genetic algorithm

Microarray is a sophisticated tool that concurrently analyzes the expression levels of thousands of genes, giving scientists an overview of DNA and RNA study. This procedure is divided into three stages: contact with biological samples, data extraction, and data analysis. Because expression levels are disclosed by the interplay of light with fluorescent markers, the data extraction stage relies on image processing methods. To extract quantitative information from the microarray image (MAI), four steps of preprocessing, gridding, segmentation, and intensity quantification are required. During the generation of MAIs, a large number of error‑prone processes occur, leading to structural problems and reduced quality in the resulting data, affecting the identification of expressed genes.

In this article, the first stage has been examined. In the preprocessing stage, the contrast of the images is first enhanced using the genetic algorithm, then the source noises that appear as small artifacts are removed using morphology, and finally, to confirm the effect of the contrast enhancement (CE) on the main stages of microarray data processing, gridding is checked on complementary deoxyribonucleic acid MAIs.

The comparison of the obtained results with an adaptive histogram equalization (AHE) and multi‑decomposition histogram equalization (M‑DHE) methods shows the superiority and efficiency of the proposed method. For example, the image contrast of the Genomic Medicine Research Center Laboratory dataset is 3.24, which is 42.91 with the proposed method and 13.48 and 32.40 with the AHE and M‑DHE methods, respectively.

The performance of the proposed methods for CE is evaluated on 3 databases and a general conclusion is obtained as to which CE method is more suitable for each dataset.

Online determination of the elemental composition of tissues near the Bragg peak is a challenge in proton therapy related studies. In the present work, an analysis method based on the whole spectral information is presented for the quantitative determination of the elemental composition (weight %) of an irradiated target from its emitted Prompt Gamma (PG) spectrum.

To address this issue, four test phantoms with different weights (%) of 12C, 16O, 20Ca, and 14N elements were considered. The simulated PG spectra were recorded using 3 × 3 inch NaI detectors. A library consisting of the spectra of single-element phantoms as well as the spectra of test-irradiated phantoms was produced for 30, 70, and 150 MeV incident protons using the Geant4 Monte Carlo toolkit. The elemental analysis was performed using the information of the whole spectrum by applying two methods, including the well-known Genetic Algorithm (GA) and Multiple Linear Regression (MLR).

The results show that the proposed method estimates the oxygen concentration accurately. Furthermore, the estimated weights of other elements, with both methods, agree well with nominal values in each test phantom, for the considered energies.

The proposed quantitative elemental analysis of proton-bombarded phantoms using their induced PG spectrum is expected to be beneficial in treatment planning and treatment verification studies.

 A reliable model for wastewater treatment plants (WWTPs) is essential to provide a tool for predicting their performance and to form a basis for controlling the operation of the process also; this would minimize the operation costs. In recent years, computer-based methods have been applied to many areas of environmental issues. Artificial neural networks (ANNs) and genetic algorithm (GA) techniques could be applied for modeling WWTP processes, owing to their high accuracy, adequacy, and quite promising applications in engineering. 

 This study applied multilayer feed forward back propagation neural network and GA to predict and optimize the performance of the second phase of the Isfahan North WWTP. Experimental results, which demonstrated the performance of the plant over 6 years were applied for modeling. 

 A three-layer neural network was developed as a predictive model and the network was trained with Levenberg–Marquardt algorithm. The chemical oxygen demand (COD), biochemical oxygen demand (BOD), total suspended solids (TSS), total kjeldahl nitrogen (TKN), and total phosphorus (TP) were introduced as the model input and output. Neural network performance was evaluated with correlation coefficient ® and least mean square error. Proposed model demonstrated the high consistency of the results of modeling and experiments. GA achieved the best value of input parameters as 324.36, 457.37, 359.11, 60.09, and 14.15 mg/l for BOD, COD, TSS, TKN, TP, respectively. 

 ANN and GA combination provides powerful analysis tool for modeling and optimization of nonlinear relationships between the parameters in WWTPs and could be used for proper design and operation of the WWTPs.

Logistic regression is one of the most common models used to predict and classify binary and multiple state responses in medicine. Genetic algorithms search techniques inspired by biology have recently been used successfully as a predictive model.

The aim of this study was to use the genetic algorithm and logistic regression models in diagnosing and predicting factors affecting breast cancer mortality.

In this study, data of 2836 people with breast cancer during the years 2014-2018 was examined; their information was recorded in the cancer registration system of Kerman University of Medical Sciences. Death status was considered a dependent variable, while age, morphology, tumor differentiation (grad), residence status, and place of residence were considered independent variables. Sensitivity, specificity, accuracy, and area under the ROC curve were used to compare the models.

the logistic regression model determined factors affecting the breast cancer mortality rate, (with sensitivity (0.62), specificity (0.81), area under the ROC curve (0.74), and accuracy (0.84)), and genetic algorithm model (, with sensitivity (0.19), specificity (0.97), area under the ROC curve (0.58) and accuracy (0.87)).

The sensitivity and area under the ROC curve of the logistic regression model were higher than those of the genetic algorithm, but the specificity and accuracy of the genetic algorithm were higher than those of the logistic regression. According to the purpose of the study, two models can be used simultaneously.

Improvement of technology can increase the use of machine learning algorithms in predicting diseases. Early diagnosis of the disease can reduce mortality and morbidity at the community level.

In this paper, a clinical decision support system for the diagnosis of gestational diabetes is pres ented by combining artificial neural network and meta - heuristic algorithm. In this study, four meta - innovative algorithms of genetics, ant colony, particle Swarm optimization and cuckoo search were selected to be combined with artificial neural network. Th en these four algorithms were compared with each other. The data set contains 768 records and 8 dependent variables. This data set has 200 missing records, so the number of study records was reduced to 568 records.

The data were divided into two sets of training and testing by 10 - Fold method. Then, all four algorithms of neural - genetic network, ant - neural colony network, neural network - particle Swarm optimization and neural network - cuckoo search on the data The trainings were performed and then ev aluated by the test set. And the accuracy of 95.02 was obtained. Also, the final output of the algorithm was examined with two similar tasks and it was shown that the proposed model worked better.

In this study showed that the combination of t wo neural network and genetic algorithms can provide a suitable predictive model for disease diagnosis.

Temperament (Mizaj) determination is an important stage of diagnosis in Persian Medicine. This study aimed to evaluate thermal imaging as a reliable tool that can be used instead of subjective assessments.

The temperament of 34 participants was assessed by a PM specialist using standardized Mojahedi Mizaj Questionnaire (MMQ) and thermal images of the wrist in the supine position, the back of the hand, and their whole face under supervision of the physician were recorded. Thirteen thermal features were extracted and a classifying algorithm was designed based on the genetic algorithm and Adaboost classifier in reference to the temperament questionnaire.

The results showed that the mean temperature and temperature variations in the thermal images were relatively consistent with the results of MMQ. Among the three body regions, the results related to the image from Malmas were most consistent with MMQ. By selecting six of the 13 features that had the most impact on the classification, the accuracy of 94.7 ± 13.0, sensitivity of 95.7 ± 11.3, and specificity of 98.2 ± 4.2 were obtained.

The thermal imaging was relatively consistent with standardized MMQ and can be used as a reliable tool for evaluating warm/cold temperament. However, the results reveal that thermal imaging features may not be only main features for temperament classification and for more reliable classification, it needs to add some different features such as wrist pulse features and some subjective characteristics.

Similarity analysis of protein structure is considered as a fundamental step to give insight into the relationships between proteins. The primary step in structural alignment is looking for the optimal correspondence between residues of two structures to optimize the scoring function. An exhaustive search for finding such a correspondence between two structures is intractable.

In this paper, a hybrid method is proposed, namely GADP-align, for pairwise protein structure alignment. The proposed method looks for an optimal alignment using a hybrid method based on a genetic algorithm and an iterative dynamic programming technique. To this end, the method first creates an initial map of correspondence between secondary structure elements (SSEs) of two proteins. Then, a genetic algorithm combined with an iterative dynamic programming algorithm is employed to optimize the alignment.

The GADP-align algorithm was employed to align 10 ‘difficult to align’ protein pairs in order to evaluate its performance. The experimental study shows that the proposed hybrid method produces highly accurate alignments in comparison with the methods using exactly the dynamic programming technique. Furthermore, the proposed method prevents the local optimal traps caused by the unsuitable initial guess of the corresponding residues.

The findings of this paper demonstrate that employing the genetic algorithm along with the dynamic programming technique yields highly accurate alignments between a protein pair by exploring the global alignment and avoiding trapping in local alignments.

Due to the terrible effects of 2019 novel coronavirus (COVID-19) on health systems and the global economy, the necessity to study future trends of the virus outbreaks around the world is seriously felt. Since geographical mobility is a risk factor of the disease, it has spread to most of the countries recently. It, therefore, necessitates to design a decision support model to i) identify the spread pattern of coronavirus and, ii) provide reliable information for the detection of future trends of the virus outbreaks.

The present study adopts a computational intelligence approach to detect the possible trends in the spread of 2019-nCoV in China for a one-month period. Then, a validated model for detecting future trends in the spread of the virus in France is proposed. It uses the Artificial Neural Network (ANN) and a combination of ANN and Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) as predictive models.

The models work on the basis of data released from the past and the present days from World Health Organization (WHO). By comparing four proposed models, ANN and GA-ANN achieve a high degree of accuracy in terms of performance indicators.  

The models proposed in this study can be used as decision support tools for managing and controlling of 2019-nCoV outbreaks.

A reliable model for any wastewater treatment plant (WWTP) is essential to predict its performance and form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This study applied artificial neural network-genetic algorithm (ANN-GA) and co-active neuro-fuzzy logic inference system (CANFIS) in comparison with ANN for predicting the performance of WWTP. The result indicated that the GA produces more accurate results than fuzzy logic technique. It was found that GA components increased the ANN ability in predicting WWTP performance. The normalized root mean square error (NRMSE) for ANN-GA in predicting chemical oxygen demand (COD), total suspended solids (TSS) and biochemical oxygen demand (BOD) were 0.15, 0.19 and 0.15, respectively. The corresponding correlation coefficients were 0.891, 0.930 and 0.890, respectively. Comparing these results with other studies showed that despite the slightly lower performance of the current model, its requirement for a lower number of input parameters can save the extra cost of sampling.

Identifying the potential firing patterns following different brain regions under normal and abnormal conditions increases our understanding of events at the level of neural interactions in the brain. Furthermore, it is important to be capable of modeling the potential neural activities to build precise artificial neural networks. The Izhikevich model is one of the simplest biologically-plausible models, i.e. capable of capturing most recognized firing patterns of neurons. This property makes the model efficient in simulating the large-scale networks of neurons. Improving the Izhikevich model for adapting with the neuronal activity of rat brain with great accuracy would make the model effective for future neural network implementations.

Data sampling from two brain regions, the HIP and BLA, was performed by the extracellular recordings of male Wistar rats, and spike sorting was conducted by Plexon offline sorter. Further analyses were performed through NeuroExplorer and MATLAB. To optimize the Izhikevich model parameters, a genetic algorithm was used. In this algorithm, optimization tools, like crossover and mutation, provide the basis for generating model parameters populations. The process of comparison in each iteration leads to the survival of better populations until achieving the optimum solution.

In the present study, the possible firing patterns of the real single neurons of the HIP and BLA were identified. Additionally, an improved Izhikevich model was achieved. Accordingly, the real neuronal spiking pattern of these regions’ neurons and the corresponding cases of the Izhikevich neuron spiking pattern were adjusted with great accuracy. 

This study was conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large-scale neural networks simulations, as well as reducing the modeling complexity. This aim was achievable by performing the improved Izhikevich model, and inserting only the plausible firing patterns and eliminating unrealistic ones.

How to explore the dynamics of transition probabilities between phases of budding yeast cell cycle (BYCC) network based on the dynamics of protein activities that control this network? How to identify the robust structure of protein interactions of BYCC Boolean network (BN)? Budding yeast allows scientists to put experiments into effect in order to discover the intracellular cell cycle regulating structures which are well simulated by mathematical modeling.

We extended an available deterministic BN of proteins responsible for the cell cycle to a Markov chain model containing apoptosis besides G1, S, G2, M, and stationary G1. Using genetic algorithm (GA), we estimated the kinetic parameters of the extended BN model so that the subsequent transition probabilities derived using Markov chain model of cell states as normal cell cycle becomes the maximum while the structure of chemical interactions of extended BN of cell cycle becomes more stable.

Using kinetic parameters optimized by GA, the probability of the subsequent transitions between cell cycle phases is maximized. The relative basin size of stationary G1 increased from 86% to 96.48% while the number of attractors decreased from 7 in the original model to 5 in the extended one. Hence, an increase in the robustness of the system has been achieved.

The structure of interacting proteins in cell cycle network affects its robustness and probabilities of transitions between different cell cycle phases. Markov chain and BN are good approaches to study the stability and dynamics of the cell cycle network.