Showing papers on "Mutation (genetic algorithm) published in 2019"

Mutation Testing Advances: An Analysis and Survey

Abstract: Mutation testing realizes the idea of using artificial defects to support testing activities. Mutation is typically used as a way to evaluate the adequacy of test suites, to guide the generation of test cases, and to support experimentation. Mutation has reached a maturity phase and gradually gains popularity both in academia and in industry. This chapter presents a survey of recent advances, over the past decade, related to the fundamental problems of mutation testing and sets out the challenges and open problems for the future development of the method. It also collects advices on best practices related to the use of mutation in empirical studies of software testing. Thus, giving the reader a “mini-handbook”-style roadmap for the application of mutation testing as experimental methodology.

Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach

Abstract: Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operators. This paper reviews various methods for choosing mutation and crossover ratios in GAs. Next, we define new deterministic control approaches for crossover and mutation rates, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic Decreasing of High Crossover (ILM/DHC). The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress, where (DHM/ILC) starts with 100% ratio for mutations, and 0% for crossovers. Both mutation and crossover ratios start to decrease and increase, respectively. By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers. (ILM/DHC) worked the same but the other way around. The proposed approach was compared with two parameters tuning methods (predefined), namely fifty-fifty crossover/mutation ratios, and the most common approach that uses static ratios such as (0.03) mutation rates and (0.9) crossover rates. The experiments were conducted on ten Traveling Salesman Problems (TSP). The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was found to be more effective when using large population size. In fact, both proposed dynamic methods outperformed the predefined methods compared in most cases tested.

cIMPACT-NOW update 4: diffuse gliomas characterized by MYB , MYBL1 , or FGFR1 alterations or BRAF V600E mutation

Abstract: Diffuse gliomas occur at all ages, but their incidence is highest among older adults [7]. They have astrocytic or oligodendroglial morphologies and are represented across WHO grades II–IV. In childhood, they are uncommon, presenting less frequently than the broad range of pediatric circumscribed gliomas, particularly pilocytic astrocytoma, and a few glioneuronal tumors, e.g., ganglioglioma [16]. The genetically defined IDH-mutant diffuse gliomas and the diffuse midline glioma, H3 K27M-mutant were introduced in the 2016 edition of the WHO classification [13]. In that update of the classification, IDH-wt/H3-wt diffuse gliomas are currently assigned to IDH-wt or NOS (not otherwise specified) diagnoses based upon morphology, grade, and IDH status when available. However, this scheme masks a heterogeneity that has implications for outcome and treatment; IDH-wt/H3-wt tumors with the same histologic features can harbor distinct genetic alterations and can demonstrate significantly different clinical outcomes and responses to targeted chemotherapy, all of which might influence the selection of an optimal adjuvant therapy. IDH-wt/H3-wt diffuse gliomas, arising mainly in middleaged adults, with WHO grade II/III histologic features and either combined chromosome seven gain and chromosome ten loss, or a TERT promoter region mutation, or EGFR amplification have a relatively aggressive behavior, with outcomes that are marginally better than those of IDH-wt glioblastomas [2, 4]. In contrast, historic studies of WHO grade II diffuse gliomas from children and adolescents, which we now know must be dominated by IDH-wt/H3-wt tumors with either a BRAFV600E mutation, an FGFR alteration, or a MYB or MYBL1 rearrangement, describe an indolent clinical behavior and rare anaplastic progression [3, 8, 18, 23, 24]. Patients with these tumors generally have a prolonged disease course and good overall survival, despite suffering significant morbidity during their chronic disease. In the context of this heterogeneity, WHO grading and the term ‘low-grade glioma’ have diminished utility; entirely different approaches to the post-operative management of a WHO grade II diffuse glioma from each of these two genetic categories would be appropriate. Therefore, the cIMPACT Steering Committee decided that it would be valuable for our working committee to complement the recommendations of ‘Update 3′ by addressing the heterogeneity among IDH-wt/H3-wt diffuse gliomas from the perspective of pediatric practice and focusing on tumors from the latter genetic category [2].

Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization

Abstract: Proposing new mutation strategies to improve the optimization performance of differential evolution (DE) is an important research study. Therefore, the main contribution of this paper goes in three directions: The first direction is introducing a less greedy mutation strategy with enhanced exploration capability, named DE/current-to-ord_best/1 (ord stands for ordered) or ord_best for short. In the second direction, we introduce a more greedy mutation strategy with enhanced exploitation capability, named DE/current-to-ord_pbest/1 (ord_pbest for short). Both of the proposed mutation strategies are based on ordering three selected vectors from the current generation to perturb the target vector, where the directed differences are used to mimic the gradient decent behavior to direct the search toward better solutions. In ord_best, the three vectors are selected randomly to enhance the exploration capability of the algorithm. On the other hand, ord_pbest is designed to enhance the exploitation capability where two vectors are selected randomly and the third is selected from the global p best vectors. Based on the proposed mutation strategies, ord_best and ord_pbest, two DE variants are introduced as EDE and EBDE, respectively. The third direction of our work is a hybridization framework. The proposed mutations can be combined with DE family algorithms to enhance their search capabilities on difficult and complicated optimization problems. Thus, the proposed mutations are incorporated into SHADE and LSHADE to enhance their performance. Finally, in order to verify and analyze the performance of the proposed mutation strategies, numerical experiments were conducted using CEC2013 and CEC2017 benchmarks. The performance was also evaluated using CEC2010 designed for Large-Scale Global Optimization. Experimental results indicate that in terms of robustness, stability, and quality of the solution obtained, both mutation strategies are highly competitive, especially as the dimension increases.

The Causes and Consequences of Genetic Interactions (Epistasis)

Abstract: The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dep...

Phase 1 study evaluating the safety, tolerability, pharmacokinetics (PK), and efficacy of AMG 510, a novel small molecule KRASG12C inhibitor, in advanced solid tumors.

Abstract: 3003Background: The KRASG12C mutation is found in approximately 13% of lung adenocarcinomas and 1–3% of other solid tumors, but there is no approved therapy that targets this mutation. AMG 510 is a...

Genetic Algorithm- A Literature Review

Abstract: Genetic Algorithm (GA) may be attributed as method for optimizing the search tool for difficult problems based on genetics selection principle. In additions to Optimization it also serves the purpose of machine learning and for Research and development. It is analogous to biology for chromosome generation with variables such as selection, crossover and mutation together constituting genetic operations which would be applicable on a random population initially. GA aims to yield solutions for the consecutive generations. The extent of success in individual production is directly in proportion to fitness of solution which is represented by it, thereby ensuring that quality in successive generations will be better. The process is concluded once an GA is most suitable for the issues that need optimization associated with some computable system.. John Holland may be regarded as funding father of original genetic algorithm and is attributed to year 1970’s as funding date. Additionally a random search method represented by Charles Darwin for a defined search space in order to effetely solve a problem. In this paper, what is genetic algorithm and its basic workflow is discussed how a genetic algorithm work and what are the process is included in this is also discussed. Further, the features and application of genetic algorithm are mentioned in the paper.

Tissue-specificity in cancer: The rule, not the exception.

Abstract: Cancer driver genes exhibit remarkable tissue-specificity We are in the midst of a renaissance in cancer genetics. Over the past several decades, candidate-based targeted sequencing efforts provided a steady stream of information on the genetic drivers for certain cancer types. However, with recent technological advances in DNA sequencing, this stream has become a torrent of unbiased genetic information revealing the frequencies and patterns of point mutations and copy number variations (CNVs) across the entire spectrum of cancers. One of the most important observations from this work is that genetic alterations in bona fide cancer drivers (those genes that, when mutated, promote tumorigenesis) show a remarkable spectrum of tissue specificity: Alterations in certain driver genes appear only in cancers derived from one or a few tissue types (1). Only a handful of cancer drivers [such as telomerase reverse transcriptase (TERT), TP53, the cyclin-dependent kinase inhibitor 2A (CDKN2A) locus, and MYC] show broad tissue spectrums. Here, we discuss the concept of tissue specificity of genetic alterations in cancer and provide general hypotheses to help explain this biological phenomenon.

Genetic Characteristics of Aldosterone-Producing Adenomas in Blacks.

Abstract: Somatic mutations have been identified in aldosterone-producing adenomas (APAs) in genes that include KCNJ5, ATP1A1, ATP2B3, and CACNA1D. Based on independent studies, there appears to be racial di...

The many faces of p53: something for everyone.

Abstract: The p53 gene, from discovery to classification: the first 10 years Forty years ago four research laboratories in London, Paris, New York/Bethesda, and Princeton uncovered the existence of the p53 protein (Deleo et al., 1979; Lane and Crawford, 1979; Linzer and Levine, 1979; Kress et al., 1979). Each laboratory came upon this protein for a different reason and with a different experimental approach that uncovered this unanticipated result. Together, the four papers permitted one to conclude the following: (i) in SV40-infected and transformed cells the SV40-encoded oncogene protein, the large T-antigen, formed a protein complex with a cellular-encoded protein of ∼53000 daltons in size. (ii) This p53 protein was detected at high levels in a variety of transformed cells derived from viral, chemical, or inherited (teratocarcinomas) transformation events. (iii) Nontransformed cells expressed lower levels of the p53 protein. (iv) Animals bearing tumors produced antibodies directed against the p53 protein. A temperature-sensitive mutation in the SV40 large T-antigen gene (the oncogene of this virus) was employed to demonstrate that the p53–T-antigen complex was formed at the permissive temperature, where the cells are transformed, but not at the nonpermissive temperature, where the cells behave normally (Linzer and Levine, 1979; Linzer et al., 1979). At a later date p53 protein complexes with viral oncogene products were observed, including the adenovirus E1b-58kd protein (Sarnow et al., 1982) and the human papilloma virus E6 oncoprotein (Scheffner, et al. 1990; Werness et al., 1990; which is the cause of human cervical cancers and some head and neck cancers). In order to explore the functions of the p53 protein, several p53 cDNAs were isolated and cloned (Oren and Levine, 1983; Oren et al., 1983; Pennica et al., 1984). These clones were tested for oncogene activities and found to cooperate with the RAS oncogene in transforming embryonic cells (Eliyahu et al., 1984; Parada et al., 1984). Thus, it appeared that the p53 gene was an oncogene whose protein forms a complex with viral oncogene proteins, possibly mediating transformation. However, the cDNA clone isolated by Pennica failed to transform cells in culture and had a single amino acid change when compared with the Oren cDNA clone, which did transform cells. Was the amino acid difference between these clones significant? Was this difference a sequencing mistake? A polymorphism? Or a mutation? If it was a mutation, which clone was the wild-type and which was the mutant? To address these questions, Oren and Levine exchanged clones (and reproduced each other’s observations). By 1989 it became clear that mutations in the p53 cDNA clones resulted in cellular transformation, and wild-type p53 protein prevented transformation and functioned as a tumor suppressor (Eliyahu et al., 1989; Finlay et al., 1989). p53 mutations in both p53 alleles in colon cancers of humans resulted in the same conclusion; p53 functioned as a tumor suppressor gene that helped to prevent cancer (Baker et al., 1990a, b, Nigro et al., 1995). From 1979 to 1989 the p53 protein was alternatively referred to as a fetal antigen expressed in the teratocarcinoma stem cells, a tumor antigen that induced antibodies in animals and humans with tumors, an oncogene whose mutant forms could transform cells, and, finally, a tumor suppressor gene that prevented cancers. During this time the p53 protein was demonstrated to increase its concentration in response to DNA damage (Maltzman and Czyzyk, 1984). Over these first 10 years of research the p53 protein was shown to have many diverse faces and activities, functioning as an oncogene and a tumor suppressor gene while responding to DNA damage in a cell.

Clonal evolution patterns in acute myeloid leukemia with NPM1 mutation

Abstract: Mutations in the nucleophosmin 1 (NPM1) gene are considered founder mutations in the pathogenesis of acute myeloid leukemia (AML). To characterize the genetic composition of NPM1 mutated (NPM1mut) AML, we assess mutation status of five recurrently mutated oncogenes in 129 paired NPM1mut samples obtained at diagnosis and relapse. We find a substantial shift in the genetic pattern from diagnosis to relapse including NPM1mut loss (n = 11). To better understand these NPM1mut loss cases, we perform whole exome sequencing (WES) and RNA-Seq. At the time of relapse, NPM1mut loss patients (pts) feature distinct mutational patterns that share almost no somatic mutation with the corresponding diagnosis sample and impact different signaling pathways. In contrast, profiles of pts with persistent NPM1mut are reflected by a high overlap of mutations between diagnosis and relapse. Our findings confirm that relapse often originates from persistent leukemic clones, though NPM1mut loss cases suggest a second “de novo” or treatment-associated AML (tAML) as alternative cause of relapse. NPM1 gene mutation is a founding event in acute myeloid leukaemia. Here, the authors find that at relapse, some patients lose the NPM1 mutation and show distinct mutational and gene expression patterns, highlighting a potential route for relapse.

Microbiome Evolution During Host Aging

Abstract: Commensal microbes and their multicellular eukaryotic hosts constitute a highly integrated system—termed the holobiont [1]—which undergoes dynamic changes through time as it integrates and responds to signals from the environment. Dwelling at the interface between host epithelia and the external environment, commensal microbes actively modulate development, nutrient absorption, and disease onset in the host. Host metabolism is significantly modulated by commensal microbes, and the gut microbial composition significantly affects blood metabolite composition [2]. Microbial communities differ among epithelia, reaching the highest complexity and taxonomic diversity in the oral cavity and in the gastrointestinal tract [3, 4]. Environmental factors, such as diet, drug use, and social environment, shape the composition of epithelia-associated microbiota [5–7], and environmental heterogeneity—rather than host genetics—can explain much of the interindividual differences in microbiota composition in humans [8]. The assembly of specific host-associated communities, however, is also dictated by the host cell composition and activity, by the molecular components of the mucus layer, by the gut peristaltic contractility [9], and by epithelial integrity [10]. In primates, recent evidence supports that host phylogenetic relatedness and gut physiology are overall better predictors of microbiota composition than diet [11]. Together, the microbiota is a dynamic community, subject to changes in conjunction with host evolution and through the lifetime of individual hosts.

Fine-Grained Analysis of Spontaneous Mutation Spectrum and Frequency in Arabidopsis thaliana.

Abstract: Mutations are the ultimate source of all genetic variation. However, few direct estimates of the contribution of mutation to molecular genetic variation are available. To address this issue, we first analyzed the rate and spectrum of mutations in the Arabidopsis thaliana reference accession after 25 generations of single-seed descent. We then compared the mutation profile in these mutation accumulation (MA) lines against genetic variation observed in the 1001 Genomes Project. The estimated haploid single nucleotide mutation (SNM) rate for A. thaliana is 6.95 × 10−9 (SE ± 2.68 × 10−10) per site per generation, with SNMs having higher frequency in transposable elements (TEs) and centromeric regions. The estimated indel mutation rate is 1.30 × 10−9 (±1.07 × 10−10) per site per generation, with deletions being more frequent and larger than insertions. Among the 1694 unique SNMs identified in the MA lines, the positions of 389 SNMs (23%) coincide with biallelic SNPs from the 1001 Genomes population, and in 289 (17%) cases the changes are identical. Of the 329 unique indels identified in the MA lines, 96 (29%) overlap with indels from the 1001 Genomes dataset, and 16 indels (5% of the total) are identical. These overlap frequencies are significantly higher than expected, suggesting that de novo mutations are not uniformly distributed and arise at polymorphic sites more frequently than assumed. These results suggest that high mutation rate potentially contributes to high polymorphism and low mutation rate to reduced polymorphism in natural populations providing insights of mutational inputs in generating natural genetic diversity.

A Premature Termination Codon Mutation in MYBPC3 Causes Hypertrophic Cardiomyopathy via Chronic Activation of Nonsense-Mediated Decay

Abstract: Background: Hypertrophic cardiomyopathy (HCM) is frequently caused by mutations in myosin-binding protein C3 (MYBPC3) resulting in a premature termination codon (PTC). The underlying mechanisms of ...

The architecture of intra-organism mutation rate variation in plants.

Abstract: Given the disposability of somatic tissue, selection can favor a higher mutation rate in the early segregating soma than in germline, as seen in some animals. Although in plants intra-organismic mutation rate heterogeneity is poorly resolved, the same selectionist logic can predict a lower rate in shoot than in root and in longer-lived terminal tissues (e.g., leaves) than in ontogenetically similar short-lived ones (e.g., petals), and that mutation rate heterogeneity should be deterministic with no significant differences between biological replicates. To address these expectations, we sequenced 754 genomes from various tissues of eight plant species. Consistent with a selectionist model, the rate of mutation accumulation per unit time in shoot apical meristem is lower than that in root apical tissues in perennials, in which a high proportion of mutations in shoots are themselves transmissible, but not in annuals, in which somatic mutations tend not to be transmissible. Similarly, the number of mutations accumulated in leaves is commonly lower than that within a petal of the same plant, and there is no more heterogeneity in accumulation rates between replicate branches than expected by chance. High mutation accumulation in runners of strawberry is, we argue, the exception that proves the rule, as mutation transmission patterns indicate that runner has a restricted germline. However, we also find that in vitro callus tissue has a higher mutation rate (per unit time) than the wild-grown comparator, suggesting nonadaptive mutational "fragility". As mutational fragility does not obviously explain why the shoot-root difference varies with plant longevity, we conclude that some mutation rate variation between tissues is consistent with selectionist theory but that a mechanistic null of mutational fragility should be considered.

Similarities and differences in patterns of germline mutation between mice and humans.

Abstract: Whole genome sequencing (WGS) studies have estimated the human germline mutation rate per basepair per generation (~1.2 × 10−8) to be higher than in mice (3.5–5.4 × 10−9). In humans, most germline mutations are paternal in origin and numbers of mutations per offspring increase with paternal and maternal age. Here we estimate germline mutation rates and spectra in six multi-sibling mouse pedigrees and compare to three multi-sibling human pedigrees. In both species we observe a paternal mutation bias, a parental age effect, and a highly mutagenic first cell division contributing to the embryo. We also observe differences between species in mutation spectra, in mutation rates per cell division, and in the parental bias of mutations in early embryogenesis. These differences between species likely result from both species-specific differences in cellular genealogies of the germline, as well as biological differences within the same stage of embryogenesis or gametogenesis. Estimates of mutation rates differ between species. Here, Lindsay et al. perform side-by-side analyses of germline mutation rates using multi-sibling mouse and human pedigrees and find different mutation rates between species, also stratified by sex and temporal stage of mutation acquisition.

An improved genetic algorithm for numerical function optimization

Abstract: To avoid problems such as premature convergence and falling into a local optimum, this paper proposes an improved real-coded genetic algorithm (RCGA-rdn) to improve the performance in solving numerical function optimization. These problems are mainly caused by the poor search ability of the algorithm and the loss of population diversity. Therefore, to improve the search ability, the algorithm integrates three specially designed operators: ranking group selection (RGS), direction-based crossover (DBX) and normal mutation (NM). In contrast to the traditional strategy framework, RCGA-rdn introduces a new step called the replacement operation, which periodically performs a local initialization operation on the population to increase the population diversity. In this paper, comparisons with several advanced algorithms were performed on 21 complex constrained optimization problems and 10 high-dimensional unconstrained optimization problems to verify the effectiveness of RCGA-rdn. Based on the results, to further verify the feasibility of the algorithm, it was applied to a series of practical engineering optimization problems. The experimental results show that the proposed operations can effectively improve the performance of the algorithm. Compared with the other algorithms, the improved algorithm (RCGA-rdn) has a better search ability, faster convergence speed and can maintain a certain population diversity.

Deep convolutional neural networks for accurate somatic mutation detection.

Abstract: Accurate detection of somatic mutations is still a challenge in cancer analysis. Here we present NeuSomatic, the first convolutional neural network approach for somatic mutation detection, which significantly outperforms previous methods on different sequencing platforms, sequencing strategies, and tumor purities. NeuSomatic summarizes sequence alignments into small matrices and incorporates more than a hundred features to capture mutation signals effectively. It can be used universally as a stand-alone somatic mutation detection method or with an ensemble of existing methods to achieve the highest accuracy.

A dynamic ant-colony genetic algorithm for cloud service composition optimization

Abstract: At present, as the candidate services in the cloud service pool increase, the scale of the service composition increases rapidly. When the existing intelligent optimization algorithms are used to solve the large-scale cloud service composition and optimization (CSCO) problem, it is difficult to ensure the high precision and stability of the optimization results. To overcome such drawbacks, a new dynamic ant-colony genetic hybrid algorithm (DAAGA) is proposed in this paper. The best fusion evaluation strategy is used to determine the invoking timing of genetic and ant-colony algorithms, so the executive time of the two algorithms can be controlled dynamically based on the current solution quality, then the optimization ability is maximized and the overall convergence speed is accelerated. An iterative adjustment threshold is introduced to control the genetic operation and population size in later iterations, in which the effect of genetic algorithm is reduced when the population closes to optimal solution, only the mutation operation is implemented to reduce the calculation, and the population size is increased to find the optimal solution more quickly. A series of comparison experiments are carried out and the results show that the accuracy and stability of DAAGA are significantly improved for the large-scale CSCO problem, and the time consumption of the algorithm is also optimized.

Short-Term Traffic Flow Prediction Based on Least Square Support Vector Machine with Hybrid Optimization Algorithm

Abstract: Accurate short-term traffic flow prediction plays an indispensable role for solving traffic congestion. However, the structure of traffic data is nonlinear and complicated. It is a challenge to get high precision. The least square support vector machine (LSSVM) has powerful capabilities for time series and nonlinear regression prediction problems if it can select appropriate parameters. To search the optimal parameters of LSSVM, this paper proposes a hybrid optimization algorithm which combines particle swarm optimization (PSO) with genetic algorithm. The main contributions are twofold: (1) A hybrid optimization method is proposed, which can skip the local optimal pitfall with less learning time by introducing a selection strategy, crossover and mutation operators into PSO; (2) the crossover and mutation operators are controlled by adaptive probability functions. The crossover and mutation probabilities increase when the population fitness is concentrated, and decrease when the fitness is dispersed. It can effectively improve the precision and speed of convergence. The proposed model is verified based on the measured data. The experimental results show that our new model yields better prediction ability and relatively high computational efficiency compared with other related models.

Historical and Heuristic-Based Adaptive Differential Evolution

Abstract: As the mutation strategy and algorithmic parameters in differential evolution (DE) are sensitive to the problems being solved, a hot research topic is to adaptively control the strategy and parameters according to the requirements of the problem. In the literature, most adaptive DE use either historical experiences of the population or heuristic information of the individuals to promote adaptation. In this paper, we develop a novel variant of adaptive DE, utilizing both the historical experience and heuristic information for the adaptation. In this novel historical and heuristic DE (HHDE), each individual dynamically adjusts its mutation strategy and associated parameters not only by learning from previous successful experience of the whole population, but also according to heuristic information related with its own current state. These help the algorithm select a more suitable mutation strategy and determinate better parameters for each individual in different evolutionary stages. The performance of the proposed HHDE is extensively evaluated on 30 benchmark functions with different dimensions. Experimental results confirm the competitiveness of the proposed algorithm to a number of DE variants.