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,microarr...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.展开更多
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 flourishin...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.展开更多
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 ...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.展开更多
Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists...Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.展开更多
The genus Thuja is ideal for investigating the genetic basis of the East Asia-North America disjunction.The biogeographical background of the genus is debatable and an adaptive strategy is lacking.Through the analysis...The genus Thuja is ideal for investigating the genetic basis of the East Asia-North America disjunction.The biogeographical background of the genus is debatable and an adaptive strategy is lacking.Through the analysis and mining of comparative transcriptomes,species differentiation and positively selected genes(PSGs)were identified to provide information for understanding the environmental adaptation strategies of the genus Thuja.De novo assembly yielded 44,397-74,252 unigenes of the five Thuja species with contig N50length ranging from 1,559 to 1,724 bp.Annotations revealed a similar distribution of functional categories among them.Based on the phylogenetic trees constructed using the transcriptome data,T.sutchuenensis was divided first,followed by T.plicata and T.occidentalis.The final differentiation of T.koraiensis and T.standishii formed a clade.Enrichment analysis indicated that the PSGs of the North American Thuja species were involved in plant hormone signal transduction and carbon fixation of photosynthetic organisms pathways.The PSGs of East Asian Thuja were related to phenolic,alkaloid,and terpenoid synthesis,important stress-resistant genes and could increase plant resistance to external environmental stresses.This study discovered numerous aroma synthetic-related PSGs including terpene synthase(TPS)genes and lipid phosphate phosphatase 2(LPP2),associated with the synthetic aroma of T.sutchuenensis.Physiological indicators,such as the contents of soluble sugars,total chlorophyll,total phenolics,and total flavonoids were determined,which are consistent with the PSGs enrichment pathways associated with adaptive strategies in the five Thuja species.The results of this study provide an important basis for future studies on conservation genetics.展开更多
Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide.Several studies have indicated that rectal cancer is significantly different from colon cancer interms of treatment, prognosis, and metasta...Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide.Several studies have indicated that rectal cancer is significantly different from colon cancer interms of treatment, prognosis, and metastasis. Recently, the differential mRNA expression of coloncancer and rectal cancer has received a great deal of attention. The current study aimed to identifysignificant differences between colon cancer and rectal cancer based on RNA sequencing (RNA-seq)data via support vector machines (SVM). Here, 393 CRC samples from the The Cancer GenomeAtlas (TCGA) database were investigated, including 298 patients with colon cancer and 95 withrectal cancer. Following the random forest (RF) analysis of the mRNA expression data, 96 genessuch as HOXB13, PR4C, and BCLAFI were identified and utilized to build the SVM classificationmodel with the Leave-One-Out Cross-validation (LOOCV) algorithm. In the training (n= 196)and the validation cohorts (n=197), the accuracy (82. 1 % and 82.2 %, respectively) and the AUC(0.87 and 0.91, respectively) indicated that the established optimal SVM classification modeldistinguished colon cancer from rectal cancer reasonably. However, additional experiments arerequired to validate the predicted gene expression levels and functions.展开更多
With the long-term support by the National Natural Science Foundation of China,Ministry of Agriculture,and Science and Technology Department of Zhejiang Province,the research team led by Prof.Zhang Mingfang(张明方)at ...With the long-term support by the National Natural Science Foundation of China,Ministry of Agriculture,and Science and Technology Department of Zhejiang Province,the research team led by Prof.Zhang Mingfang(张明方)at Zhejiang University,assembled an allopolyploid B.juncea genome and uncovered differential homoeolog gene expression influencing selection,which was published in Nature展开更多
Acute graft-versus-host disease (aGVHD) is a serious systemic complication of allogeneic hematopoietic stemcell transplantation (HSCT) causing considerable morbidity and mortality. Acute GVHD occurs when alloreactived...Acute graft-versus-host disease (aGVHD) is a serious systemic complication of allogeneic hematopoietic stemcell transplantation (HSCT) causing considerable morbidity and mortality. Acute GVHD occurs when alloreactivedonor-derived T cells recognize host-recipient antigens as foreign. These trigger a complex multiphase process thatultimately results in apoptotic injury in target organs. The early events leading to GVHD seem to occur very soon,presumably within hours from the graft infusion. Therefore, when the first signs of aGVHD clinically manifest, thedisease has been ongoing for several days at the cellular level, and the inflammatory cytokine cascade is fully activated.So, it comes as no surprise that progress in treatment based on clinical diagnosis of aGVHD has been limited in the past30 years. It is likely that a pre-emptive strategy using systemic high-dose corticosteroids as early as possible couldimprove the outcome of aGVHD. Due to the deleterious effects of such treatment particularly in terms of infection riskposed by systemic steroid administration in a population that is already immune-suppressed, it is critical to identifybiomarker signatures for approaching this very complex task. Some research groups have begun addressing this issuethrough molecular and proteomic analyses, combining these approaches with computational intelligence techniques,with the specific aim of facilitating the identification of diagnostic biomarkers in aGVHD. In this review, we focus on theaGVHD scenario and on the more recent state-of-the-art.We also attempt to give an overview of the classical and noveltechniques proposed as medical decision support system for the diagnosis of GVHD.展开更多
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China (32273136,31872572)Agriculture Research System of China (ARS-47)+1 种基金Science and Technology Planning Project of Guangdong Province (2023B1212060023)Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (SML2023SP201)。
文摘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.
文摘Acute leukemia is an aggressive disease that has high mortality rates worldwide.The error rate can be as high as 40%when classifying acute leukemia into its subtypes.So,there is an urgent need to support hematologists during the classification process.More than two decades ago,researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case.The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data.Ensemble machine learning is an effective method that combines individual classifiers to classify new samples.Ensemble classifiers are recognized as powerful algorithms with numerous advantages over traditional classifiers.Over the past few decades,researchers have focused a great deal of attention on ensemble classifiers in a wide variety of fields,including but not limited to disease diagnosis,finance,bioinformatics,healthcare,manufacturing,and geography.This paper reviews the recent ensemble classifier approaches utilized for acute leukemia gene expression data classification.Moreover,a framework for classifying acute leukemia gene expression data is proposed.The pairwise correlation gene selection method and the Rotation Forest of Bayesian Networks are both used in this framework.Experimental outcomes show that the classification accuracy achieved by the acute leukemia ensemble classifiers constructed according to the suggested framework is good compared to the classification accuracy achieved in other studies.
基金supported by the National Natural Science Foundation of China(31870664)the 948 Program of National Forestry and Grassland Administration(2013-4-47)the National Key Research and Development Program of China(2022YFD2200103)。
文摘The genus Thuja is ideal for investigating the genetic basis of the East Asia-North America disjunction.The biogeographical background of the genus is debatable and an adaptive strategy is lacking.Through the analysis and mining of comparative transcriptomes,species differentiation and positively selected genes(PSGs)were identified to provide information for understanding the environmental adaptation strategies of the genus Thuja.De novo assembly yielded 44,397-74,252 unigenes of the five Thuja species with contig N50length ranging from 1,559 to 1,724 bp.Annotations revealed a similar distribution of functional categories among them.Based on the phylogenetic trees constructed using the transcriptome data,T.sutchuenensis was divided first,followed by T.plicata and T.occidentalis.The final differentiation of T.koraiensis and T.standishii formed a clade.Enrichment analysis indicated that the PSGs of the North American Thuja species were involved in plant hormone signal transduction and carbon fixation of photosynthetic organisms pathways.The PSGs of East Asian Thuja were related to phenolic,alkaloid,and terpenoid synthesis,important stress-resistant genes and could increase plant resistance to external environmental stresses.This study discovered numerous aroma synthetic-related PSGs including terpene synthase(TPS)genes and lipid phosphate phosphatase 2(LPP2),associated with the synthetic aroma of T.sutchuenensis.Physiological indicators,such as the contents of soluble sugars,total chlorophyll,total phenolics,and total flavonoids were determined,which are consistent with the PSGs enrichment pathways associated with adaptive strategies in the five Thuja species.The results of this study provide an important basis for future studies on conservation genetics.
基金supported by the Six Talent Peaks Project in Jiangsu Province(No.2014-wsw-017)Beijing Medical Award Foundation(No.YJHYXKYJJ-432)+2 种基金Foundation of Social Development Project of the Science and Technology Department of Jiangsu Province(No.BE2015719)Social Development Key Research and Development Plan of Jiangsu Province(No.BE2017694)The Foundation of Nanjing Medical University(No.2017NJMUZD140).
文摘Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide.Several studies have indicated that rectal cancer is significantly different from colon cancer interms of treatment, prognosis, and metastasis. Recently, the differential mRNA expression of coloncancer and rectal cancer has received a great deal of attention. The current study aimed to identifysignificant differences between colon cancer and rectal cancer based on RNA sequencing (RNA-seq)data via support vector machines (SVM). Here, 393 CRC samples from the The Cancer GenomeAtlas (TCGA) database were investigated, including 298 patients with colon cancer and 95 withrectal cancer. Following the random forest (RF) analysis of the mRNA expression data, 96 genessuch as HOXB13, PR4C, and BCLAFI were identified and utilized to build the SVM classificationmodel with the Leave-One-Out Cross-validation (LOOCV) algorithm. In the training (n= 196)and the validation cohorts (n=197), the accuracy (82. 1 % and 82.2 %, respectively) and the AUC(0.87 and 0.91, respectively) indicated that the established optimal SVM classification modeldistinguished colon cancer from rectal cancer reasonably. However, additional experiments arerequired to validate the predicted gene expression levels and functions.
文摘With the long-term support by the National Natural Science Foundation of China,Ministry of Agriculture,and Science and Technology Department of Zhejiang Province,the research team led by Prof.Zhang Mingfang(张明方)at Zhejiang University,assembled an allopolyploid B.juncea genome and uncovered differential homoeolog gene expression influencing selection,which was published in Nature
文摘Acute graft-versus-host disease (aGVHD) is a serious systemic complication of allogeneic hematopoietic stemcell transplantation (HSCT) causing considerable morbidity and mortality. Acute GVHD occurs when alloreactivedonor-derived T cells recognize host-recipient antigens as foreign. These trigger a complex multiphase process thatultimately results in apoptotic injury in target organs. The early events leading to GVHD seem to occur very soon,presumably within hours from the graft infusion. Therefore, when the first signs of aGVHD clinically manifest, thedisease has been ongoing for several days at the cellular level, and the inflammatory cytokine cascade is fully activated.So, it comes as no surprise that progress in treatment based on clinical diagnosis of aGVHD has been limited in the past30 years. It is likely that a pre-emptive strategy using systemic high-dose corticosteroids as early as possible couldimprove the outcome of aGVHD. Due to the deleterious effects of such treatment particularly in terms of infection riskposed by systemic steroid administration in a population that is already immune-suppressed, it is critical to identifybiomarker signatures for approaching this very complex task. Some research groups have begun addressing this issuethrough molecular and proteomic analyses, combining these approaches with computational intelligence techniques,with the specific aim of facilitating the identification of diagnostic biomarkers in aGVHD. In this review, we focus on theaGVHD scenario and on the more recent state-of-the-art.We also attempt to give an overview of the classical and noveltechniques proposed as medical decision support system for the diagnosis of GVHD.
