scholarly journals MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction

Genes ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 969
Author(s):  
Zahra Momeni ◽  
Mohammad Saniee Abadeh
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Gradient Boosting ◽  
Range Data ◽  
Parallel Genetic Algorithm ◽  
Data Parallel ◽  
Cpg Sites ◽  
Age Related ◽  
Age Range ◽  
Age Prediction

Genomic biomarkers such as DNA methylation (DNAm) are employed for age prediction. In recent years, several studies have suggested the association between changes in DNAm and its effect on human age. The high dimensional nature of this type of data significantly increases the execution time of modeling algorithms. To mitigate this problem, we propose a two-stage parallel algorithm for selection of age related CpG-sites. The algorithm first attempts to cluster the data into similar age ranges. In the next stage, a parallel genetic algorithm (PGA), based on the MapReduce paradigm (MR-based PGA), is used for selecting age-related features of each individual age range. In the proposed method, the execution of the algorithm for each age range (data parallel), the evaluation of chromosomes (task parallel) and the calculation of the fitness function (data parallel) are performed using a novel parallel framework. In this paper, we consider 16 different healthy DNAm datasets that are related to the human blood tissue and that contain the relevant age information. These datasets are combined into a single unioned set, which is in turn randomly divided into two sets of train and test data with a ratio of 7:3, respectively. We build a Gradient Boosting Regressor (GBR) model on the selected CpG-sites from the train set. To evaluate the model accuracy, we compared our results with state-of-the-art approaches that used these datasets, and observed that our method performs better on the unseen test dataset with a Mean Absolute Deviation (MAD) of 3.62 years, and a correlation (R2) of 95.96% between age and DNAm. In the train data, the MAD and R2 are 1.27 years and 99.27%, respectively. Finally, we evaluate our method in terms of the effect of parallelization in computation time. The algorithm without parallelization requires 4123 min to complete, whereas the parallelized execution on 3 computing machines having 32 processing cores each, only takes a total of 58 min. This shows that our proposed algorithm is both efficient and scalable.

scholarly journals Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor

Genes ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 424 ◽  
Author(s):  
Xingyan Li ◽  
Weidong Li ◽  
Yan Xu
Keyword(s):  
Dna Methylation ◽  
Pearson Correlation ◽  
Training Data ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Independent Dataset ◽  
Blood Samples ◽  
Cpg Sites ◽  
Age Related ◽  
Age Prediction

All tissues of organisms will become old as time goes on. In recent years, epigenetic investigations have found that there is a close correlation between DNA methylation and aging. With the development of DNA methylation research, a quantitative statistical relationship between DNA methylation and different ages was established based on the change rule of methylation with age, it is then possible to predict the age of individuals. All the data in this work were retrieved from the Illumina HumanMethylation BeadChip platform (27K or 450K). We analyzed 16 sets of healthy samples and 9 sets of diseased samples. The healthy samples included a total of 1899 publicly available blood samples (0–103 years old) and the diseased samples included 2395 blood samples. Six age-related CpG sites were selected through calculating Pearson correlation coefficients between age and DNA methylation values. We built a gradient boosting regressor model for these age-related CpG sites. 70% of the data was randomly selected as training data and the other 30% as independent data in each dataset for 25 runs in total. In the training dataset, the healthy samples showed that the correlation between predicted age and DNA methylation was 0.97, and the mean absolute deviation (MAD) was 2.72 years. In the independent dataset, the MAD was 4.06 years. The proposed model was further tested using the diseased samples. The MAD was 5.44 years for the training dataset and 7.08 years for the independent dataset. Furthermore, our model worked well when it was applied to saliva samples. These results illustrated that the age prediction based on six DNA methylation markers is very effective using the gradient boosting regressor.

scholarly journals Age Prediction of Human Based on DNA Methylation by Blood Tissues

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 870
Author(s):  
Jiansheng Zhang ◽  
Hongli Fu ◽  
Yan Xu
Keyword(s):  
Dna Methylation ◽  
Pearson Correlation ◽  
Close Correlation ◽  
Gradient Boosting ◽  
Tissue Samples ◽  
Regression Methods ◽  
Cpg Sites ◽  
Testing Dataset ◽  
Age Related ◽  
Better Than

In recent years, scientists have found a close correlation between DNA methylation and aging in epigenetics. With the in-depth research in the field of DNA methylation, researchers have established a quantitative statistical relationship to predict the individual ages. This work used human blood tissue samples to study the association between age and DNA methylation. We built two predictors based on healthy and disease data, respectively. For the health data, we retrieved a total of 1191 samples from four previous reports. By calculating the Pearson correlation coefficient between age and DNA methylation values, 111 age-related CpG sites were selected. Gradient boosting regression was utilized to build the predictive model and obtained the R2 value of 0.86 and MAD of 3.90 years on testing dataset, which were better than other four regression methods as well as Horvath’s results. For the disease data, 354 rheumatoid arthritis samples were retrieved from a previous study. Then, 45 CpG sites were selected to build the predictor and the corresponded MAD and R2 were 3.11 years and 0.89 on the testing dataset respectively, which showed the robustness of our predictor. Our results were better than the ones from other four regression methods. Finally, we also analyzed the twenty-four common CpG sites in both healthy and disease datasets which illustrated the functional relevance of the selected CpG sites.

Parallel Genetic Algorithm for Design of Morphing Cellular Truss Structures

Aerospace ◽  
2005 ◽  
Author(s):  
Deepak S. Ramrakhyani ◽  
George A. Lesieutre ◽  
Mary Frecker ◽  
Smita Bharti
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Unit Cell ◽  
Truss Structures ◽  
Optimization Approach ◽  
Parallel Genetic Algorithm ◽  
Ground Structure ◽  
Structural Layout ◽  
Fitness Value ◽  
Wing Structure

A parallel genetic algorithm is developed for the design of morphing aircraft structures using tendon actuated compliant truss. The wing structure in this concept is made of solid members and cables. The solid members are connected through compliant joints so that they can be deformed relatively easily without storing much strain energy in the structure. The structure is actuated using cables to deform into a required shape. Once the structure is deformed, the cables are locked and hence carry loads. Previously an octahedral unit cell made of cables and truss members was developed to achieve the required shape change of a morphing wing developed at NASA. It was observed that a continuously deformable truss structure with required morphing capability can be achieved by a cellular geometry tailored to local strain deformation. A wing structure made of these unit cells was sized for a representative aircraft and was found to be suitable. This paper describes the development of new unit cell designs that fit the morphing requirements using topology optimization. A ground structure approach is used to set up the problem. A predetermined set of points is selected and the members are connected in between the neighboring nodes. Each member in this ground structure has four possibilities, 1) a truss member, 2) a cable that morphs the structure into a required shape, 3) a cable that is antagonistic and brings it back to the original shape 4) a void, i.e., the member doesn’t exist in the structure. This choice is represented with a discrete variable. A parallel genetic algorithm is used as an optimization approach to optimize the variables in the ground structure to get the best structural layout. The parallelization is done using a master slave process. A fitness function is used to calculate how well a structural layout fits the design requirements. In general, a unit cell that has lesser deflection under external loads and higher deflection under actuation has a higher fitness value. Other requirements such as having fewer cables and achieving a required morphing shape are also included in the fitness function. The finite element calculations in the fitness function can be done using either linear or nonlinear analysis. The paper discusses the different ways of formulating the fitness function and the results thereof.

scholarly journals High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm

2016 ◽  
Vol Volume 112 (Number 1/2) ◽  
Author(s):  
Dieter Hendricks ◽  
Tim Gebbie ◽  
Diane Wilcox ◽  
◽  
◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Graphics Processing Units ◽  
High Speed ◽  
Spanning Trees ◽  
Fitness Function ◽  
Low Cost ◽  
Cost Effective ◽  
Fourth Generation ◽  
Parallel Genetic Algorithm ◽  
Matrix Estimation

Abstract We implement a master-slave parallel genetic algorithm with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs) to implement a parallel genetic algorithm and visualise the results using disjoint minimal spanning trees. We demonstrate that our GPU parallel genetic algorithm, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This approach represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable because of compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.

Research on text feature clustering based on an improved parallel genetic algorithm

2015 ◽  
Author(s):  
M. Y. Jiang ◽  
X. J. Fan ◽  
Y. X. Zhou ◽  
J. Lian ◽  
J. Q. Jiang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Parallel Genetic Algorithm ◽  
Feature Clustering ◽  
Text Feature

scholarly journals Application of Genetic Algorithm Elements to Modelling of Rotation Processes in Motion Transmission Including a Long Shaft

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 115
Author(s):  
Andriy Chaban ◽  
Marek Lis ◽  
Andrzej Szafraniec ◽  
Radoslaw Jedynak
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Least Square ◽  
Distributed Parameter ◽  
Parameter Method ◽  
Analytical Mechanics ◽  
Parameter System ◽  
Rotational Systems ◽  
Elastic Elements

Genetic algorithms are used to parameter identification of the model of oscillatory processes in complicated motion transmission of electric drives containing long elastic shafts as systems of distributed mechanical parameters. Shaft equations are generated on the basis of a modified Hamilton–Ostrogradski principle, which serves as the foundation to analyse the lumped parameter system and distributed parameter system. They serve to compute basic functions of analytical mechanics of velocity continuum and rotational angles of shaft elements. It is demonstrated that the application of the distributed parameter method to multi-mass rotational systems, that contain long elastic elements and complicated control systems, is not always possible. The genetic algorithm is applied to determine the coefficients of approximation the system of Rotational Transmission with Elastic Shaft by equivalent differential equations. The fitness function is determined as least-square error. The obtained results confirm that application of the genetic algorithms allow one to replace the use of a complicated distributed parameter model of mechanical system by a considerably simpler model, and to eliminate sophisticated calculation procedures and identification of boundary conditions for wave motion equations of long elastic elements.

scholarly journals Age-related differences in monocyte DNA methylation and immune function in healthy Kenyan adults and children

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Katherine R. Dobbs ◽  
Paula Embury ◽  
Emmily Koech ◽  
Sidney Ogolla ◽  
Stephen Munga ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Young Adults ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Innate Immune ◽  
Inflammatory Gene Expression ◽  
Cpg Sites ◽  
Age Related ◽  
Adults And Children

Abstract Background Age-related changes in adaptive and innate immune cells have been associated with a decline in effective immunity and chronic, low-grade inflammation. Epigenetic, transcriptional, and functional changes in monocytes occur with aging, though most studies to date have focused on differences between young adults and the elderly in populations with European ancestry; few data exist regarding changes that occur in circulating monocytes during the first few decades of life or in African populations. We analyzed DNA methylation profiles, cytokine production, and inflammatory gene expression profiles in monocytes from young adults and children from western Kenya. Results We identified several hypo- and hyper-methylated CpG sites in monocytes from Kenyan young adults vs. children that replicated findings in the current literature of differential DNA methylation in monocytes from elderly persons vs. young adults across diverse populations. Differentially methylated CpG sites were also noted in gene regions important to inflammation and innate immune responses. Monocytes from Kenyan young adults vs. children displayed increased production of IL-8, IL-10, and IL-12p70 in response to TLR4 and TLR2/1 stimulation as well as distinct inflammatory gene expression profiles. Conclusions These findings complement previous reports of age-related methylation changes in isolated monocytes and provide novel insights into the role of age-associated changes in innate immune functions.

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