Background Biologically annotated neural networks(BANNs)are feedforward Bayesian neural network models that utilize partially connected architectures based on SN P-set annotations.As an interpretable neural network,BA...Background Biologically annotated neural networks(BANNs)are feedforward Bayesian neural network models that utilize partially connected architectures based on SN P-set annotations.As an interpretable neural network,BANNs model SNP and SNP-set effects in their input and hidden layers,respectively.Furthermore,the weights and connections of the network are regarded as random variables with prior distributions reflecting the manifestation of genetic effects at various genomic scales.However,its application in genomic prediction has yet to be explored.Results This study extended the BANNs framework to the area of genomic selection and explored the optimal SN P-set partitioning strategies by using dairy cattle datasets.The SN P-sets were partitioned based on two strategiesgene annotations and 100 kb windows,denoted as BANN_gene and BANN_100kb,respectively.The BANNs model was compared with GBLU P,random forest(RF),BayesB and BayesCπthrough five replicates of five-fold cross-validation using genotypic and phenotypic data on milk production traits,type traits,and one health trait of 6,558,6,210and 5,962 Chinese Holsteins,respectively.Results showed that the BANNs framework achieves higher genomic prediction accuracy compared to GBLU P,RF and Bayesian methods.Specifically,the BANN_100kb demonstrated superior accuracy and the BANN_gene exhibited generally suboptimal accuracy compared to GBLUP,RF,BayesB and BayesCrr across all traits.The average accuracy improvements of BANN_100kb over GBLU P,RF,BayesB and BayesCrr were 4.86%,3.95%,3.84%and 1.92%,and the accuracy of BANN_gene was improved by3.75%,2.86%,2.73%and 0.85%compared to GBLUP,RF,BayesB and BayesCπ,respectively across all seven traits.Meanwhile,both BANN_100kb and BANN_gene yielded lower overall mean square error values than GBLUP,RF and Bayesian methods.Conclusion Our findings demonstrated that the BANNs framework performed better than traditional genomic prediction methods in our tested scenarios,and might serve as a promising alternative approach for genomic prediction in dairy cattle.展开更多
Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.H...Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.展开更多
In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under...In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current （AC） stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.展开更多
Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hami...Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.展开更多
Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and met...Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy.From this perspective,we first review the state of high-throughput biological data acquisition(i.e.multiomics data)and analysis(i.e.computational tools)and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net)that is based on these current high-throughput methods.The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes,omics data acquisition,analysis of network information,and integration with validated database knowledge.Thus,MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks.We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.展开更多
Signal transduction is an important and basic mechanism to cell life activities.The stochastic state transition of receptor induces the release of signaling molecular,which triggers the state transition of other recep...Signal transduction is an important and basic mechanism to cell life activities.The stochastic state transition of receptor induces the release of signaling molecular,which triggers the state transition of other receptors.It constructs a nonlinear sigaling network,and leads to robust switchlike properties which are critical to biological function.Network architectures and state transitions of receptor affect the performance of this biological network.In this work,we perform a study of nonlinear signaling on biological polymorphic network by analyzing network dynamics of the Ca^(2+)-induced Ca^(2+)release(CICR)mechanism,where fast and slow processes are involved and the receptor has four conformational states.Three types of networks,Erdos–R´enyi(ER)network,Watts–Strogatz(WS)network,and BaraB´asi–Albert(BA)network,are considered with different parameters.The dynamics of the biological networks exhibit different patterns at different time scales.At short time scale,the second open state is essential to reproduce the quasi-bistable regime,which emerges at a critical strength of connection for all three states involved in the fast processes and disappears at another critical point.The pattern at short time scale is not sensitive to the network architecture.At long time scale,only monostable regime is observed,and difference of network architectures affects the results more seriously.Our finding identifies features of nonlinear signaling networks with multistate that may underlie their biological function.展开更多
The incidence of prostate cancer is rising in the Asia-Pacific region as well as other countries. Androgen-ablation therapy is clinically useful in the androgen-dependent phenotype however, many patients progress to h...The incidence of prostate cancer is rising in the Asia-Pacific region as well as other countries. Androgen-ablation therapy is clinically useful in the androgen-dependent phenotype however, many patients progress to hormone refractory prostate cancer that is difficult to treat and needs newer interventions that are more effective. The objective of this study was to determine functionally-relevant biological networks, to appreciate the potential crosstalk between signaling members, and to identify biomarker signatures in prostate cancer. We used microarray analyses to identify key genes that were upregulated or down regulated at least five-fold in human prostate cancer and constructed canonical interaction networks that are important in prostate cancer through metabolomics analyses. Our prostate cancer network architecture revealed several key biomarkers including ERK1/2, JNK, p38, MEK, PI3 K, NFκB, AP-1, 14-3-3, VEGF, PDGF, Rb, WNT8 A, WNT10 A, CD44, ESR2, FSH and LH. Furthermore, the top ten transcription factors identified by TFBS-association signature analysis in the regulatory elements of co-regulated biomarkers were delineated, which may crosstalk with upstream or downstream genes elicited in our network architecture. Taken together, our results demonstrate that the regulatory interaction networks in prostate cancer provide a universal view of crosstalk between important biomarkers, i.e., key players in the pathogenesis of this disease. This will facilitate more rapid screening of functional biomarkers in early/intermediate drug discovery.展开更多
The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they ...The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.展开更多
The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established...The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.展开更多
Objective:Cell co-culture technology has been widely used to analyze the effects of drugs on cell proliferation and the expression of some proteins in cells,especially in the field of traditional Chinese medicine(TCM)...Objective:Cell co-culture technology has been widely used to analyze the effects of drugs on cell proliferation and the expression of some proteins in cells,especially in the field of traditional Chinese medicine(TCM);however,the interactions between cells and the transmission of TCM effects between cells have not been adequately studied.Materials and Methods:Using data on gene transcription regulation,biological response,signal channel,and cell-specific expression protein,we built a network for cell types based on entity grammar.Through the correspondence and location information of signal molecules and receptors,type-specific networks of single cells were connected and a multicellular network of smooth muscle cells,neurons,and vascular endothelial cells was constructed.The mechanism of action of nimodipine was analyzed based on the multicellular communication network and its simulation capability was evaluated.Results:The outputs generated by the model developed in this study showed that nimodipine inhibited smooth muscle contraction,due to the overload of Ca^(2+)and the toxicity of excitatory amino acids,and protected neurons and vascular endothelial cells by supporting cell proliferation and inhibiting cell apoptosis.These results were consistent with the known mechanism of nimodipine action,thus confirming that the multicellular network can be used to study the transmission of drug effects among cells.Conclusions:This study lays a foundation for the analysis of the transmission of drug effects in multi-cells,tissues,organs,and other spatial scales through multicellular co-culture experiments,based on a multicellular communication network.In addition,it provides a biological network model for the analysis of TCM action mechanisms.展开更多
Background： Module detection is widely used to analyze and visualize biological networks. A number of methods and tools have been developed to achieve it. Meanwhile, bipartite module detection is also very useful for...Background： Module detection is widely used to analyze and visualize biological networks. A number of methods and tools have been developed to achieve it. Meanwhile, bipartite module detection is also very useful for mining and analyzing bipartite biological networks and a few methods have been developed for it. However, there is few user- friendly toolkit for this task. Methods： To this end, we develop an online web toolkit BMTK, which implements seven existing methods. Results： BMTK provides a uniform operation platform and visualization function, standardizes input and output format, and improves algorithmic structure to enhance computing speed. We also apply this toolkit onto a drug-target bipartite network to demonstrate its effectiveness. Conclusions： BMTK will be a powerful tool for detecting bipartite modules in diverse bipartite biological networks. Availability： The web application is freely accessible at http：//www.zhanglabtools.net/BMTK.展开更多
Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies....Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.展开更多
One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques t...One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.展开更多
Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in f...Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians.Because clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be avoided.Previous research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic retinopathy.Therefore,we aimed to develop a novel integrated approach to increase the accuracy of detection.This approach utilized both convolutional neural networks and signal processing techniques.In this proposed method,the biological electro retinogram(ERG)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify DR.In the BSN system,electrodes were used to record ERGsignal,which was preprocessed to be noise-free.Processing was performed in the frequency domain by the application of fast Fourier transform(FFT)and mel frequency cepstral coefficients(MFCCs)were extracted.Artificial neural network(ANN)classifier was used to classify the signals of eyes with DR and normal eye.Additionally,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based analysis.The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR images.Furthermore,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated PDR.The accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,respectively.Both the accuracy rate and sensitivity rate of theDCNNclassifierwas 96.5%for the images acquired from various hospitals as well as databases.A comparison between the accuracy rates of BSN andDCNN approaches showed thatDCNNwith fundus images decreased the error rate to 4%.展开更多
With the rapid advances in technology and life science,biological security is now at a defining moment.The mandate of the 2022 Biological and Toxin Weapons Convention 9th Review Conference emphasised the urgent need f...With the rapid advances in technology and life science,biological security is now at a defining moment.The mandate of the 2022 Biological and Toxin Weapons Convention 9th Review Conference emphasised the urgent need for new tools to strengthen the Convention.In this paper,we review the development and efforts of the International Nuclear Security Education Network(INSEN)to provide examples of best practice for implementation of the newly founded International Biological Security Education Network(IBSEN).Learning from the lessons of the INSEN,the sustainability of the network through continuous engagement of its members is essential for the further development of global biosecurity education.展开更多
With the rapid accumulation of high-throughput metagenomic sequencing data,it is possible to infer microbial species relations in a microbial community systematically.In recent years,some approaches have been proposed...With the rapid accumulation of high-throughput metagenomic sequencing data,it is possible to infer microbial species relations in a microbial community systematically.In recent years,some approaches have been proposed for identifying microbial interaction network.These methods often focus on one dataset without considering the advantage of data integration.In this study,we propose to use a similarity network fusion(SNF)method to infer microbial relations.The SNF efficiently integrates the similarities of species derived from different datasets by a cross-network diffusion process.We also introduce consensus k-nearest neighborhood(Ck-NN)method instead of k-NN in the original SNF(we call the approach CSNF).The final network represents the augmented species relationships with aggregated evidence from various datasets,taking advantage of complementarity in the data.We apply the method on genus profiles derived from three microbiome datasets and we find that CSNF can discover the modular structure of microbial interaction network which cannot be identified by analyzing a single dataset.展开更多
Revealing how a biological network is organized to realize its function is one of the main topics in systems biology. Tile functional backbone network, defined as the primary structure of the biological network, is of...Revealing how a biological network is organized to realize its function is one of the main topics in systems biology. Tile functional backbone network, defined as the primary structure of the biological network, is of great importance in maintaining the main function of the biological network. We propose a new algorithm, the tinker algorithm, to determine this core structure and apply it in the cell-cycle system. With this algorithm, the backbone network of the cell-cycle network can be determined accurately and efficiently in various models such as the Boolean model, stochastic model, and ordinary differential equation model. Results show that our algorithm is more efficient than that used in the previous research. We hope this method can be put into practical use in relevant future studies.展开更多
Biological systems can be modeled and described by biological networks.Biological networks are typical complex networks with widely real-world applications.Many problems arising in biological systems can be boiled dow...Biological systems can be modeled and described by biological networks.Biological networks are typical complex networks with widely real-world applications.Many problems arising in biological systems can be boiled down to the identification of important nodes.For example,biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants.To facilitate the identification of important nodes in biological systems,one needs to know network structures or behavioral data of nodes(such as gene expression data).If network topology was known,various centrality measures can be developed to solve the problem;while if only behavioral data of nodes were given,some sophisticated statistical methods can be employed.This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects,that is,1)in general complex networks based on complex networks theory and epidemic dynamic models;2)in biological networks based on network motifs;and 3)in plants based on RNA-seq data.The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes,such mapping is not necessarily with explicit form.The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework.This paper also proposed some challenges and future works on the related topics.The associated investigations have potential real-world applications in the control of biological systems,network medicine and new variety cultivation of crops.展开更多
With the popularization of microarray experi-ments in biomedical laboratories,how to make context-specific knowledge discovery from expression data becomes a hot topic.While the static“reference networks”for key mod...With the popularization of microarray experi-ments in biomedical laboratories,how to make context-specific knowledge discovery from expression data becomes a hot topic.While the static“reference networks”for key model organisms are nearly at hand,the endeavors to recover context-specific network modules are still at the beginning.Currently,this is achieved through filtering existing edges of the ensemble reference network or constructing gene networks ab initio.In this paper,we briefly review recent progress in the field and point out some research directions awaiting improved work,includ-ing expression-data-guided revision of reference networks.展开更多
The core principle of diagnosis and treatment in traditional Chinese medicine(TCM) is the identification of different syndromes.Cold and hot syndromes are important elements in TCM theory.Identifying the biological ba...The core principle of diagnosis and treatment in traditional Chinese medicine(TCM) is the identification of different syndromes.Cold and hot syndromes are important elements in TCM theory.Identifying the biological basis of cold and hot syndromes in TCM will help elucidate TCM theories scientifically,thus promoting precise treatment in TCM.Although the biological basis of cold/hot syndromes in TCM remains poorly understood,growing evide nce suggests that immunometab olic interactions play an important role in balancing cold and hot syndromes.Immuno metabolism involve s complex interactions between the immune and metabolic systems.Multilevel mechanisms of interaction between the immune and metabolic systems may underlie many inflammatory diseases and offer substantial therapeutic promise.Therefore,dissecting the relationship between immunometabolism and the biological network of cold/hot syndromes has become a priority.This article reviews the progress of cold/hot syndrome research from the perspective of immunometabolic homeostasis,thus further clarifying cold/hot syndromes in TCM.展开更多
基金supported by the National Key Research and Development Program of China(2022YFD1302204)the earmarked fund CARS36+1 种基金Ningxia Key Research and Development Program of China(2023BCF010042019NYYZ09)。
文摘Background Biologically annotated neural networks(BANNs)are feedforward Bayesian neural network models that utilize partially connected architectures based on SN P-set annotations.As an interpretable neural network,BANNs model SNP and SNP-set effects in their input and hidden layers,respectively.Furthermore,the weights and connections of the network are regarded as random variables with prior distributions reflecting the manifestation of genetic effects at various genomic scales.However,its application in genomic prediction has yet to be explored.Results This study extended the BANNs framework to the area of genomic selection and explored the optimal SN P-set partitioning strategies by using dairy cattle datasets.The SN P-sets were partitioned based on two strategiesgene annotations and 100 kb windows,denoted as BANN_gene and BANN_100kb,respectively.The BANNs model was compared with GBLU P,random forest(RF),BayesB and BayesCπthrough five replicates of five-fold cross-validation using genotypic and phenotypic data on milk production traits,type traits,and one health trait of 6,558,6,210and 5,962 Chinese Holsteins,respectively.Results showed that the BANNs framework achieves higher genomic prediction accuracy compared to GBLU P,RF and Bayesian methods.Specifically,the BANN_100kb demonstrated superior accuracy and the BANN_gene exhibited generally suboptimal accuracy compared to GBLUP,RF,BayesB and BayesCrr across all traits.The average accuracy improvements of BANN_100kb over GBLU P,RF,BayesB and BayesCrr were 4.86%,3.95%,3.84%and 1.92%,and the accuracy of BANN_gene was improved by3.75%,2.86%,2.73%and 0.85%compared to GBLUP,RF,BayesB and BayesCπ,respectively across all seven traits.Meanwhile,both BANN_100kb and BANN_gene yielded lower overall mean square error values than GBLUP,RF and Bayesian methods.Conclusion Our findings demonstrated that the BANNs framework performed better than traditional genomic prediction methods in our tested scenarios,and might serve as a promising alternative approach for genomic prediction in dairy cattle.
基金supported by the Natural Science Foundation of Jiangsu Province under Grant No. BK2012742
文摘Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.
基金The project supported by National Natural Science Foundation of China under Grant Nos. 70571017 and 10547004 and the Key Projects of National Natural Science Foundation of China under Grant No. 70431002
文摘In this paper, we propose a new model of weighted small-world biological neural networks based on biophysical Hodgkin-Huxley neurons with side-restrain mechanism. Then we study excitement properties of the model under alternating current （AC） stimulation. The study shows that the excitement properties in the networks are preferably consistent with the behavior properties of a brain nervous system under different AC stimuli, such as refractory period and the brain neural excitement response induced by different intensities of noise and coupling. The results of the study have reference worthiness for the brain nerve electrophysiology and epistemological science.
文摘Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.
基金supported by the National Natural Science Foundation of China(81890994,31871343)National Key Research and Development Program of China(2017YFA0505503,2018YFB0704304,2018YFA0801402)+1 种基金the WBE Liver Fibrosis Foundation(CFHPC 2020021)the Beijing Dongcheng District outstanding talent funding project and the Beijing Undergraduate Training Programs for Innovation and Entrepreneurship(202010023046)。
文摘Genetic,epigenetic,and metabolic alterations are all hallmarks of cancer.However,the epigenome and metabolome are both highly complex and dynamic biological networks in vivo.The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy.From this perspective,we first review the state of high-throughput biological data acquisition(i.e.multiomics data)and analysis(i.e.computational tools)and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net)that is based on these current high-throughput methods.The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes,omics data acquisition,analysis of network information,and integration with validated database knowledge.Thus,MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks.We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.
基金Project supported by the National Natural Science Foundation of China(Grant No.11675228)China Postdoctoral Science Foundation(Grant No.2015M572662XB).
文摘Signal transduction is an important and basic mechanism to cell life activities.The stochastic state transition of receptor induces the release of signaling molecular,which triggers the state transition of other receptors.It constructs a nonlinear sigaling network,and leads to robust switchlike properties which are critical to biological function.Network architectures and state transitions of receptor affect the performance of this biological network.In this work,we perform a study of nonlinear signaling on biological polymorphic network by analyzing network dynamics of the Ca^(2+)-induced Ca^(2+)release(CICR)mechanism,where fast and slow processes are involved and the receptor has four conformational states.Three types of networks,Erdos–R´enyi(ER)network,Watts–Strogatz(WS)network,and BaraB´asi–Albert(BA)network,are considered with different parameters.The dynamics of the biological networks exhibit different patterns at different time scales.At short time scale,the second open state is essential to reproduce the quasi-bistable regime,which emerges at a critical strength of connection for all three states involved in the fast processes and disappears at another critical point.The pattern at short time scale is not sensitive to the network architecture.At long time scale,only monostable regime is observed,and difference of network architectures affects the results more seriously.Our finding identifies features of nonlinear signaling networks with multistate that may underlie their biological function.
基金RO1 CA 094828 to Prof. Ah-Ng Tony Kong and in part by R21 CA133675 to Dr.Li Cai both from the National Institutes of Health(NIH)
文摘The incidence of prostate cancer is rising in the Asia-Pacific region as well as other countries. Androgen-ablation therapy is clinically useful in the androgen-dependent phenotype however, many patients progress to hormone refractory prostate cancer that is difficult to treat and needs newer interventions that are more effective. The objective of this study was to determine functionally-relevant biological networks, to appreciate the potential crosstalk between signaling members, and to identify biomarker signatures in prostate cancer. We used microarray analyses to identify key genes that were upregulated or down regulated at least five-fold in human prostate cancer and constructed canonical interaction networks that are important in prostate cancer through metabolomics analyses. Our prostate cancer network architecture revealed several key biomarkers including ERK1/2, JNK, p38, MEK, PI3 K, NFκB, AP-1, 14-3-3, VEGF, PDGF, Rb, WNT8 A, WNT10 A, CD44, ESR2, FSH and LH. Furthermore, the top ten transcription factors identified by TFBS-association signature analysis in the regulatory elements of co-regulated biomarkers were delineated, which may crosstalk with upstream or downstream genes elicited in our network architecture. Taken together, our results demonstrate that the regulatory interaction networks in prostate cancer provide a universal view of crosstalk between important biomarkers, i.e., key players in the pathogenesis of this disease. This will facilitate more rapid screening of functional biomarkers in early/intermediate drug discovery.
基金supported in part by the Rosetrees Trust(#CF-2023-I-2_113)by the Israel Ministry of Innovation,Science,and Technology(#7393)(to ES).
文摘The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.
基金supported by the National Natural Science Foundation of China(grant No.52375247)Natural Science Foundation of Jiangsu Province(grant No.BK20201421)+3 种基金Graduate Research and Innovation Projects of Jiangsu Province(grant No.KYCX21-3380)Jiangsu Agricultural Science and Technology Independent Innovation Fund(grant No.CX(22)3090)Taizhou Science and Technology Project(grant No.TN202101)a Project Funded by the Priority Academic Program Development of Jiangsu Higher。
文摘The kinetic model is the theoretical basis for optimizing the structure and operation performance of vibration screening devices.In this paper,a biological neurodynamic equation and neural connections were established according to the motion and interaction properties of the material under vibration excitation.The material feeding to the screen and the material passing through apertures were considered as excitatory and inhibitory inputs,respectively,and the generated stable neural activity landscape was used to describe the material distribution on the 2D screen surface.The dynamic process of material vibration screening was simulated using discrete element method(DEM).By comparing the similarity between the material distribution established using biological neural network(BNN)and that obtained using DEM simulation,the optimum coefficients of BNN model under a certain screening parameter were determined,that is,one relationship between the BNN model coefficients and the screening operation parameters was established.Different screening parameters were randomly selected,and the corresponding relationships were established as a database.Then,with straw/grain ratio,aperture diameter,inclination angle,vibration strength in normal and tangential directions as inputs,five independent adaptive neuro-fuzzy inference systems(ANFIS)were established to predict the optimum BNN model coefficients,respectively.The training results indicated that ANFIS models had good stability and accuracy.The flexibility and adaptability of the proposed BNN method was demonstrated by modeling material distribution under complex feeding conditions such as multiple regions and non-uniform rate.
基金supported by the Hebei Province Natural Science Foundation of China (H2021201022)Youth Scienceof the National Natural Science Foundation of China (81903931)Post-graduates Innovation Fund Project of Hebei University (HBU2022ss001)
文摘Objective:Cell co-culture technology has been widely used to analyze the effects of drugs on cell proliferation and the expression of some proteins in cells,especially in the field of traditional Chinese medicine(TCM);however,the interactions between cells and the transmission of TCM effects between cells have not been adequately studied.Materials and Methods:Using data on gene transcription regulation,biological response,signal channel,and cell-specific expression protein,we built a network for cell types based on entity grammar.Through the correspondence and location information of signal molecules and receptors,type-specific networks of single cells were connected and a multicellular network of smooth muscle cells,neurons,and vascular endothelial cells was constructed.The mechanism of action of nimodipine was analyzed based on the multicellular communication network and its simulation capability was evaluated.Results:The outputs generated by the model developed in this study showed that nimodipine inhibited smooth muscle contraction,due to the overload of Ca^(2+)and the toxicity of excitatory amino acids,and protected neurons and vascular endothelial cells by supporting cell proliferation and inhibiting cell apoptosis.These results were consistent with the known mechanism of nimodipine action,thus confirming that the multicellular network can be used to study the transmission of drug effects among cells.Conclusions:This study lays a foundation for the analysis of the transmission of drug effects in multi-cells,tissues,organs,and other spatial scales through multicellular co-culture experiments,based on a multicellular communication network.In addition,it provides a biological network model for the analysis of TCM action mechanisms.
基金This work has been supported by the National Natural Science Foundation of China （Nos. 61621003, 61422309, 61379092 and 11661141019）, the Strategic Priority Research Program of the Chinese Academy of Sciences （CAS） （XDB13040600） and CAS Frontier Science Research Key Project for Top Young Scientist （QYZDB-SSW-SYS008）.
文摘Background： Module detection is widely used to analyze and visualize biological networks. A number of methods and tools have been developed to achieve it. Meanwhile, bipartite module detection is also very useful for mining and analyzing bipartite biological networks and a few methods have been developed for it. However, there is few user- friendly toolkit for this task. Methods： To this end, we develop an online web toolkit BMTK, which implements seven existing methods. Results： BMTK provides a uniform operation platform and visualization function, standardizes input and output format, and improves algorithmic structure to enhance computing speed. We also apply this toolkit onto a drug-target bipartite network to demonstrate its effectiveness. Conclusions： BMTK will be a powerful tool for detecting bipartite modules in diverse bipartite biological networks. Availability： The web application is freely accessible at http：//www.zhanglabtools.net/BMTK.
基金support from the Shanghai Science and Technology Development Program (Grant Nos. 03DZ14024 & 07ZR14010)the 863 High Technology Foundation of China (Grant No. 2006AA02A310)+1 种基金US NIH 1R01AI064806-01A2, 5R21DK082706U.S. Department of Energy, the Office of Science (BER) (Grant No. DE-FG02- 07ER64422)
文摘Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.
基金support from Taif University Researchers supporting Project Number(TURSP-2020/215),Taif University,Taif,Saudi Arabia.
文摘One of the leading cancers for both genders worldwide is lung cancer.The occurrence of lung cancer has fully augmented since the early 19th century.In this manuscript,we have discussed various data mining techniques that have been employed for cancer diagnosis.Exposure to air pollution has been related to various adverse health effects.This work is subject to analysis of various air pollutants and associated health hazards and intends to evaluate the impact of air pollution caused by lung cancer.We have introduced data mining in lung cancer to air pollution,and our approach includes preprocessing,data mining,testing and evaluation,and knowledge discovery.Initially,we will eradicate the noise and irrelevant data,and following that,we will join the multiple informed sources into a common source.From that source,we will designate the information relevant to our investigation to be regained from that assortment.Following that,we will convert the designated data into a suitable mining process.The patterns are abstracted by utilizing a relational suggestion rule mining process.These patterns have revealed information,and this information is categorized with the help of an Auto Associative Neural Network classification method(AANN).The proposed method is compared with the existing method in various factors.In conclusion,the projected Auto associative neural network and relational suggestion rule mining methods accomplish a high accuracy status.
文摘Diabetes is associated with many complications that could lead to death.Diabetic retinopathy,a complication of diabetes,is difficult to diagnose and may lead to vision loss.Visual identification of micro features in fundus images for the diagnosis of DR is a complex and challenging task for clinicians.Because clinical testing involves complex procedures and is timeconsuming,an automated system would help ophthalmologists to detect DR and administer treatment in a timelymanner so that blindness can be avoided.Previous research works have focused on image processing algorithms,or neural networks,or signal processing techniques alone to detect diabetic retinopathy.Therefore,we aimed to develop a novel integrated approach to increase the accuracy of detection.This approach utilized both convolutional neural networks and signal processing techniques.In this proposed method,the biological electro retinogram(ERG)sensor network(BSN)and deep convolution neural network(DCNN)were developed to detect and classify DR.In the BSN system,electrodes were used to record ERGsignal,which was preprocessed to be noise-free.Processing was performed in the frequency domain by the application of fast Fourier transform(FFT)and mel frequency cepstral coefficients(MFCCs)were extracted.Artificial neural network(ANN)classifier was used to classify the signals of eyes with DR and normal eye.Additionally,fundus images were captured using a fundus camera,and these were used as the input for DCNN-based analysis.The DCNN consisted of many layers to facilitate the extraction of features and classification of fundus images into normal images,non-proliferative DR(NPDR)or earlystage DR images,and proliferative DR(PDR)or advanced-stage DR images.Furthermore,it classifiedNPDRaccording tomicroaneurysms,hemorrhages,cotton wool spots,and exudates,and the presence of new blood vessels indicated PDR.The accuracy,sensitivity,and specificity of the ANNclassifier were found to be 94%,95%,and 93%,respectively.Both the accuracy rate and sensitivity rate of theDCNNclassifierwas 96.5%for the images acquired from various hospitals as well as databases.A comparison between the accuracy rates of BSN andDCNN approaches showed thatDCNNwith fundus images decreased the error rate to 4%.
基金supported by the Joseph Rowntree Charitable Trust(JRCT),United Kingdom,grant ID NO5469.
文摘With the rapid advances in technology and life science,biological security is now at a defining moment.The mandate of the 2022 Biological and Toxin Weapons Convention 9th Review Conference emphasised the urgent need for new tools to strengthen the Convention.In this paper,we review the development and efforts of the International Nuclear Security Education Network(INSEN)to provide examples of best practice for implementation of the newly founded International Biological Security Education Network(IBSEN).Learning from the lessons of the INSEN,the sustainability of the network through continuous engagement of its members is essential for the further development of global biosecurity education.
基金supported in part by US National Science Foundation,Division of Industrial Innovation and Partnerships(1160960 and 1332024)Computing and Communication Foundations(0905291)+2 种基金National Natural Science Foundation of China(90920005,61170189)the Twelfth Five-year Plan of China(2012BAK24B01)National Social Science Funds of China(12&2D223,13&ZD183)
文摘With the rapid accumulation of high-throughput metagenomic sequencing data,it is possible to infer microbial species relations in a microbial community systematically.In recent years,some approaches have been proposed for identifying microbial interaction network.These methods often focus on one dataset without considering the advantage of data integration.In this study,we propose to use a similarity network fusion(SNF)method to infer microbial relations.The SNF efficiently integrates the similarities of species derived from different datasets by a cross-network diffusion process.We also introduce consensus k-nearest neighborhood(Ck-NN)method instead of k-NN in the original SNF(we call the approach CSNF).The final network represents the augmented species relationships with aggregated evidence from various datasets,taking advantage of complementarity in the data.We apply the method on genus profiles derived from three microbiome datasets and we find that CSNF can discover the modular structure of microbial interaction network which cannot be identified by analyzing a single dataset.
基金This study was supported partially by the National Science Foundation of China （Grant Nos. 11475253, 11405263, and 11675112） and the Natural Science Foundation of Zhejiang Province （Grant No. LY16A050001）.
文摘Revealing how a biological network is organized to realize its function is one of the main topics in systems biology. Tile functional backbone network, defined as the primary structure of the biological network, is of great importance in maintaining the main function of the biological network. We propose a new algorithm, the tinker algorithm, to determine this core structure and apply it in the cell-cycle system. With this algorithm, the backbone network of the cell-cycle network can be determined accurately and efficiently in various models such as the Boolean model, stochastic model, and ordinary differential equation model. Results show that our algorithm is more efficient than that used in the previous research. We hope this method can be put into practical use in relevant future studies.
基金supported by the National Natural Science Foundation of China under Grant No.61773153the Natural Science Foundation of Henan under Grant No.202300410045+2 种基金the Supporting Plan for Scientific and Technological Innovative Talents in Universities of Henan Province under Grant No.20HASTIT025the Training Plan of Young Key Teachers in Colleges and Universities of Henan Province under Grant No.2018GGJS021Partly supported by the Supporting Grant of Bioinformatics Center of Henan University under Grant No.2018YLJC03。
文摘Biological systems can be modeled and described by biological networks.Biological networks are typical complex networks with widely real-world applications.Many problems arising in biological systems can be boiled down to the identification of important nodes.For example,biomedical researchers frequently need to identify important genes that potentially leaded to disease phenotypes in animal and explore crucial genes that were responsible for stress responsiveness in plants.To facilitate the identification of important nodes in biological systems,one needs to know network structures or behavioral data of nodes(such as gene expression data).If network topology was known,various centrality measures can be developed to solve the problem;while if only behavioral data of nodes were given,some sophisticated statistical methods can be employed.This paper reviewed some of the recent works on statistical identification of important nodes in biological systems from three aspects,that is,1)in general complex networks based on complex networks theory and epidemic dynamic models;2)in biological networks based on network motifs;and 3)in plants based on RNA-seq data.The identification of important nodes in a complex system can be seen as a mapping from the system to the ranking score vector of nodes,such mapping is not necessarily with explicit form.The three aspects reflected three typical approaches on ranking nodes in biological systems and can be integrated into one general framework.This paper also proposed some challenges and future works on the related topics.The associated investigations have potential real-world applications in the control of biological systems,network medicine and new variety cultivation of crops.
基金This work was supported by a grant from Shanghai Institutes for Biological Sciences,Chinese Academy of Sciences(No.2008KIP207)the National Natural Science Foundation of China(Grant No.30770497)the National Key Technologies R&D Program(No.2007AA02Z331).
文摘With the popularization of microarray experi-ments in biomedical laboratories,how to make context-specific knowledge discovery from expression data becomes a hot topic.While the static“reference networks”for key model organisms are nearly at hand,the endeavors to recover context-specific network modules are still at the beginning.Currently,this is achieved through filtering existing edges of the ensemble reference network or constructing gene networks ab initio.In this paper,we briefly review recent progress in the field and point out some research directions awaiting improved work,includ-ing expression-data-guided revision of reference networks.
基金sponsored by Tsinghua-Toyota Joint Research Fundsupported by the Anhui Province TraditionalChinese Medicine Science and Technology Research Project(202303a07020001)the National Natural Science Foundation of China (T2341008)
文摘The core principle of diagnosis and treatment in traditional Chinese medicine(TCM) is the identification of different syndromes.Cold and hot syndromes are important elements in TCM theory.Identifying the biological basis of cold and hot syndromes in TCM will help elucidate TCM theories scientifically,thus promoting precise treatment in TCM.Although the biological basis of cold/hot syndromes in TCM remains poorly understood,growing evide nce suggests that immunometab olic interactions play an important role in balancing cold and hot syndromes.Immuno metabolism involve s complex interactions between the immune and metabolic systems.Multilevel mechanisms of interaction between the immune and metabolic systems may underlie many inflammatory diseases and offer substantial therapeutic promise.Therefore,dissecting the relationship between immunometabolism and the biological network of cold/hot syndromes has become a priority.This article reviews the progress of cold/hot syndrome research from the perspective of immunometabolic homeostasis,thus further clarifying cold/hot syndromes in TCM.
