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...

Adversarial sample detection for deep neural network through model mutation testing

Abstract: Deep neural networks (DNN) have been shown to be useful in a wide range of applications. However, they are also known to be vulnerable to adversarial samples. By transforming a normal sample with some carefully crafted human imperceptible perturbations, even highly accurate DNN make wrong decisions. Multiple defense mechanisms have been proposed which aim to hinder the generation of such adversarial samples. However, a recent work show that most of them are ineffective. In this work, we propose an alternative approach to detect adversarial samples at runtime. Our main observation is that adversarial samples are much more sensitive than normal samples if we impose random mutations on the DNN. We thus first propose a measure of 'sensitivity' and show empirically that normal samples and adversarial samples have distinguishable sensitivity. We then integrate statistical hypothesis testing and model mutation testing to check whether an input sample is likely to be normal or adversarial at runtime by measuring its sensitivity. We evaluated our approach on the MNIST and CIFAR10 datasets. The results show that our approach detects adversarial samples generated by state-of-the-art attacking methods efficiently and accurately.

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.

Mutation of ZmDMP enhances haploid induction in maize.

Abstract: Doubled haploid (DH) breeding based on in vivo haploid induction has led to a new approach for maize breeding1. All modern haploid inducers used in DH breeding are derived from the haploid inducer line Stock6. Two key quantitative trait loci, qhir1 and qhir8, lead to high-frequency haploid induction2. Mutation of the gene MTL/ZmPLA1/NLD in qhir1 could generate a ~2% haploid induction rate (HIR)3–5; nevertheless, this mutation is insufficient for modern haploid inducers whose average HIR is ~10%6. Therefore, cloning of the gene underlying qhir8 is important for illuminating the genetic basis of haploid induction. Here, we present the discovery that mutation of a non-Stock6-originating gene in qhir8, namely, ZmDMP, enhances and triggers haploid induction. ZmDMP was identified by map-based cloning and further verified by CRISPR–Cas9-mediated knockout experiments. A single-nucleotide change in ZmDMP leads to a 2–3-fold increase in the HIR. ZmDMP knockout triggered haploid induction with a HIR of 0.1–0.3% and exhibited a greater ability to increase the HIR by 5–6-fold in the presence of mtl/zmpla1/nld. ZmDMP was highly expressed during the late stage of pollen development and localized to the plasma membrane. These findings provide important approaches for studying the molecular mechanism of haploid induction and improving DH breeding efficiency in maize. By map-based cloning and knockout experiments, a study identified and validated ZmDMP to be one of the two genes known to control haploid induction in maize. A single-nucleotide mutation in ZmDMP causes a 2–3-fold increase in the haploid induction rate.

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.

Degradation of Bruton's tyrosine kinase mutants by PROTACs for potential treatment of ibrutinib-resistant non-Hodgkin lymphomas.

Abstract: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma (NHL). In the United States and United Kingdom, the annual prevalence of DLBCL is as high as 0.08% [1]. The first-line clinical treatment for DLBCL is combined chemo-immunotherapy with a 5-year survival rate of around 58% for senior adults [2]. Mantle cell lymphoma (MCL) contributes to more than 6–8% of NHL worldwide [3]. Because of the t(11;14)(q13; q32) chromosomal translocation and upregulated expression of cyclin D1, malignant proliferation is commonly observed in MCL [4]. Unfortunately, there is no optimal therapy for MCL [5] and the 5-year survival rate for MCL patients in the United States is <50% [6]. These statistics highlight an urgent need for the development of more effective treatments for patients with DLBCL or MCL. B-cell receptor (BCR) signaling is indispensable for the adhesion, survival, and growth of B cells. As an essential membrane proximal signal molecule in the BCR pathway, Bruton’s tyrosine kinase (BTK) plays a critical role in Bcell activation and proliferation [7]. In 2013, the BTK covalent inhibitor ibrutinib was approved by the Food and Drug Administration for the treatment of MCL. Additionally, activated B-cell-like (ABC)-DLBCL patients achieved remission after treatment with ibrutinib [8]. Unfortunately, drug-resistant tumor cells have been isolated from MCL patients during treatment with ibrutinib and the relapsespecific C481S missense BTK mutation contributes to this resistance [9]. In the growth inhibition of DLBCL, the BTK C481S mutant also resulted in resistance to ibrutinib [10]. Proteolysis-targeting chimera (PROTAC) is a novel strategy for selective knockdown of target proteins by small molecules [11]. PROTAC molecules are heterobifunctional compounds with three components: a target protein-binding moiety, an E3 ligase ligand, and a linker connecting the two (Fig. 1a). The degradation machinery brings the target cellular protein to the corresponding E3 ligase, resulting in degradation by the ubiquitinproteasome system. In recent years, this newly developed method has been widely used in antitumor studies [12, 13]. Recently, we developed the first PROTACderived degrader for selective BTK degradation [10, 14, 15]. Although preliminary data showed that our degrader effectively controlled BTK C481S mutant-induced ibrutinib-resistant B-cell malignancies in vitro, the following critical questions remain unaddressed. (1) Is the BTK degrader also effective for DLBCL in vivo? Due to the poor aqueous solubility of the first generation of our degrader, a new generation of BTK degraders with improved solubility must be developed for in vivo These authors contributed equally: Yonghui Sun, Ning Ding, Yuqin Song

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.

Molecular genetic analysis using targeted NGS analysis of 677 individuals with retinal dystrophy

Abstract: Inherited retinal diseases (IRDs) are a common cause of visual impairment. IRD covers a set of genetically highly heterogeneous disorders with more than 150 genes associated with one or more clinical forms of IRD. Molecular genetic diagnosis has become increasingly important especially due to expanding number of gene therapy strategies under development. Next generation sequencing (NGS) of gene panels has proven a valuable diagnostic tool in IRD. We present the molecular findings of 677 individuals, residing in Denmark, with IRD and report 806 variants of which 187 are novel. We found that deletions and duplications spanning one or more exons can explain 3% of the cases, and thus copy number variation (CNV) analysis is important in molecular genetic diagnostics of IRD. Seven percent of the individuals have variants classified as pathogenic or likely-pathogenic in more than one gene. Possible Danish founder variants in EYS and RP1 are reported. A significant number of variants were classified as variants with unknown significance; reporting of these will hopefully contribute to the elucidation of the actual clinical consequence making the classification less troublesome in the future. In conclusion, this study underlines the relevance of performing targeted sequencing of IRD including CNV analysis as well as the importance of interaction with clinical diagnoses.

Impact of the H3K27M mutation on survival in pediatric high-grade glioma: a systematic review and meta-analysis.

Abstract: OBJECTIVE Pediatric high-grade gliomas (pHGGs), including diffuse intrinsic pontine glioma, present a prognostic challenge given their lethality and rarity. A substitution mutation of lysine for methionine at position 27 in histone H3 (H3K27M) has been shown to be highly specific to these tumors. Data are accumulating regarding the poor outcomes of patients with these tumors; however, the quantification of pooled outcomes has yet to be done, which could assist in prioritizing management. The aim of this study was to quantitatively pool data in the current literature on the H3K27M mutation as an independent prognostic factor in pHGG. METHODS Searches of seven electronic databases from their inception to March 2018 were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data were extracted and pooled using a meta-analysis of proportions. Meta-regression was used to identify potential sources of heterogeneity. RESULTS Six observational studies satisfied the selection criteria for inclusion. They reported the survival outcomes of a pooled cohort of 474 pHGG patients, with 258 (54%) and 216 (46%) patients positive and negative, respectively, for the H3K27M mutation. Overall, the presence of the mutation was independently and significantly associated with a worse prognosis (HR 3.630, p < 0.001). Overall survival was significantly shorter (by 2.300 years; p = 0.008) when the H3K27M mutation was present in pHGG. Meta-regression did not identify any study covariates of heterogeneous concern. CONCLUSIONS According to the current literature, pHGG patients positive for the H3K27M mutation are more than 3 times more susceptible to succumbing to this disease by more than 2 years, compared to patients negative for the mutation. More robust outcome data are required to improve our quantitative understanding of this pathological entity in order to assist in prioritizing clinical management. Future larger prospective studies are required to overcome inherent biases in the current literature to validate the quantitative findings of this study. ABBREVIATIONS CI = confidence interval; GRADE = Grades of Recommendation Assessment, Development and Evaluation; HR = hazard ratio; MD = mean difference; NOS = Newcastle-Ottawa Scale; OS = overall survival; pHGG = pediatric high-grade glioma; PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses; RE = random effects.