Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Hybrid Gene Selection Methods for High-Dimensional Lung Cancer Data Using Improved Arithmetic Optimization Algorithm

Lung cancer is among the most frequent cancers in the world,with over one million deaths per year.Classification is required for lung cancer diagnosis and therapy to be effective,accurate,and reliable.Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner.Machine Learning(ML)has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique.Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification.Normally,microarrays include several genes and may cause confusion or false prediction.Therefore,the Arithmetic Optimization Algorithm(AOA)is used to identify the optimal gene subset to reduce the number of selected genes.Which can allow the classifiers to yield the best performance for lung cancer classification.In addition,we proposed a modified version of AOA which can work effectively on the high dimensional dataset.In the modified AOA,the features are ranked by their weights and are used to initialize the AOA population.The exploitation process of AOA is then enhanced by developing a local search algorithm based on two neighborhood strategies.Finally,the efficiency of the proposed methods was evaluated on gene expression datasets related to Lung cancer using stratified 4-fold cross-validation.The method’s efficacy in selecting the optimal gene subset is underscored by its ability to maintain feature proportions between 10%to 25%.Moreover,the approach significantly enhances lung cancer prediction accuracy.For instance,Lung_Harvard1 achieved an accuracy of 97.5%,Lung_Harvard2 and Lung_Michigan datasets both achieved 100%,Lung_Adenocarcinoma obtained an accuracy of 88.2%,and Lung_Ontario achieved an accuracy of 87.5%.In conclusion,the results indicate the potential promise of the proposed modified AOA approach in classifying microarray cancer data.

Assessment of molecular markers and marker-assisted selection for drought tolerance in barley(Hordeum vulgare L.)

This review updates the present status of the field of molecular markers and marker-assisted selection(MAS),using the example of drought tolerance in barley.The accuracy of selected quantitative trait loci(QTLs),candidate genes and suggested markers was assessed in the barley genome cv.Morex.Six common strategies are described for molecular marker development,candidate gene identification and verification,and their possible applications in MAS to improve the grain yield and yield components in barley under drought stress.These strategies are based on the following five principles:(1)Molecular markers are designated as genomic‘tags’,and their‘prediction’is strongly dependent on their distance from a candidate gene on genetic or physical maps;(2)plants react differently under favourable and stressful conditions or depending on their stage of development;(3)each candidate gene must be verified by confirming its expression in the relevant conditions,e.g.,drought;(4)the molecular marker identified must be validated for MAS for tolerance to drought stress and improved grain yield;and(5)the small number of molecular markers realized for MAS in breeding,from among the many studies targeting candidate genes,can be explained by the complex nature of drought stress,and multiple stress-responsive genes in each barley genotype that are expressed differentially depending on many other factors.

An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

Autism Spectrum Disorder(ASD)is a complicated neurodevelopmen-tal disorder that is often identified in toddlers.The microarray data is used as a diagnostic tool to identify the genetics of the disorder.However,microarray data is large and has a high volume.Consequently,it suffers from the problem of dimensionality.In microarray data,the sample size and variance of the gene expression will lead to overfitting and misclassification.Identifying the autism gene(feature)subset from microarray data is an important and challenging research area.It has to be efficiently addressed to improve gene feature selection and classification.To overcome the challenges,a novel Intelligent Hybrid Ensem-ble Gene Selection(IHEGS)model is proposed in this paper.The proposed model integrates the intelligence of different feature selection techniques over the data partitions.In this model,the initial gene selection is carried out by data perturba-tion,and thefinal autism gene subset is obtained by functional perturbation,which reduces the problem of dimensionality in microarray data.The functional perturbation module employs three meta-heuristic swarm intelligence-based tech-niques for gene selection.The obtained gene subset is validated by the Deep Neural Network(DNN)model.The proposed model is implemented using python with six National Center for Biotechnology Information(NCBI)gene expression datasets.From the comparative study with other existing state-of-the-art systems,the proposed model provides stable results in terms of feature selection and clas-sification accuracy.

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

Gene expression(GE)classification is a research trend as it has been used to diagnose and prognosis many diseases.Employing machine learning(ML)in the prediction of many diseases based on GE data has been a flourishing research area.However,some diseases,like Alzheimer’s disease(AD),have not received considerable attention,probably owing to data scarcity obstacles.In this work,we shed light on the prediction of AD from GE data accurately using ML.Our approach consists of four phases:preprocessing,gene selection(GS),classification,and performance validation.In the preprocessing phase,gene columns are preprocessed identically.In the GS phase,a hybrid filtering method and embedded method are used.In the classification phase,three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase.The final phase is to validate the performance of these classifiers using different metrics.The crux of this article is to select the most informative genes from the hybrid method,and the best ML technique to predict AD using this minimal set of genes.Five different datasets are used to achieve our goal.We predict AD with impressive values forMultiLayer Perceptron(MLP)classifier which has the best performance metrics in four datasets,and the Support Vector Machine(SVM)achieves the highest performance values in only one dataset.We assessed the classifiers using sevenmetrics;and received impressive results,allowing for a credible performance rating.The metrics values we obtain in our study lie in the range[.97,.99]for the accuracy(Acc),[.97,.99]for F1-score,[.94,.98]for kappa index,[.97,.99]for area under curve(AUC),[.95,1]for precision,[.98,.99]for sensitivity(recall),and[.98,1]for specificity.With these results,the proposed approach outperforms recent interesting results.With these results,the proposed approach outperforms recent interesting results.

Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.

Molecular Marker Assisted Selection for Yield-Enhancing Genes in the Progeny of Minghui63 x O. rufipogon

Two yield-enhancing genes (yld1.1 and yld2.1) are located on chromosomes 1 and 2 respectivelyin a weedy relative of cultivated rice, Oryza rufipogon. SSR markers RM9 and RM166 are closelylinked with the two loci respectively. Minghui63 (MH63) has been a widely used restorationline in hybrid rice production in China during the past two decades. The F1 of cross 'MH63O.rufipogon' was backcrossed with MH63 generation by generation. RM9 and RM166 were used toselect the plants from the progeny of the backcross populations. The results were as follows:(1) In BC2F1 population, the percentage of the individuals which have RM9 and RM166 amplifiedbands simultaneously was 12.2%, while in the BC3F1 population, that was 16.3%. (2) Among 400individuals of BC3F1, four yield-promising plants were obtained, with yield being 30% more thanthat of MH63. (3) The products amplified by primer RM166 in O. rufipogon and MH63 weresequenced. It was found that the DNA fragment sequence amplified by RM166 from MH63 was 101 bpshorter than that from O. rufipogon. The 101bp sequence is a part of an intron of the PCNA(proliferating cell nuclear antigen) gene.

Transferring Translucent Endosperm Mutant Gene Wx-mq and Rice Stripe Disease Resistance Gene Stv-bi by Marker-Assisted Selection in Rice (Oryza sativa)

A high-yielding japonica rice variety, Wuyunjing 7, bred in Jiangsu Province, China as a female parent was crossed with a Japanese rice variety Kantou 194, which carries a rice stripe disease resistance gene Stv-b＇ and a translucent endosperm mutant gene Wx-mq. From F2 generations, a sequence characterized amplified region （SCAR） marker tightly linked with Stv-b＇ and a cleaved amplified polymorphic sequence （CAPS） marker for Wx-mq were used for marker-assisted selection. Finally, a new japonica rice line, Ning 9108, with excellent agronomic traits was obtained by multi-generational selection on stripe disease resistance and endosperm appearance. The utilization of the markers from genes related to rice quality and disease resistance was helpful not only for establishing a marker-assisted selection system of high-quality and disease resistance for rice but also for providing important intermediate materials and rapid selection method for good quality, disease resistance and high yield in rice breeding.

Stimulation Study of Gene Pyramiding inAnimals by Marker-Assisted Selection

This gene pyramiding strategy is based on the idea of efficiently pyramiding genes of interest by crosses and selection to obtain a population with favorable alleles from different breeds or lines, which is called an ideal population. We investigate impacts of some factors on the pyramiding efficiencies by simulation. These factors include selection strategies （the breeding value selection, the molecular scores selection and the index selection）, proportion selected （2, 10 and 20%）, recombination rates between adjacent target genes （0.1, 0.3 and 0.5） and different mating types （the random mating and the positive assortative mating avoiding sib mating）. The results show that： （1） The more recombination rate and the lower proportion male selected, the better pyramiding efficiency; （2） the ideal population is obtained via various selection strategies, while different selection strategies are suitable for different breeding objectives. From the perspective of pyramiding target genes merely, the molecular scores selection is the best one, for the purpose of pyramiding target genes and recovering genetic background of the target trait, the index selection is the best one, while from the saving cost point of view, the breeding value selection is the best one; （3） the positive assortative mating is more efficient for gene pyramiding compared with the random mating in the terms of the number of generations of intercross for getting the ideal population.

Genetic Diversity of Recurrent Selection Populations with Ms2 Gene Assessed by Gliadins in Common Wheat (Triticum aestivum L.)

The male-sterile lines with Ms2 gene were highly evaluated in recurrent selection in wheat （Triticum aestivum L.）. Three populations C6 （population after six cycles of selection）, C7 （population after seven cycles of selection）, and C8 （population after eight cycles of selection） were constructed through recurrent selection with 12 parental materials （P）. Acid polyacrymide gel electrophoresis （A-PAGE） analysis was used to identify gliadin patterns and evaluate the genetic diversity in 12 parents and three populations. A total of 63 bands were identified, of which 17 polymorphic bands and 7 unique bands were present in populations and seven polymorphic bands and four unique bands were present in parents. The number of polymorphic and unique bands decreased gradually from C6 to C8, especially for to- and y-gliadins. The genetic distances in C6, C7, and C8 were calculated. The distributions of genetic distance were different in three recurrent selection populations. From C6 to C8, the genetic distance was 0.2687, 0.2652 and 0.1987, respectively. Statistically significant differences were detected between C7 and C8 with the T value of 37.9718. The result of cluster analysis based on genetic similarity matrix of three populations fitted well to those of principle coordinates analysis （PCoA）. Compared with 12 parents, almost all individuals of three populations are new genotypes. Most of the individuals from C6 and C7 could be divided into two groups, while most individuals of C8 were in one cluster. In conclusion, the results indicated that the genetic diversity was decreased severely according to the information revealed by A-PAGE, although some variations could be created in the recurrent selection. It was necessary to introduce diverse germplasm based on the genetic database of recurrent population to maintain and improve the breeding efficiency in the further program.

SCAR Markers Assisted Selection for a Bentazon Susceptible Lethality Gene (ben) in Rice

In progenies resulting from crosses involving rice cultivar Norin 8m susceptible to bentazon as the donor of ben gene, SCARs tightly linked to ben were utilized for selection of ben. The homozygous and heterozygous genotypes with ben could be identified with the SCARs. The molecular markers offer a powerful tool for indirect selection of ben and can accelerate the introgression of ben into current rice cultivars.

Application of the New Gene Gm6 Against Rice GallMidge in Resistance Breeding Through PCR-BasedMarker Aided Selection

The research results of marker aided selection(MAS)for resistant varieties and lines against rice gall midge Orseolia oryzae Wood-Mason successfully in 1999 - 2002 were reported in the present paper. The molecular markers linked to the gene Gm6 against rice gall midge were used to select and breed the resistant varieties and lines. The RAPD marker OPM06 was used to verify the existence actually of gene Gm6 in ten developed varieties resistant to gall midge such as Duokang1, Duokang2, Kangwen2, Kangwen3, Kang-wen5, Duokangzaozhan, Kangwenqinzhan, which were derived from Daqiuqi. For resistance breeding through PCRbased marker aided selection(MAS), the polymorphisms in the resistant and susceptible parents were i-dentified by RG476/Alu I and RG476/Sca I respectively. The RAPD marker OPM06(1.4 kb)was used to i-dentify 15 new resistance lines from F3 lines of Fengyinzhan1/Daqiuqi in 1999. 21 and 7 resistance lines were selected from F4 and F6 lines of KWQZ/Gui99(restored line of hybrid rice)using RG476/Alu I in 2000-2001 respectively. The Gm6 gene was transferred into the restored line of hybrid rice. In 2001 - 2002, RG214/ Hha I and G214/Sca I were used for selecting 11 and 5 resistance lines from F3 lines of KWQZ/IR56 and AXZ/KWQZ successfully. The application of the resistance gene through PCR-based marker aided selection is a new and effective approach in resistance breeding.

Genome-wide identification,evolutionary selection,and genetic variation of DNA methylation-related genes in Brassica rapa and Brassica oleracea

DNA methylation plays an important role in plant growth and development,and in regulating the activity of transposable elements(TEs).Research on DNA methylation-related(DMR)genes has been reported in Arabidopsis,but little research on DMR genes has been reported in Brassica rapa and Brassica oleracea,the genomes of which exhibit significant differences in TE content.In this study,we identified 78 and 77 DMR genes in Brassica rapa and Brassica oleracea,respectively.Detailed analysis revealed that the numbers of DMR genes in different DMR pathways varied in B.rapa and B.oleracea.The evolutionary selection pressure of DMR genes in B.rapa and B.oleracea was compared,and the DMR genes showed differential evolution between these two species.The nucleotide diversity(π)and selective sweep(Tajima’s D)revealed footprints of selection in the B.rapa and B.oleracea populations.Transcriptome analysis showed that most DMR genes exhibited similar expression characteristics in B.rapa and B.oleracea.This study dissects the evolutionary differences and genetic variations of the DMR genes in B.rapa and B.oleracea,and will provide valuable resources for future research on the divergent evolution of DNA methylation between B.rapa and B.oleracea.

Genes for the high life： New genetic variants point to positive selection for high altitude hypoxia in Tibetans

People living on the high plateaus of the world have long fascinated biological anthropologists and geneticists because they live in ＂thin air＂ and epitomize an extreme of human biological adaptation.

Analysis of short fruiting branch gene and Marker-assisted selection with SNP linked to its trait in upland cotton

Background： With the rapid development of genomics, many functional genes have been targeted. Molecular marker assisted selection can accelerate the breeding process by linking selection to functional genes. Methods： In a study of upland cotton （Gossypium hirsutum L.）, the F2 segregated population was constructed by crossing X1570 （short branches） with Ekangmian 13 （long branches） to identify the short fruiting branch gene and marker assisted selection with SNP（Single Nucleotide Polymorphisms, SNP） linked to its trait. Result： The result demonstrated that linked SSR marker BNL3232 was screened by BSA（Bulked segregant analysis, BSA） method; one SNP locus was found, which was totally separated from the fruiting branches trait in upland cotton. Conclusion： It was verified that this SNP marker could be used for molecular assisted selection of cotton architecture

A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process.However,the high dimensionality of genetic data makes the classification process a challenging task.In this paper,we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm(simulated annealing(SA)),which will help select the most informative genes for breast cancer prediction.These optimal genes will then be used to train the classifier to improve its accuracy and efficiency.Three supervised machine-learning algorithms,namely,the support vector machine,the decision tree,and the random forest were used to create the classifier models that will help to predict breast cancer.Two different experiments were conducted using three datasets:Gene expression(GE),deoxyribonucleic acid(DNA)methylation,and a combination of the two.Six measures were used to evaluate the performance of the proposed algorithm,which include the following:Accuracy,precision,recall,specificity,area under the curve(AUC),and execution time.The effectiveness of the proposed classifiers was evaluated through comprehensive experiments.The results demonstrated that our approach outperformed the conventional classifiers as expected in terms of accuracy and execution time.High accuracy values of 99.77%,99.45%,and 99.45%have been achieved by SA-SVM for GE,DNA methylation,and the combined datasets,respectively.The execution time of the proposed approach was significantly reduced,in comparison to that of the traditional classifiers and the best execution time has been reached by SA-SVM,which was 0.02,0.03,and 0.02 on GE,DNA methylation,and the combined datasets respectively.In regard to precision and specificity,SA-RF obtained the best result of 100 on GE dataset.While SA-SVM attained the best recall result of 100 on GE dataset.

Genetic mechanism of body size variation in groupers:Insights from phylotranscriptomics

Animal body size variation is of particular interest in evolutionary biology,but the genetic basis remains largely unknown.Previous studies have shown the presence of two parallel evolutionary genetic clusters within the fish genus Epinephelus with evident divergence in body size,providing an excellent opportunity to investigate the genetic basis of body size variation in vertebrates.Herein,we performed phylotranscriptomic analysis and reconstructed the phylogeny of 13 epinephelids originating from the South China Sea.Two genetic clades with an estimated divergence time of approximately 15.4 million years ago were correlated with large and small body size,respectively.A total of 180 rapidly evolving genes and two positively selected genes were identified between the two groups.Functional enrichment analyses of these candidate genes revealed distinct enrichment categories between the two groups.These pathways and genes may play important roles in body size variation in groupers through complex regulatory networks.Based on our results,we speculate that the ancestors of the two divergent groups of groupers may have adapted to different environments through habitat selection,leading to genetic variations in metabolic patterns,organ development,and lifespan,resulting in body size divergence between the two locally adapted populations.These findings provide important insights into the genetic mechanisms underlying body size variation in groupers and species differentiation.

Allelic Polymorphism,Gene Duplication and Balancing Selection of MHC Class ⅡB Genes in the Omei Treefrog(Rhacophorus omeimontis)

The worldwide declines in amphibian populations have largely been caused by infectious fungi and bacteria. Given that vertebrate immunity against these extracellular pathogens is primarily functioned by the major histocompatibility complex（MHC） class Ⅱ molecules, the characterization and the evolution of amphibian MHC class Ⅱ genes have attracted increasing attention. The polymorphism of MHC class Ⅱ genes was found to be correlated with susceptibility to fungal pathogens in many amphibian species, suggesting the importance of studies on MHC class Ⅱ genes for amphibians. However, such studies on MHC class Ⅱ gene evolution have rarely been conducted on amphibians in China. In this study, we chose Omei treefrog（Rhacophorus omeimontis）, which lived moist environments easy for breeding bacteria, to study the polymorphism of its MHC class Ⅱ genes and the underlying evolutionary mechanisms. We amplified the entire MHC class ⅡB exon 2 sequence in the R. omeimontis using newly designed primers. We detected 102 putative alleles in 146 individuals. The number of alleles per individual ranged from one to seven, indicating that there are at least four loci containing MHC class ⅡB genes in R. omeimontis. The allelic polymorphism estimated from the 102 alleles in R. omeimontis was not high compared to that estimated in other anuran species. No significant gene recombination was detected in the 102 MHC class ⅡB exon 2 sequences. In contrast, both gene duplication and balancing selection greatly contributed to the variability in MHC class ⅡB exon 2 sequences of R. omeimontis. This study lays the groundwork for the future researches to comprehensively analyze the evolution of amphibian MHC genes and to assess the role of MHC gene polymorphisms in resistance against extracellular pathogens for amphibians in China.

Breeding of Anti-diarrhea Gene in Local and Exotic Pig Breeds in Guizhou Province

[Objectives]This study was conducted to breed special pig breeds resistant to diarrhea by using modern biotechnology.[Methods]From Guizhou local breeds,such as Nuogu pigs,Kele pig,Yorkshire pigs and Duroc pigs,190 samples were collected for the analysis of anti-diarrhea gene.[Results]The anti-diarrhea genotype frequency of Kele pigs was 70.00%,which was higher than that of Nuogu pigs(67.37%)and Yorkshire pigs(Yorkshire pigs and Duroc pigs)(50.59%).The favorable anti-diarrhea gene of all Nuogu pigs,Kele pigs,and Yorkshire pigs and Duroc pigs was G,with gene frequencies of 0.7355,0.8368 and 0.8500,respectively,and the frequencies of allele A were 0.2645,0.1632 and 0.1500,respectively.In the process of generation selection,combination selection of GG♂×GG♀,GG♂×GA♀,GA♂×GG♀and GA♂×GA♀was conducted,and GG individuals were selected while gradually phasing out GA and AA individuals.The anti-diarrhea genotypes of 98 pigs in the offspring were tested,and it was found that the frequency of genotype GG was greatly improved,and the frequencies in Nuogu pigs,Kele pigs,Yorkshire pigs and Duroc pigs were increased to 73.91%,81.82%,85.25%and 66.67%respectively,thus forming a special anti-diarrhea breed.[Conclusions]This study provides a basis for selecting excellent breeding pigs,establishing core populations and screening resistance genes in the core populations and their offspring.