With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with part...With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with particular promise in plant genomics.This review systematically examines the progress of DL applications in DNA,RNA,and protein sequence analysis,covering key tasks such as gene regulatory element identification,gene function annotation,and protein structure prediction,and highlighting how these DL applications illuminate research of plants,including horticultural plants.We evaluate the advantages of different neural network architectures and their applications in different biology studies,as well as the development of large language models(LLMs)in genomic modelling,such as the plantspecific models PDLLMs and AgroNT.We also briefly introduce the general workflow of the basic DL model for plant genomics study.While DL has significantly improved prediction accuracy in plant genomics,its broader application remains constrained by several challenges,including the limited availability of well-annotated data,computational capacity,innovative model architectures adapted to plant genomes,and model interpretability.Future advances will require interdisciplinary collaborations to develop DL applications for intelligent plant genomic research frameworks with broader applicability.展开更多
Accurate chiller performance prediction is crucial for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems.Data-driven models commonly used to enhance chiller performance often rel...Accurate chiller performance prediction is crucial for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems.Data-driven models commonly used to enhance chiller performance often rely on sparse data collected under restricted conditions.These models must extrapolate beyond their training data in practical applications,but they generally lack the generalization capability needed for reliable predictions outside their training range.Additionally,their limited interpretability hampers understanding of the physical processes affecting chiller performance,complicating fault identification and performance optimization.To address these issues,this study embeds physical neurons in physics-informed neural networks(EP-PINNs)to enhance chiller performance prediction.By leveraging prior physical knowledge,physical neurons are introduced and embedded into the neural network,forming a neural network architecture with intrinsic physics-based information flow.Simultaneously,simplified physical loss terms are used to guide the training process.The proposed EP-PINNs were applied to predict the performance of four different chillers,and the results demonstrated their high prediction accuracy.Compared to data-driven models,the EP-PINNs exhibited significantly improved generalization capability and interpretability.These advantages highlight the practical value of EP-PINNs in HVAC equipment performance prediction.展开更多
In recent research,deep learning algorithms have presented effective representation learning models for natural languages.The deep learningbased models create better data representation than classical models.They are ...In recent research,deep learning algorithms have presented effective representation learning models for natural languages.The deep learningbased models create better data representation than classical models.They are capable of automated extraction of distributed representation of texts.In this research,we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module,and also addresses memory issues that were not addresses before.The proposed model employs a tree structured mechanism to generate the phrase and text embedding.The proposed architecture mimics the tree configuration of the text-texts and provide better feature representation.It also incorporates an attention mechanism that offers an additional information source to conduct better summary extraction.The novel model addresses text summarization as a classification process,where the model calculates the probabilities of phrase and text-summary association.The model classification is divided into multiple features recognition such as information entropy,significance,redundancy and position.The model was assessed on two datasets,on the Multi-Doc Composition Query(MCQ)and Dual Attention Composition dataset(DAC)dataset.The experimental results prove that our proposed model has better summarization precision vs.other models by a considerable margin.展开更多
In the domain of medical image analysis,there is a burgeoning recognition and adoption of large models distinguished by their extensive parameter count and intricate neural network architecture that is predominantly d...In the domain of medical image analysis,there is a burgeoning recognition and adoption of large models distinguished by their extensive parameter count and intricate neural network architecture that is predominantly due to their outstanding performance.This review article seeks to concisely explore the historical evolution,specific applications,and training methodologies associated with these large models considering their current prominence in medical image analysis.Moreover,we delve into the prevailing challenges and prospective opportunities related to the utilization of large models in the context of medical image analysis.Through a comprehensive analysis of these substantial models,this study aspires to provide valuable insights and guidance to researchers in the field of radiology,fostering further advances and optimizations in their incorporation into medical image analysis practices,in accordance with the submission requirements.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20210,31722048,and 32102382)Central Public-interest Scientific Institution Basal Research Fund,The Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural SciencesKey Laboratory of Biology and Genetic Improvement of Horticultural Crops,Ministry of Agriculture and Rural Affairs,China.
文摘With the rapid development of high-throughput sequencing technologies and the accumulation of large-scale multi-omics data,deep learning(DL)has emerged as a powerful tool to solve complex biological problems,with particular promise in plant genomics.This review systematically examines the progress of DL applications in DNA,RNA,and protein sequence analysis,covering key tasks such as gene regulatory element identification,gene function annotation,and protein structure prediction,and highlighting how these DL applications illuminate research of plants,including horticultural plants.We evaluate the advantages of different neural network architectures and their applications in different biology studies,as well as the development of large language models(LLMs)in genomic modelling,such as the plantspecific models PDLLMs and AgroNT.We also briefly introduce the general workflow of the basic DL model for plant genomics study.While DL has significantly improved prediction accuracy in plant genomics,its broader application remains constrained by several challenges,including the limited availability of well-annotated data,computational capacity,innovative model architectures adapted to plant genomes,and model interpretability.Future advances will require interdisciplinary collaborations to develop DL applications for intelligent plant genomic research frameworks with broader applicability.
基金supported by the National Natural Science Foundation of China(No.22441020).
文摘Accurate chiller performance prediction is crucial for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems.Data-driven models commonly used to enhance chiller performance often rely on sparse data collected under restricted conditions.These models must extrapolate beyond their training data in practical applications,but they generally lack the generalization capability needed for reliable predictions outside their training range.Additionally,their limited interpretability hampers understanding of the physical processes affecting chiller performance,complicating fault identification and performance optimization.To address these issues,this study embeds physical neurons in physics-informed neural networks(EP-PINNs)to enhance chiller performance prediction.By leveraging prior physical knowledge,physical neurons are introduced and embedded into the neural network,forming a neural network architecture with intrinsic physics-based information flow.Simultaneously,simplified physical loss terms are used to guide the training process.The proposed EP-PINNs were applied to predict the performance of four different chillers,and the results demonstrated their high prediction accuracy.Compared to data-driven models,the EP-PINNs exhibited significantly improved generalization capability and interpretability.These advantages highlight the practical value of EP-PINNs in HVAC equipment performance prediction.
基金This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R113),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In recent research,deep learning algorithms have presented effective representation learning models for natural languages.The deep learningbased models create better data representation than classical models.They are capable of automated extraction of distributed representation of texts.In this research,we introduce a new tree Extractive text summarization that is characterized by fitting the text structure representation in knowledge base training module,and also addresses memory issues that were not addresses before.The proposed model employs a tree structured mechanism to generate the phrase and text embedding.The proposed architecture mimics the tree configuration of the text-texts and provide better feature representation.It also incorporates an attention mechanism that offers an additional information source to conduct better summary extraction.The novel model addresses text summarization as a classification process,where the model calculates the probabilities of phrase and text-summary association.The model classification is divided into multiple features recognition such as information entropy,significance,redundancy and position.The model was assessed on two datasets,on the Multi-Doc Composition Query(MCQ)and Dual Attention Composition dataset(DAC)dataset.The experimental results prove that our proposed model has better summarization precision vs.other models by a considerable margin.
基金funding from the National Key Research and Development Program of China under Grant Nos.2021YFC2500402,2017YFA0700401,2022YFC2503700,and 2022YFC2503705the Ministry of Science and Technology of China under Grant No.2017YFA0205200+1 种基金the National Natural Science Foundation of China under Grant Nos.82001917,81930053,82090052,62027901,81227901,92159202,U22A2023,U22A20343,and 82172039the Project of High-Level Talents Team Introduction in Zhuhai City(Zhuhai HLHPTP201703).
文摘In the domain of medical image analysis,there is a burgeoning recognition and adoption of large models distinguished by their extensive parameter count and intricate neural network architecture that is predominantly due to their outstanding performance.This review article seeks to concisely explore the historical evolution,specific applications,and training methodologies associated with these large models considering their current prominence in medical image analysis.Moreover,we delve into the prevailing challenges and prospective opportunities related to the utilization of large models in the context of medical image analysis.Through a comprehensive analysis of these substantial models,this study aspires to provide valuable insights and guidance to researchers in the field of radiology,fostering further advances and optimizations in their incorporation into medical image analysis practices,in accordance with the submission requirements.
