Objective:Breast cancer is a common tumor and has a high mortality rate.Gene regulatory networks(GRNs)can genetically facilitate targeted therapies for this disease.Impact Statement:This study proposes a new method to...Objective:Breast cancer is a common tumor and has a high mortality rate.Gene regulatory networks(GRNs)can genetically facilitate targeted therapies for this disease.Impact Statement:This study proposes a new method to infer GRNs.This new method combining genetic modules and convolutional neural networks is presented to infer GRNs from the RNA sequencing data of breast cancer.Introduction:GRNs play an essential role in many disease treatments.Previous studies showed that GRNs will accelerate tumor therapy.However,most of the existing network inference methods are based on large-scale gene collections,which ignore the characteristics of different tumors.Methods:In this work,weighted gene coexpression network analysis was deployed to screen key genes and gene modules.The gene regulatory associations in gene modules were then transformed into 2-dimensional histogram types.A convolutional neural network was chosen as the main framework to fit the gene regulatory types and infer the GRN.Results:The method integrates genetic data analysis and deep learning perspectives to screen key genes and predict GRNs among key genes.The key genes screened were validated by multiple methods,and the inferred gene regulatory associations were widely validated in real datasets.Conclusion:The method can be used as an auxiliary tool with the potential to predict key genes and the GRNs of key genes.It has the potential to facilitate the therapeutic process and targeted therapy for breast cancer.展开更多
Many methods aim to use data,especially data about gene expression based on high throughput genomic methods,to identify complicated regulatory relationships between genes.The authors employ a simple but powerful tool,...Many methods aim to use data,especially data about gene expression based on high throughput genomic methods,to identify complicated regulatory relationships between genes.The authors employ a simple but powerful tool,called fuzzy cognitive maps(FCMs),to accurately reconstruct gene regulatory networks(GRNs).Many automated methods have been carried out for training FCMs from data.These methods focus on simulating the observed time sequence data,but neglect the optimisation of network structure.In fact,the FCM learning problem is multi-objective which contains network structure information,thus,the authors propose a new algorithm combining ensemble strategy and multi-objective evolutionary algorithm(MOEA),called EMOEAFCM-GRN,to reconstruct GRNs based on FCMs.In EMOEAFCM-GRN,the MOEA first learns a series of networks with different structures by analysing historical data simultaneously,which is helpful in finding the target network with distinct optimal local information.Then,the networks which receive small simulation error on the training set are selected from the Pareto front and an efficient ensemble strategy is provided to combine these selected networks to the final network.The experiments on the DREAM4 challenge and synthetic FCMs illustrate that EMOEAFCM-GRN is efficient and able to reconstruct GRNs accurately.展开更多
Biology provides many examples of complex systems whose properties allow organisms to develop in a highly reproducible,or robust,manner.One such system is the growth and development of flat leaves in Arabidopsis thali...Biology provides many examples of complex systems whose properties allow organisms to develop in a highly reproducible,or robust,manner.One such system is the growth and development of flat leaves in Arabidopsis thaliana.This mechanistically challenging process results from multiple inputs including gene interactions,cellular geometry,growth rates,and coordinated cell divisions.To better understand how this complex genetic and cellular information controls leaf growth,we developed a mathematical model of flat leaf production.This two-dimensional model describes the gene interactions in a vertex network of cells which grow and divide according to physical forces and genetic information.Interestingly,the model predicts the presence of an unknown additional factor required for the formation of biologically realistic gene expression domains and iterative cell division.This two-dimensional model will form the basis for future studies into robustness of adaxial-abaxial patterning.展开更多
针对YOLOv5(you only look once version five)模型在农作物害虫密集目标上的检测效果无法满足实际需求,以及训练过程中模型收敛速度较慢等问题,该研究提出了融入全局响应归一化(global response normalization,GRN)注意力机制的YOLOv5...针对YOLOv5(you only look once version five)模型在农作物害虫密集目标上的检测效果无法满足实际需求,以及训练过程中模型收敛速度较慢等问题,该研究提出了融入全局响应归一化(global response normalization,GRN)注意力机制的YOLOv5农作物害虫识别模型(YOLOv5-GRNS)。设计了融入GRN注意力机制的编码器(convolution three,C3)模块,提高对密集目标的识别精度;利用形状交并比(shape intersection over union,SIoU)损失函数提高模型收敛速度和识别精度;在公开数据集IP102(insect pests 102)的基础上,筛选出危害陕西省主要农作物的8种害虫类型,构建了新数据集IP8-CW(insect pests eight for corn and wheat)。改进后的模型在新IP8-CW和完整的IP102两种数据集上进行了全面验证。对于IP8-CW,全类别平均准确率(mean average precision,mAP)mAP@.5和mAP@.5:.95分别达到了72.3%和47.0%。该研究还对YOLOv5-GRNS模型进行了类激活图分析,不仅从识别精度,而且从可解释性的角度,验证了对农作物害虫、尤其是密集目标的优秀识别效果。此外,模型还兼具参数量少、运算量低的优势,具有良好的嵌入式设备应用前景。展开更多
Based on a model of network encoding and dynamics called the artificial genome, we propose a segmental duplication and divergence model for evolving artificial regulatory networks. We find that this class of networks ...Based on a model of network encoding and dynamics called the artificial genome, we propose a segmental duplication and divergence model for evolving artificial regulatory networks. We find that this class of networks share structural properties with natural transcriptional regulatory networks. Specifically, these networks can display scale-free and small-world structures. We also find that these networks have a higher probability to operate in the ordered regimen, and a lower probability to operate in the chaotic regimen. That is, the dynamics of these networks is similar to that of natural networks. The results show that the structure and dynamics inherent in natural networks may be in part due to their method of generation rather than being exclusively shaped by subsequent evolution under natural selection.展开更多
基金supported by National Natural Science Foundation of China(62366048)Major Project of Gansu Province Joint Research Fund(23JRRA1537).
文摘Objective:Breast cancer is a common tumor and has a high mortality rate.Gene regulatory networks(GRNs)can genetically facilitate targeted therapies for this disease.Impact Statement:This study proposes a new method to infer GRNs.This new method combining genetic modules and convolutional neural networks is presented to infer GRNs from the RNA sequencing data of breast cancer.Introduction:GRNs play an essential role in many disease treatments.Previous studies showed that GRNs will accelerate tumor therapy.However,most of the existing network inference methods are based on large-scale gene collections,which ignore the characteristics of different tumors.Methods:In this work,weighted gene coexpression network analysis was deployed to screen key genes and gene modules.The gene regulatory associations in gene modules were then transformed into 2-dimensional histogram types.A convolutional neural network was chosen as the main framework to fit the gene regulatory types and infer the GRN.Results:The method integrates genetic data analysis and deep learning perspectives to screen key genes and predict GRNs among key genes.The key genes screened were validated by multiple methods,and the inferred gene regulatory associations were widely validated in real datasets.Conclusion:The method can be used as an auxiliary tool with the potential to predict key genes and the GRNs of key genes.It has the potential to facilitate the therapeutic process and targeted therapy for breast cancer.
文摘Many methods aim to use data,especially data about gene expression based on high throughput genomic methods,to identify complicated regulatory relationships between genes.The authors employ a simple but powerful tool,called fuzzy cognitive maps(FCMs),to accurately reconstruct gene regulatory networks(GRNs).Many automated methods have been carried out for training FCMs from data.These methods focus on simulating the observed time sequence data,but neglect the optimisation of network structure.In fact,the FCM learning problem is multi-objective which contains network structure information,thus,the authors propose a new algorithm combining ensemble strategy and multi-objective evolutionary algorithm(MOEA),called EMOEAFCM-GRN,to reconstruct GRNs based on FCMs.In EMOEAFCM-GRN,the MOEA first learns a series of networks with different structures by analysing historical data simultaneously,which is helpful in finding the target network with distinct optimal local information.Then,the networks which receive small simulation error on the training set are selected from the Pareto front and an efficient ensemble strategy is provided to combine these selected networks to the final network.The experiments on the DREAM4 challenge and synthetic FCMs illustrate that EMOEAFCM-GRN is efficient and able to reconstruct GRNs accurately.
基金supported by the NSF#2039489 to A.Y.H and the NSF#1813071 to C.-S.C.
文摘Biology provides many examples of complex systems whose properties allow organisms to develop in a highly reproducible,or robust,manner.One such system is the growth and development of flat leaves in Arabidopsis thaliana.This mechanistically challenging process results from multiple inputs including gene interactions,cellular geometry,growth rates,and coordinated cell divisions.To better understand how this complex genetic and cellular information controls leaf growth,we developed a mathematical model of flat leaf production.This two-dimensional model describes the gene interactions in a vertex network of cells which grow and divide according to physical forces and genetic information.Interestingly,the model predicts the presence of an unknown additional factor required for the formation of biologically realistic gene expression domains and iterative cell division.This two-dimensional model will form the basis for future studies into robustness of adaxial-abaxial patterning.
文摘针对YOLOv5(you only look once version five)模型在农作物害虫密集目标上的检测效果无法满足实际需求,以及训练过程中模型收敛速度较慢等问题,该研究提出了融入全局响应归一化(global response normalization,GRN)注意力机制的YOLOv5农作物害虫识别模型(YOLOv5-GRNS)。设计了融入GRN注意力机制的编码器(convolution three,C3)模块,提高对密集目标的识别精度;利用形状交并比(shape intersection over union,SIoU)损失函数提高模型收敛速度和识别精度;在公开数据集IP102(insect pests 102)的基础上,筛选出危害陕西省主要农作物的8种害虫类型,构建了新数据集IP8-CW(insect pests eight for corn and wheat)。改进后的模型在新IP8-CW和完整的IP102两种数据集上进行了全面验证。对于IP8-CW,全类别平均准确率(mean average precision,mAP)mAP@.5和mAP@.5:.95分别达到了72.3%和47.0%。该研究还对YOLOv5-GRNS模型进行了类激活图分析,不仅从识别精度,而且从可解释性的角度,验证了对农作物害虫、尤其是密集目标的优秀识别效果。此外,模型还兼具参数量少、运算量低的优势,具有良好的嵌入式设备应用前景。
文摘Based on a model of network encoding and dynamics called the artificial genome, we propose a segmental duplication and divergence model for evolving artificial regulatory networks. We find that this class of networks share structural properties with natural transcriptional regulatory networks. Specifically, these networks can display scale-free and small-world structures. We also find that these networks have a higher probability to operate in the ordered regimen, and a lower probability to operate in the chaotic regimen. That is, the dynamics of these networks is similar to that of natural networks. The results show that the structure and dynamics inherent in natural networks may be in part due to their method of generation rather than being exclusively shaped by subsequent evolution under natural selection.
