Over the past decade,artificial intelligence(AI)has evolved at an unprecedented pace,transforming technology,industry,and society.From diagnosing diseases with remarkable accuracy to powering self-driving cars and rev...Over the past decade,artificial intelligence(AI)has evolved at an unprecedented pace,transforming technology,industry,and society.From diagnosing diseases with remarkable accuracy to powering self-driving cars and revolutionizing personalized learning,AI is reshaping our world in ways once thought impossible.Spanning fields such as machine learning,deep learning,natural language processing,robotics,and ChatGPT,AI continues to push the boundaries of innovation.As AI continues to advance,it is vital to have a platform that not only disseminates cutting-edge research innovations but also fosters broad discussions on its societal impact,ethical considerations,and interdisciplinary applications.With this vision in mind,we proudly introduce Artificial Intelligence Science and Engineering(AISE)-a journal dedicated to nurturing the next wave of AI innovation and engineering applications.Our mission is to provide a premier outlet where researchers can share high-quality,impactful studies and collaborate to advance AI across academia,industry,and beyond.展开更多
Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,a...Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint changes.These limitations result in delayed diagnoses and inconsistent outcomes.Additionally,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and biomarkers.Recent advancements in artificial intelligence(AI)offer transformative potential to address these challenges.This review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics integration.By consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA research.The findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.展开更多
Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays ...Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small molecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient features across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associated with machine learning(ML)methods for molecular representation and improving downstream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine(TCM)medicinal substances and facilitating TCM target discovery.展开更多
This paper addresses the issue of software rejuvenation modeling.Rejuvenation strategies with sequential inspection periods and state multi-control limits are proposed here because the inspection-based approach involv...This paper addresses the issue of software rejuvenation modeling.Rejuvenation strategies with sequential inspection periods and state multi-control limits are proposed here because the inspection-based approach involves the sampling of longer fixed periods of the state of system, which increases the probability of soft failure. The degradation process of the software system interferes with inspection and rejuvenation is modeled as a Markov chain. The steady-state probability density function of the system is thus derived, and a numerical solution of the function is provided. Expressions for mean unavailability time are derived during the inspection period when soft failure occurs. Finally, the steady-state availability of the system is modeled, and the solution to it is obtained using a genetic algorithm. The effectiveness of the model was verified by numerical experiments. Compared with rejuvenation strategies with fixed inspection periods, those with sequential inspection periods yielded greater steady-state availability of the software system.展开更多
In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become i...In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become indistinguishable as the curse of dimensionality increases in the objective space and the accumulation of surrogate approximated errors.Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi⁃objective evolutionary algorithm of strengthened dominance relation.The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators.Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set.The algorithm is contrasted against several advanced surrogate⁃assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many⁃objective optimization problems.展开更多
This paper studies the consensus problems for multi-agent systems with general linear and nonlinear dynamics. The leaderless and leader-following consensus problems are investigated respectively. Contraction theory is...This paper studies the consensus problems for multi-agent systems with general linear and nonlinear dynamics. The leaderless and leader-following consensus problems are investigated respectively. Contraction theory is employed to gen- erate some sufficient conditions for testing the agents reaching consensus. Under these conditions and certain assumptions, the trajectories of multi-agent systems in directed topology will converge to each other. Finally, two numerical examples are given to illustrate the effectiveness of the proposed results,展开更多
Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of data.It is challenging and infeasible to transfe...Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of data.It is challenging and infeasible to transfer and process trillions and zillions of bytes using the current cloud-device architecture.展开更多
1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,Ch...1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,China E-mail:xjlei@snnu.edu.cn;yalichen@snnu.edu.cn;yi.pan@siat.ac.cn Received December 9,2022;accepted July 29,2024.Abstract Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of diseases.In this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named MMHN-MDA.First,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and metabolites.Second,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease nodes.At the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease nodes.Finally,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease associations.Experimental results show that the proposed method is more effective than existing methods.In addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier.展开更多
Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical rules.Herein,we aim to opti...Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical rules.Herein,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated molecule.The Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization properties.The main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix dimension.Threshold intervals and state changes are then used to encode logD and solubility for subsequent tests.During the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,respectively.Transformer models are compared with the baseline models with respect to their abilities to generate molecules with specific properties.Results show that the transformer model can accurately optimize the source molecules to satisfy specific properties.展开更多
The blades on the plane are one of the most important parts of the engine,in the course of service,due to high temperature,strong vibration and great centrifugal force and so on.The using environment is very bad,so it...The blades on the plane are one of the most important parts of the engine,in the course of service,due to high temperature,strong vibration and great centrifugal force and so on.The using environment is very bad,so it is easy to produce fatigue cracks in the welding site and the near surface of the root,which will seriously affect the blade of the work intensity and fatigue life,and even the safety of aircraft structure,causing a huge security risk.Therefore,it must be tested.In order to solve the problem of the rapid detection of aircraft engine in situ cracks,and gett the rela-tionship between feature information and detect depth,the laboratory experimental platform was built,laser was used to excite laser ultrasonic signals on a range of aviation aluminum plates with different depth defects,the collected sig-nal was processed by wavelet de-noising,and the band energy distribution of the reflected echo signal was studied by using wavelet packet.The results show that the energy of reflected echo signal is mainly concentrated in the S80~So7 band.When the depth of defect is 0.2 mm to 0.4 mm,the energy is mainly concentrated in the adjacent bands.When the depth of defect is 0.5 mm to 0.7 mm,the energy is mainly concentrated in the two bands.This method provides a way to quantify surface micro-defects by ultrasonic signals,which will lay a foundation for the future analysis of crack depth from band energy.In order to avoid the interference of other irregular cracks,the cracks of the aviation aluminum parts are used as ar-tificial way for producing.The overall size of the specimen is 200 mmx80 mmx100 mm,the width of the defect is 0.15 mm,the range of the defect depth is 0.2 mm~0.7 mm,step size is 0.1 mm,and the total number of the specimen is six.After the experimental data is proposed,choosing the reflected echo signal for analysis,performing wavelet packet transform,the decomposition layer is 8.The percentage in the Sao~Sa7band is 89.77%、91.82%、91.41%、90.94%、90.19%、and 87.86%.The result shows that most of the energy is concentrated in the first eight bands.Therefore,the paper selects the first eight bands for analysis.In order to analyze the distribution characteristics of the different depth defect and the band energy,the energy dis-tribution of the first four bands of the defect depth of 0.2 mm to 0.4 mm is plotted in Fig,according to the spectrum,getting the center frequency were 3.14 MHz,2.58 MHz,2.17 MHz.These frequencies are located in the S83,S82,S82 band,respectively,which are the largest energy band,but the energy distribution in the adjacent segment Ss:also ac-counts for a larger proportion.When the depth of the defect increases from 0.2 mm to 0.4 mm,the center frequency decreases gradually,and the sum of the energy of the center frequency band and the adjacent higher energy band in-creases gradually.展开更多
Surface-based geometric modeling has many advantages in terms of visualization and traditional subtractive manufacturing using computer-numerical-control cutting-machine tools.However,it is not an ideal solution for a...Surface-based geometric modeling has many advantages in terms of visualization and traditional subtractive manufacturing using computer-numerical-control cutting-machine tools.However,it is not an ideal solution for additive manufacturing because to digitally print a surface-represented geometric object using a certain additive manufacturing technology,the object has to be converted into a solid representation.However,converting a known surface-based geometric representation into a printable representation is essentially a redesign process,and this is especially the case,when its interior material structure needs to be considered.To specify a 3D geometric object that is ready to be digitally manufactured,its representation has to be in a certain volumetric form.In this research,we show how some of the difficulties experienced in additive manufacturing can be easily solved by using implicitly represented geometric objects.Like surface-based geometric representation is subtractive manufacturing-friendly,implicitly described geometric objects are additive manufacturing-friendly:implicit shapes are 3D printing ready.The implicit geometric representation allows to combine a geometric shape,material colors,an interior material structure,and other required attributes in one single description as a set of implicit functions,and no conversion is needed.In addition,as implicit objects are typically specified procedurally,very little data is used in their specifications,which makes them particularly useful for design and visualization with modern cloud-based mobile devices,which usually do not have very big storage spaces.Finally,implicit modeling is a design procedure that is parallel computing-friendly,as the design of a complex geometric object can be divided into a set of simple shape-designing tasks,owing to the availability of shape-preserving implicit blending operations.展开更多
In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements ...In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements are very complicated, and it is time-consuming, even omission may occur when enumerating them with various combinations of units and even with different maintenance actions for them. Deterioration state space partition (DSSP) method is an efficient approach to analyze all possible maintenance requirements at each maintenance decision point and deduce their corresponding probabilities for maintenance modeling of multi-unit systems. In this paper, an extended DSSP method is developed for systems with multiple non-identical units considering opportunistic, preventive and corrective maintenance activities for each unit. In this method, different maintenance types are distinguished in each maintenance requirement. A new representation of the possible maintenance requirements and their corresponding probabilities is derived according to the partition results based on the joint probability density function of the maintained system deterioration state. Furthermore, focusing on a two-unit system with a non-periodical inspected condition-based opportunistic preventive-maintenance strategy;a long-term average cost model is established using the proposed method to determine its optimal maintenance parameters jointly, in which “hard failure” and non-negligible maintenance time are considered. Numerical experiments indicate that the extended DSSP method is valid for opportunistic maintenance modeling of multi-unit systems.展开更多
Accurately diagnosing Alzheimer's disease is essential for improving elderly health.Meanwhile,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the prog...Accurately diagnosing Alzheimer's disease is essential for improving elderly health.Meanwhile,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's disease.However,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks.To address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score prediction.In our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction.Then,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each task.We validate the effectiveness of our proposed method on multiple datasets.In Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,respectively.Experimental results show that our proposed method outperforms most state-of-the-art methods.Our proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score prediction.Therefore,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.展开更多
Next-generation sequencing data are widely utilised for various downstream applications in bioinformatics and numerous techniques have been developed for PCR-deduplication and error-correction to eliminate bias and er...Next-generation sequencing data are widely utilised for various downstream applications in bioinformatics and numerous techniques have been developed for PCR-deduplication and error-correction to eliminate bias and errors introduced during the sequencing.This study first-time provides a joint overview of recent advances in PCR-deduplication and error-correction on short reads.In particular,we utilise UMI-based PCR-deduplication strategies and sequencing data to assess the performance of the solelycomputational PCR-deduplication approaches and investigate how error correction affects the performance of PCR-deduplication.Our survey and comparative analysis reveal that the deduplicated reads generated by the solely-computational PCR-deduplication and error-correction methods exhibit substantial differences and divergence from the sets of reads obtained by the UMI-based deduplication methods.The existing solelycomputational PCR-deduplication and error-correction tools can eliminate some errors but still leave hundreds of thousands of erroneous reads uncorrected.All the error-correction approaches raise thousands or more new sequences after correction which do not have any benefit to the PCRdeduplication process.Based on our findings,we discuss future research directions and make suggestions for improving existing computational approaches to enhance the quality of short-read sequencing data.展开更多
Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs wi...Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end,we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG development.Furthermore,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for MKG.In addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.展开更多
Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitati...Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature learning.In view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding sites.We have constructed a data set which includes eight sub-datasets.First,circ2CBA encodes circRNA sequences using the one-hot method.Next,a two-layer convolutional neural network(CNN)is used to initially extract the features.After CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the features.The AUC value of circ2CBA reaches 0.8987.Comparison of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.展开更多
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between comp...Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer.Here,we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs.First,we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations.Second,we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms.Finally,we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery.Taken together,the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer,thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.展开更多
Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential proteins.However,g...Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential proteins.However,gene expression data are prone to significant fluctuations due to noise interference in topological networks.In this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise fluctuation.We then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression data.Using the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential proteins.This strategy exhibited a high recognition rate and good specificity.We validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy analysis.Compared with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and WDC.We also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene expression.The experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.展开更多
In this paper,an extended heterogeneous SIR model is proposed,which generalizes the heterogeneous mean-field theory.Different from the traditional heterogeneous mean-field model only taking into account the heterogene...In this paper,an extended heterogeneous SIR model is proposed,which generalizes the heterogeneous mean-field theory.Different from the traditional heterogeneous mean-field model only taking into account the heterogeneity of degree,our model considers not only the heterogeneity of degree but also the heterogeneity of susceptibility and recovery rates.Then,we analytically study the basic reproductive number and the final epidemic size.Combining with numerical simulations,it is found that the basic reproductive number depends on the mean of distributions of susceptibility and disease course when both of them are independent.If the mean of these two distributions is identical,increasing the variance of susceptibility may block the spread of epidemics,while the corresponding increase in the variance of disease course has little effect on the final epidemic size.It is also shown that positive correlations between individual susceptibility,course of disease and the square of degree make the population more vulnerable to epidemic and avail to the epidemic prevalence,whereas the negative correlations make the population less vulnerable and impede the epidemic prevalence.展开更多
This paper describes a fundamental consideration on our works on the design of general Bayes' filters for the state estimation of non-stationary, non-linear, and non-Gaussian environmental sound and vibration syst...This paper describes a fundamental consideration on our works on the design of general Bayes' filters for the state estimation of non-stationary, non-linear, and non-Gaussian environmental sound and vibration systems. We have discussed an essential point of several Bayes' filters proposed by using the orthogonal or non-orthogonal expansion form of Bayes' theorem. They can estimate any kinds of statistics of arbitrary function type of state variables including the lower and the higher order statistics connected with the Lx evaluation index in the environmental sound and vibration systems. Here, we have mainly focussed on giving the fundamental viewpoints of their design policies. Some new estimation methods and new results not yet published are included.展开更多
文摘Over the past decade,artificial intelligence(AI)has evolved at an unprecedented pace,transforming technology,industry,and society.From diagnosing diseases with remarkable accuracy to powering self-driving cars and revolutionizing personalized learning,AI is reshaping our world in ways once thought impossible.Spanning fields such as machine learning,deep learning,natural language processing,robotics,and ChatGPT,AI continues to push the boundaries of innovation.As AI continues to advance,it is vital to have a platform that not only disseminates cutting-edge research innovations but also fosters broad discussions on its societal impact,ethical considerations,and interdisciplinary applications.With this vision in mind,we proudly introduce Artificial Intelligence Science and Engineering(AISE)-a journal dedicated to nurturing the next wave of AI innovation and engineering applications.Our mission is to provide a premier outlet where researchers can share high-quality,impactful studies and collaborate to advance AI across academia,industry,and beyond.
基金supported by the National Natural Science Foundation of China(82302757)Shenzhen Science and Technology Program(JCY20240813145204006,SGDX20201103095600002,JCYJ20220818103417037,KJZD20230923115200002)+1 种基金Shenzhen Key Laboratory of Digital Surgical Printing Project(ZDSYS201707311542415)Shenzhen Development and Reform Program(XMHT20220106001).
文摘Osteoarthritis(OA)is a degenerative joint disease with significant clinical and societal impact.Traditional diagnostic methods,including subjective clinical assessments and imaging techniques such as X-rays and MRIs,are often limited in their ability to detect early-stage OA or capture subtle joint changes.These limitations result in delayed diagnoses and inconsistent outcomes.Additionally,the analysis of omics data is challenged by the complexity and high dimensionality of biological datasets,making it difficult to identify key molecular mechanisms and biomarkers.Recent advancements in artificial intelligence(AI)offer transformative potential to address these challenges.This review systematically explores the integration of AI into OA research,focusing on applications such as AI-driven early screening and risk prediction from electronic health records(EHR),automated grading and morphological analysis of imaging data,and biomarker discovery through multi-omics integration.By consolidating progress across clinical,imaging,and omics domains,this review provides a comprehensive perspective on how AI is reshaping OA research.The findings have the potential to drive innovations in personalized medicine and targeted interventions,addressing longstanding challenges in OA diagnosis and management.
基金supported by the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)the Shenzhen Science and Technology Program(No.JCYJ20230807140709020)+2 种基金National Natural Science Foundation of China(Nos.62402489,U22A2041,and 62373172)the China Postdoctoral Science Foundation(No.2023M743688)Guangdong Basic and Applied Basic Research Foundation(Nos.2024A1515011960 and 2023A1515110570)。
文摘Artificial intelligence(AI)researchers and cheminformatics specialists strive to identify effective drug precursors while optimizing costs and accelerating development processes.Digital molecular representation plays a crucial role in achieving this objective by making molecules machine-readable,thereby enhancing the accuracy of molecular prediction tasks and facilitating evidence-based decision making.This study presents a comprehensive review of small molecular representations and AI-driven drug discovery downstream tasks utilizing these representations.The research methodology begins with the compilation of small molecule databases,followed by an analysis of fundamental molecular representations and the models that learn these representations from initial forms,capturing patterns and salient features across extensive chemical spaces.The study then examines various drug discovery downstream tasks,including drug-target interaction(DTI)prediction,drug-target affinity(DTA)prediction,drug property(DP)prediction,and drug generation,all based on learned representations.The analysis concludes by highlighting challenges and opportunities associated with machine learning(ML)methods for molecular representation and improving downstream task performance.Additionally,the representation of small molecules and AI-based downstream tasks demonstrates significant potential in identifying traditional Chinese medicine(TCM)medicinal substances and facilitating TCM target discovery.
文摘This paper addresses the issue of software rejuvenation modeling.Rejuvenation strategies with sequential inspection periods and state multi-control limits are proposed here because the inspection-based approach involves the sampling of longer fixed periods of the state of system, which increases the probability of soft failure. The degradation process of the software system interferes with inspection and rejuvenation is modeled as a Markov chain. The steady-state probability density function of the system is thus derived, and a numerical solution of the function is provided. Expressions for mean unavailability time are derived during the inspection period when soft failure occurs. Finally, the steady-state availability of the system is modeled, and the solution to it is obtained using a genetic algorithm. The effectiveness of the model was verified by numerical experiments. Compared with rejuvenation strategies with fixed inspection periods, those with sequential inspection periods yielded greater steady-state availability of the software system.
基金Sponsored by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(Grant No.2022L294)Taiyuan University of Science and Technology Scientific Research Initial Funding(Grant Nos.W2022018,W20242012)Foundamental Research Program of Shanxi Province(Grant No.202403021212170).
文摘In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become indistinguishable as the curse of dimensionality increases in the objective space and the accumulation of surrogate approximated errors.Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi⁃objective evolutionary algorithm of strengthened dominance relation.The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators.Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set.The algorithm is contrasted against several advanced surrogate⁃assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many⁃objective optimization problems.
基金supported by the National Natural Science Foundation of China(Grant Nos.61473136 and 61174021)the Fundamental Research Funds for the Central Universities of China(Grant No.JUSRP51322B)the 111 Project(Grant No.B12018)
文摘This paper studies the consensus problems for multi-agent systems with general linear and nonlinear dynamics. The leaderless and leader-following consensus problems are investigated respectively. Contraction theory is employed to gen- erate some sufficient conditions for testing the agents reaching consensus. Under these conditions and certain assumptions, the trajectories of multi-agent systems in directed topology will converge to each other. Finally, two numerical examples are given to illustrate the effectiveness of the proposed results,
文摘Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of data.It is challenging and infeasible to transfer and process trillions and zillions of bytes using the current cloud-device architecture.
基金supported by the National Natural Science Foundation of China under Grant Nos.62272288 and U22A2041the Fundamental Research Funds for the Central Universities of China,and Shaanxi Normal University under Grant No.GK202302006.
文摘1 School of Computer Science,Shaanxi Normal University,Xi’an 710119,China 2 Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China 3 Shenzhen Key Laboratory of Intelligent Bioinformatics,Shenzhen Institute of Advanced Technology,Chinese Academy of Science,Shenzhen 518055,China E-mail:xjlei@snnu.edu.cn;yalichen@snnu.edu.cn;yi.pan@siat.ac.cn Received December 9,2022;accepted July 29,2024.Abstract Identifying microbes associated with diseases is important for understanding the pathogenesis of diseases as well as for the diagnosis and treatment of diseases.In this article,we propose a method based on a multi-source association network to predict microbe-disease associations,named MMHN-MDA.First,a heterogeneous network of multimolecule associations is constructed based on associations between microbes,diseases,drugs,and metabolites.Second,the graph embedding algorithm Laplacian eigenmaps is applied to the association network to learn the behavior features of microbe nodes and disease nodes.At the same time,the denoising autoencoder(DAE)is used to learn the attribute features of microbe nodes and disease nodes.Finally,attribute features and behavior features are combined to get the final embedding features of microbes and diseases,which are fed into the convolutional neural network(CNN)to predict the microbedisease associations.Experimental results show that the proposed method is more effective than existing methods.In addition,case studies on bipolar disorder and schizophrenia demonstrate good predictive performance of the MMHN-MDA model,and further,the results suggest that gut microbes may influence host gene expression or compounds in the nervous system,such as neurotransmitters,or metabolites that alter the blood-brain barrier.
基金This work was supported by the National Natural Science Foundation of China(Nos.62272288,61972451,and U22A2041)the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001).
文摘Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical rules.Herein,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated molecule.The Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization properties.The main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix dimension.Threshold intervals and state changes are then used to encode logD and solubility for subsequent tests.During the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,respectively.Transformer models are compared with the baseline models with respect to their abilities to generate molecules with specific properties.Results show that the transformer model can accurately optimize the source molecules to satisfy specific properties.
文摘The blades on the plane are one of the most important parts of the engine,in the course of service,due to high temperature,strong vibration and great centrifugal force and so on.The using environment is very bad,so it is easy to produce fatigue cracks in the welding site and the near surface of the root,which will seriously affect the blade of the work intensity and fatigue life,and even the safety of aircraft structure,causing a huge security risk.Therefore,it must be tested.In order to solve the problem of the rapid detection of aircraft engine in situ cracks,and gett the rela-tionship between feature information and detect depth,the laboratory experimental platform was built,laser was used to excite laser ultrasonic signals on a range of aviation aluminum plates with different depth defects,the collected sig-nal was processed by wavelet de-noising,and the band energy distribution of the reflected echo signal was studied by using wavelet packet.The results show that the energy of reflected echo signal is mainly concentrated in the S80~So7 band.When the depth of defect is 0.2 mm to 0.4 mm,the energy is mainly concentrated in the adjacent bands.When the depth of defect is 0.5 mm to 0.7 mm,the energy is mainly concentrated in the two bands.This method provides a way to quantify surface micro-defects by ultrasonic signals,which will lay a foundation for the future analysis of crack depth from band energy.In order to avoid the interference of other irregular cracks,the cracks of the aviation aluminum parts are used as ar-tificial way for producing.The overall size of the specimen is 200 mmx80 mmx100 mm,the width of the defect is 0.15 mm,the range of the defect depth is 0.2 mm~0.7 mm,step size is 0.1 mm,and the total number of the specimen is six.After the experimental data is proposed,choosing the reflected echo signal for analysis,performing wavelet packet transform,the decomposition layer is 8.The percentage in the Sao~Sa7band is 89.77%、91.82%、91.41%、90.94%、90.19%、and 87.86%.The result shows that most of the energy is concentrated in the first eight bands.Therefore,the paper selects the first eight bands for analysis.In order to analyze the distribution characteristics of the different depth defect and the band energy,the energy dis-tribution of the first four bands of the defect depth of 0.2 mm to 0.4 mm is plotted in Fig,according to the spectrum,getting the center frequency were 3.14 MHz,2.58 MHz,2.17 MHz.These frequencies are located in the S83,S82,S82 band,respectively,which are the largest energy band,but the energy distribution in the adjacent segment Ss:also ac-counts for a larger proportion.When the depth of the defect increases from 0.2 mm to 0.4 mm,the center frequency decreases gradually,and the sum of the energy of the center frequency band and the adjacent higher energy band in-creases gradually.
基金supported by the National Natural Science Foundation of China(Grant No.61502402 and 61379080)the Natural Science Foundation of Fujian Province of China(Grant No.2015J05129).
文摘Surface-based geometric modeling has many advantages in terms of visualization and traditional subtractive manufacturing using computer-numerical-control cutting-machine tools.However,it is not an ideal solution for additive manufacturing because to digitally print a surface-represented geometric object using a certain additive manufacturing technology,the object has to be converted into a solid representation.However,converting a known surface-based geometric representation into a printable representation is essentially a redesign process,and this is especially the case,when its interior material structure needs to be considered.To specify a 3D geometric object that is ready to be digitally manufactured,its representation has to be in a certain volumetric form.In this research,we show how some of the difficulties experienced in additive manufacturing can be easily solved by using implicitly represented geometric objects.Like surface-based geometric representation is subtractive manufacturing-friendly,implicitly described geometric objects are additive manufacturing-friendly:implicit shapes are 3D printing ready.The implicit geometric representation allows to combine a geometric shape,material colors,an interior material structure,and other required attributes in one single description as a set of implicit functions,and no conversion is needed.In addition,as implicit objects are typically specified procedurally,very little data is used in their specifications,which makes them particularly useful for design and visualization with modern cloud-based mobile devices,which usually do not have very big storage spaces.Finally,implicit modeling is a design procedure that is parallel computing-friendly,as the design of a complex geometric object can be divided into a set of simple shape-designing tasks,owing to the availability of shape-preserving implicit blending operations.
文摘In the optimal maintenance modeling, all possible maintenance activities and their corresponding probabilities play a key role in modeling. For a system with multiple non-identical units, its maintenance requirements are very complicated, and it is time-consuming, even omission may occur when enumerating them with various combinations of units and even with different maintenance actions for them. Deterioration state space partition (DSSP) method is an efficient approach to analyze all possible maintenance requirements at each maintenance decision point and deduce their corresponding probabilities for maintenance modeling of multi-unit systems. In this paper, an extended DSSP method is developed for systems with multiple non-identical units considering opportunistic, preventive and corrective maintenance activities for each unit. In this method, different maintenance types are distinguished in each maintenance requirement. A new representation of the possible maintenance requirements and their corresponding probabilities is derived according to the partition results based on the joint probability density function of the maintained system deterioration state. Furthermore, focusing on a two-unit system with a non-periodical inspected condition-based opportunistic preventive-maintenance strategy;a long-term average cost model is established using the proposed method to determine its optimal maintenance parameters jointly, in which “hard failure” and non-negligible maintenance time are considered. Numerical experiments indicate that the extended DSSP method is valid for opportunistic maintenance modeling of multi-unit systems.
基金supported in part by the National Natural Science Foundation of China(No.62172444)Natural Science Foundation of Hunan Province(No.2022JJ30753)+1 种基金Central South University Innovation-Driven Research Programme(No.2023CXQD018)High Performance Computing Center of Central South University.
文摘Accurately diagnosing Alzheimer's disease is essential for improving elderly health.Meanwhile,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's disease.However,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks.To address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score prediction.In our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction.Then,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each task.We validate the effectiveness of our proposed method on multiple datasets.In Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,respectively.Experimental results show that our proposed method outperforms most state-of-the-art methods.Our proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score prediction.Therefore,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.
文摘Next-generation sequencing data are widely utilised for various downstream applications in bioinformatics and numerous techniques have been developed for PCR-deduplication and error-correction to eliminate bias and errors introduced during the sequencing.This study first-time provides a joint overview of recent advances in PCR-deduplication and error-correction on short reads.In particular,we utilise UMI-based PCR-deduplication strategies and sequencing data to assess the performance of the solelycomputational PCR-deduplication approaches and investigate how error correction affects the performance of PCR-deduplication.Our survey and comparative analysis reveal that the deduplicated reads generated by the solely-computational PCR-deduplication and error-correction methods exhibit substantial differences and divergence from the sets of reads obtained by the UMI-based deduplication methods.The existing solelycomputational PCR-deduplication and error-correction tools can eliminate some errors but still leave hundreds of thousands of erroneous reads uncorrected.All the error-correction approaches raise thousands or more new sequences after correction which do not have any benefit to the PCRdeduplication process.Based on our findings,we discuss future research directions and make suggestions for improving existing computational approaches to enhance the quality of short-read sequencing data.
基金supported in part by the National Key Research and Development Program of China(No.2021YFF1201200)the National Natural Science Foundation of China(No.62006251)the Science and Technology Innovation Program of Hunan Province(No.2021RC4008).
文摘Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical applications.Thus,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end,we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG development.Furthermore,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for MKG.In addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.
基金supported by the National Natural Science Foundation of China(Grant Nos.61972451,61902230)the Fundamental Research Funds for the Central Universities,Shaanxi Normal University(GK202103091)。
文摘Circular RNAs(circRNAs)are RNAs with closed circular structure involved in many biological processes by key interactions with RNA binding proteins(RBPs).Existing methods for predicting these interactions have limitations in feature learning.In view of this,we propose a method named circ2CBA,which uses only sequence information of circRNAs to predict circRNA-RBP binding sites.We have constructed a data set which includes eight sub-datasets.First,circ2CBA encodes circRNA sequences using the one-hot method.Next,a two-layer convolutional neural network(CNN)is used to initially extract the features.After CNN,circ2CBA uses a layer of bidirectional long and short-term memory network(BiLSTM)and the self-attention mechanism to learn the features.The AUC value of circ2CBA reaches 0.8987.Comparison of circ2CBA with other three methods on our data set and an ablation experiment confirm that circ2CBA is an effective method to predict the binding sites between circRNAs and RBPs.
文摘Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer.Here,we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs.First,we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations.Second,we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms.Finally,we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery.Taken together,the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer,thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
基金supported by the Shenzhen KQTD Project(No.KQTD20200820113106007)China Scholarship Council(No.201906725017)+2 种基金the Collaborative Education Project of Industry-University cooperation of the Chinese Ministry of Education(No.201902098015)the Teaching Reform Project of Hunan Normal University(No.82)the National Undergraduate Training Program for Innovation(No.202110542004).
文摘Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential proteins.However,gene expression data are prone to significant fluctuations due to noise interference in topological networks.In this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise fluctuation.We then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression data.Using the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential proteins.This strategy exhibited a high recognition rate and good specificity.We validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy analysis.Compared with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and WDC.We also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene expression.The experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.
基金This work is supported by the National Natural Science Foundation of China under Grant No.11331009The Science and Technology Innovation Team in Shanxi Province No.201605D131044-06。
文摘In this paper,an extended heterogeneous SIR model is proposed,which generalizes the heterogeneous mean-field theory.Different from the traditional heterogeneous mean-field model only taking into account the heterogeneity of degree,our model considers not only the heterogeneity of degree but also the heterogeneity of susceptibility and recovery rates.Then,we analytically study the basic reproductive number and the final epidemic size.Combining with numerical simulations,it is found that the basic reproductive number depends on the mean of distributions of susceptibility and disease course when both of them are independent.If the mean of these two distributions is identical,increasing the variance of susceptibility may block the spread of epidemics,while the corresponding increase in the variance of disease course has little effect on the final epidemic size.It is also shown that positive correlations between individual susceptibility,course of disease and the square of degree make the population more vulnerable to epidemic and avail to the epidemic prevalence,whereas the negative correlations make the population less vulnerable and impede the epidemic prevalence.
文摘This paper describes a fundamental consideration on our works on the design of general Bayes' filters for the state estimation of non-stationary, non-linear, and non-Gaussian environmental sound and vibration systems. We have discussed an essential point of several Bayes' filters proposed by using the orthogonal or non-orthogonal expansion form of Bayes' theorem. They can estimate any kinds of statistics of arbitrary function type of state variables including the lower and the higher order statistics connected with the Lx evaluation index in the environmental sound and vibration systems. Here, we have mainly focussed on giving the fundamental viewpoints of their design policies. Some new estimation methods and new results not yet published are included.
