality reduction (MDR) operation to evaluate the solutions from BDE-based algorithms. Simulated and real data sets were used to evaluate the ability of several BDE-based algorithms in identifying specific SNP-SNP interactions. We compared the fitness function derived from the MDR operation with that derived according to the difference between cases and controls, by using the different BDE-based algorithms. The results showed that the proposed CT-BDE algorithm applying the fitness function derived from the MDR operation exhibited a superior ability in identifying SNP-SNP interactions compared with the other BDE-based algorithms.

Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene-gene interactions

Epistasis within disease-related genes (gene–gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene–gene interactions; however, some gene–gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene–gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene–gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene–gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene–gene interactions in genome-wide association studies.

Joint Analysis of SNP-SNP-Environment Interactions for Chronic Dialysis by an Improved Branch and Bound Algorithm

In previous studies, both single-nucleotide polymorphism (SNP)-SNP or gene-gene (G × G) interactions and SNP-environmental factor (G × E) interactions were reported to partially account for "missing" heritability. However, (G × G) × E interactions were less commonly addressed. The purpose of this study was to develop a novel strategy to evaluate possible (G × G) × E interactions in D-loop-based chronic dialysis association. Using values from our previously published data set (704 controls and 193 cases) of 77 D-loop SNPs and 7 environmental factors (coronary heart disease, hypertension, diabetes mellitus, triglyceride, cholesterol, blood thiol, and TBARS levels), we compared the performances of G, G × G, G × E, and (G × G) × E. We found that the interactions of four individual SNPs previously associated with a significantly high risk of chronic dialysis [odds ratio (OR) = 1.56-4.93] with environmental factors (G × E) increased the risk of chronic dialysis (maximum OR = 35.43). We then used an improved branch and bound algorithm to identify combinations of two to four SNPs that were most highly associated with chronic dialysis (OR = 9.27-34.39). When the interactions of the two- and three-SNP combinations with environmental factors were evaluated, we found that the (G × G) × E effects increased the risk of chronic dialysis (maximum OR = 8.32-57.54 and OR = 12.52-57.81, respectively; adjusted OR = 8.67-81.81 and OR = 12.29-81.95, respectively). Taken together, the (G × G) × E interactions identified chronic dialysis-associated SNPs that would not have been found using G × G or G × E interactions, suggesting that (G × G) × E interactions may be helpful to solve the problems of missing heritability in association studies.

Detecting Susceptibility to Breast Cancer with SNP-SNP Interaction Using BPSOHS and Emotional Neural Networks

Studies for the association between diseases and informative single nucleotide polymorphisms (SNPs) have received great attention. However, most of them just use the whole set of useful SNPs and fail to consider the SNP-SNP interactions, while these interactions have already been proven in biology experiments. In this paper, we use a binary particle swarm optimization with hierarchical structure (BPSOHS) algorithm to improve the effective of PSO for the identification of the SNP-SNP interactions. Furthermore, in order to use these SNP interactions in the susceptibility analysis, we propose an emotional neural network (ENN) to treat SNP interactions as emotional tendency. Different from the normal architecture, just as the emotional brain, this architecture provides a specific path to treat the emotional value, by which the SNP interactions can be considered more quickly and directly. The ENN helps us use the prior knowledge about the SNP interactions and other influence factors together. Finally, the experimental results prove that the proposed BPSOHS_ENN algorithm can detect the informative SNP-SNP interaction and predict the breast cancer risk with a much higher accuracy than existing methods.

Method for generating multiple risky barcodes of complex diseases using ant colony algorithm

Background

Susceptible barcode recognition plays an important role in the diagnosis and treatment of complex diseases. Numerous approaches have been proposed to identify risky barcodes involved in the progress of complex diseases. However, some methods only consider differences in barcode frequencies between the control and disease groups; as such, these methods may be partial or even wrong. For example, some barcodes with a high risk ratio yield a low frequency on cases or exhibit a high frequency on controls, which may unreasonable from a statistical point.

Results

In our study, a stricter criteria, maximum discrepancy and maximum constituency, is designed to evaluate each barcode and ant colony algorithm is used to search combination space of epistasis. For complex diseases with multi-subtypes, our method can list several potential barcodes contributing to different subtypes of complex diseases. Another contribution of this work is to introduce a method for determining the length of barcodes and excluding noisy barcodes whose frequencies are abnormal. In addition, common pathogenic genes shared by different risky barcodes are also recognized, which may provide key clue for further study, such as gene function analysis.

Conclusions

Experimental results reveal that our method can find multiple risky barcodes whose risk ratio and odds ratio are >1. These barcodes could be related to different subtypes of complex diseases.

The Combinational Polymorphisms of ORAI1 Gene Are Associated with Preventive Models of Breast Cancer in the Taiwanese

The ORAI calcium release-activated calcium modulator 1 (ORAI1) has been proven to be an important gene for breast cancer progression and metastasis. However, the protective association model between the single nucleotide polymorphisms (SNPs) of ORAI1 gene was not investigated. Based on a published data set of 345 female breast cancer patients and 290 female controls, we used a particle swarm optimization (PSO) algorithm to identify the possible protective models of breast cancer association in terms of the SNPs of ORAI1 gene. Results showed that the PSO-generated models of 2-SNP (rs12320939-TT/rs12313273-CC), 3-SNP (rs12320939-TT/rs12313273-CC/rs712853-(TT/TC)), 4-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs712853-(TT/TC)), and 5-SNP (rs12320939-TT/rs12313273-CC/rs7135617-(GG/GT)/rs6486795-CC/rs712853-(TT/TC)) displayed low values of odds ratios (0.409–0.425) for breast cancer association. Taken together, these results suggested that our proposed PSO strategy is powerful to identify the combinational SNPs of rs12320939, rs12313273, rs7135617, rs6486795, and rs712853 of ORAI1 gene with a strongly protective association in breast cancer.

An efficiency analysis of high-order combinations of gene-gene interactions using multifactor-dimensionality reduction

Background

Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property.

Results

Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene–gene interaction with less computational complexity than the MDR in high-order interaction analysis.

Conclusion

FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1717-8) contains supplementary material, which is available to authorized users.

Explanation-aware computing of the prognosis for breast cancer supported by IK-DCBRC: Technical innovation

Background:

Active research is being conducted to determine the prognosis for breast cancer. However, the uncertainty is a major obstacle in this domain of medical research. In that context, explanation-aware computing has the potential for providing meaningful interactions between complex medical applications and users, which would ensure a significant reduction of uncertainty and risks. This paper presents an explanation-aware agent, supported by Intensive Knowledge-Distributed Case-Based Reasoning Classifier (IK-DCBRC), to reduce the uncertainty and risks associated with the diagnosis of breast cancer.

Methods:

A meaningful explanation is generated by inferring from a rule-based system according to the level of abstraction and the reasoning traces. The computer-aided detection is conducted by IK-DCBRC, which is a multi-agent system that applies the case-based reasoning paradigm and a fuzzy similarity function for the automatic prognosis by the class of breast tumors, i.e. malignant or benign, from a pattern of cytological images.

Results:

A meaningful interaction between the physician and the computer-aided diagnosis system, IK-DCBRC, is achieved via an intelligent agent. The physician can observe the trace of reasoning, terms, justifications, and the strategy to be used to decrease the risks and doubts associated with the automatic diagnosis. The capability of the system we have developed was proven by an example in which conflicts were clarified and transparency was ensured.

Conclusion:

The explanation agent ensures the transparency of the automatic diagnosis of breast cancer supported by IK-DCBRC, which decreases uncertainty and risks and detects some conflicts.

Restoring TRAIL mediated signaling in ovarian cancer cells

Ovarian cancer has emerged as a multifaceted and genomically complex disease. Genetic/epigenetic mutations, suppression of tumor suppressors, overexpression of oncogenes, rewiring of intracellular signaling cascades and loss of apoptosis are some of the deeply studied mechanisms. In vitro and in vivo studies have highlighted different molecular mechanisms that regulate tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) mediated apoptosis in ovarian cancer. In this review, we bring to limelight, expansion in understanding systematical characterization of ovarian cancer cells has led to the rapid development of new drugs and treatments to target negative regulators of TRAIL mediated signaling pathway. Wide ranging synthetic and natural agents have been shown to stimulate mRNA and protein expression of death receptors. This review is compartmentalized into programmed cell death protein 4, platelet-derived growth factor signaling and miRNA control of TRAIL mediated signaling to ovarian cancer. Mapatumumab and PRO95780 have been tested for efficacy against ovarian cancer. Use of high-throughput screening assays will aid in dissecting the heterogeneity of this disease and increasing a long-term survival which might be achieved by translating rapidly accumulating information obtained from molecular and cellular studies to clinic researches.

Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interactions

Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two- to six-order models of gene-gene interaction for the risk association with breast cancer (odds ratio > 1.0; P value <0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer.

Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm

BACKGROUND

Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear.

METHODS

This study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups).

RESULTS

The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance.

CONCLUSIONS

This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion.

MDR-ER: balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction

Background

Determining the complex relationship between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has proven capable of effectively detecting statistical patterns of epistasis. However, MDR has its weakness in accurately assigning multi-locus genotypes to either high-risk and low-risk groups, and does generally not provide accurate error rates when the case and control data sets are imbalanced. Consequently, results for classification error rates and odds ratios (OR) may provide surprising values in that the true positive (TP) value is often small.

Methodology/Principal Findings

To address this problem, we introduce a classifier function based on the ratio between the percentage of cases in case data and the percentage of controls in control data to improve MDR (MDR-ER) for multi-locus genotypes to be classified correctly into high-risk and low-risk groups. In this study, a real data set with different ratios of cases to controls (1∶4) was obtained from the mitochondrial D-loop of chronic dialysis patients in order to test MDR-ER. The TP and TN values were collected from all tests to analyze to what degree MDR-ER performed better than MDR.

Conclusions/Significance

Results showed that MDR-ER can be successfully used to detect the complex associations in imbalanced data sets.