The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources.The developments of artificial intellig...The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources.The developments of artificial intelligence(AI)and machine learning(ML)models assist in the effectual design of medical data classification models.Therefore,this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-toSequence Autoencoder(OSAE-LSTM)model for biomedical data classification.The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases.Primarily,the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform format.Followed by,the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical data.At last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization process.The utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM model.The simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTM model over the other approaches under several dimensions.展开更多
Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in healthcare.Biome-dical data is usually examined by physicians for decision making ...Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in healthcare.Biome-dical data is usually examined by physicians for decision making process in patient treatment.Since manual diagnosis is a tedious and time consuming task,numerous automated models,using Artificial Intelligence(AI)techniques,have been presented so far.With this motivation,the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI(BDC-CMBOAI)technique.The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data.Besides,the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection(CMBO-FS)technique to derive a useful subset of features.In addition,Ridge Regression(RR)model is also utilized as a classifier to identify the existence of disease.The novelty of the current work is its designing of CMBO-FS model for data classification.Moreover,CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy.The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.展开更多
The development of artificial intelligence(AI)and the mining of biomedical data complement each other.From the direct use of computer vision results to analyze medical images for disease screening,to now integrating b...The development of artificial intelligence(AI)and the mining of biomedical data complement each other.From the direct use of computer vision results to analyze medical images for disease screening,to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries,the boundaries of both are constantly expanding,and their connections are becoming closer.Therefore,the theme of the 2024 Annual Quantitative Biology Conference is set as“Biomedical Data and AI”,and was held in Chengdu,China from July 15 to 17,2024.展开更多
On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th Nation...On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.展开更多
National Population Health Data Center(NPHDC)is one of China's 20 national-level science data centers,jointly designated by the Ministry of Science and Technology and the Ministry of Finance.Operated by the Chines...National Population Health Data Center(NPHDC)is one of China's 20 national-level science data centers,jointly designated by the Ministry of Science and Technology and the Ministry of Finance.Operated by the Chinese Academy of Medical Sciences under the oversight of the National Health Commission,NPHDC adheres to national regulations including the Scientific Data Management Measures and the National Science and Technology Infrastructure Service Platform Management Measures,and is committed to collecting,integrating,managing,and sharing biomedical and health data through openaccess platform,fostering open sharing and engaging in international cooperation.展开更多
As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate...As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.展开更多
tmbalanced data is a common and serious problem in many biomedical classification tasks. It causes a bias on the training of classifiers and results in lower accuracy of minority classes prediction. This problem has a...tmbalanced data is a common and serious problem in many biomedical classification tasks. It causes a bias on the training of classifiers and results in lower accuracy of minority classes prediction. This problem has attracted a lot of research interests in the past decade. Unfortunately, most research efforts only concentrate on 2-class problems. In this paper, we study a new method of formulating a multiclass Support Vector Machine （SVM） problem for imbalanced biomedical data to improve the classification performance. The proposed method applies cost-sensitive approach and ramp loss function to the Crammer and Singer multiclass SVM formulation. Experimental results on multiple biomedical datasets show that the proposed solution can effectively cure the problem when the datasets are noisy and highly imbalanced.展开更多
In recent years, there have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. The biomedical data can be analyzed to enhance asses...In recent years, there have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. The biomedical data can be analyzed to enhance assessment of at-risk patients and improve disease diagnosis, treatment, and prevention. However, these datasets usually have many features, which contain many irrelevant or redundant information. Feature selection is a solution that involves finding the optimal subset, which is known to be an NP problem because of the large search space. Considering this, a new feature selection approach based on Binary Chemical Reaction Optimization algorithm （BCRO） and k-Nearest Neighbors （KNN） classifier is presented in this paper. Tabu search is integrated with CRO framework to enhance local search capacity. KNN is adopted to evaluate the quality of selected candidate subset. The results for an experiment conducted on nine standard medical datasets demonstrate that the proposed approach outperforms other state-of-the-art methods.展开更多
Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security asses...Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security assessment and risk management,many countries have successively issued relevant laws,regulations,and assessment guidelines.This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement.From the perspective of a single organization,the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors.Then,the risk assessment index system of cross-border data flow is constructed.A case study of risk assessment in 358 biomedical organizations is carried out,and the suggestions for data management are offered.This study is condusive to improving security monitoring and the early warning of the cross-border data flow,thereby realizing the safe and orderly global flow of biomedical data.展开更多
Atherosclerosis diagnosis is an inarticulate and complicated cognitive process.Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions.Since the medical diagnost...Atherosclerosis diagnosis is an inarticulate and complicated cognitive process.Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions.Since the medical diagnostic outcomes need to be prompt and accurate,the recently developed artificial intelligence(AI)and deep learning(DL)models have received considerable attention among research communities.This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification(MDL-BADDC)model.The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing,feature selection,classification,and parameter tuning.Besides,the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer(QOBMO)based feature selection technique.Moreover,the deep stacked autoencoder(DSAE)based classification model is designed for the detection and classification of atherosclerosis disease.Furthermore,the krill herd algorithm(KHA)based parameter tuning technique is applied to properly adjust the parameter values.In order to showcase the enhanced classification performance of the MDL-BADDC technique,a wide range of simulations take place on three benchmarks biomedical datasets.The comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.展开更多
With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-base...With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.展开更多
The hematopoietic system has long served as an excellent model for biological and medical research,owing to its highly organized hierarchical structure,accessibility for sampling,and rapid cellular turnover.These feat...The hematopoietic system has long served as an excellent model for biological and medical research,owing to its highly organized hierarchical structure,accessibility for sampling,and rapid cellular turnover.These features have enabled pivotal discoveries in stem cell biology,oncogenic transformation,and targeted therapies,exemplified by milestones such as the identification of the BCR-ABL fusion gene and the successful development of molecular-targeted treatments.展开更多
AI is revolutionizing the current paradigm of pharmaceutical research,addressing the challenges encountered at all stages of the process.AI driven drug discovery is based on biomedical big data and new algorithms to i...AI is revolutionizing the current paradigm of pharmaceutical research,addressing the challenges encountered at all stages of the process.AI driven drug discovery is based on biomedical big data and new algorithms to identify drug targets,screen and optimize active compounds,analyze drug properties,and facilitate drug production and quality control.展开更多
Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and communication.Identifying ASD as soon as possible is f...Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and communication.Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD.Recognition of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were affected.Nowadays,healthcare and machine learning(ML)industries are combined for determining the existence of various diseases.This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)model.The goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical data.The proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform range.In addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature subsets.Moreover,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ASD.Furthermore,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU system.The experimental assessment of the JSODL-ASDDC model is investigated against distinct datasets.The experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.展开更多
The promise that big data will revolutionize scientific discovery and technology innovation is now being widely recognized. With the explosive growth of biomedical data, life science is being transformed into a digita...The promise that big data will revolutionize scientific discovery and technology innovation is now being widely recognized. With the explosive growth of biomedical data, life science is being transformed into a digital science in which novel insights are gained from in-depth data analysis and modeling. Extensive and innovative utilization of biomedical big data is a key to the success of precision medicine. Therefore, constructing a centralized national-level biomedical big data infrastructure becomes crucial and urgent for China. Such infrastructure should achieve superb capacity of safe data storage, standardized data processing and quality control, systematic data integration across multiple types, and in-depth data mining and effective data sharing. Full data chain service including information retrieval, knowledge discovery and technology support can be provided to data centers, research institutes and healthcare industries. Relying on Shanghai Institutes for Biological Sciences, agreements have been signed that a main node of the infrastructure will be located in Shanghai, and a backup node will be set up in Guizhou Province. After a construction period of five years, the infrastructure should greatly enhance China＇s core competence in collection, interpretation and application of biomedical big data.展开更多
Alzheimer's disease(AD)is characterized by cognitive and functional deterioration,with pathological features such as amyloid-beta(Aβ)aggregates in the extracellular spaces of parenchymal neurons and intracellular...Alzheimer's disease(AD)is characterized by cognitive and functional deterioration,with pathological features such as amyloid-beta(Aβ)aggregates in the extracellular spaces of parenchymal neurons and intracellular neurofibrillary tangles formed by the hyperphosphorylation of tau protein.Despite a thorough investigation,current treatments targeting the reduction of Aβproduction,promotion of its clearance,and inhibition of tau protein phosphorylation and aggregation have not met clinical expectations,posing a substantial obstacle in the development of drugs for AD.Recently,artificial intelligence(AI),computational biology(CB),and systems biology(SB)have emerged as promising methodologies in AD research.Their capacity to analyze extensive and varied datasets facilitates the identification of intricate patterns,thereby enriching our comprehension of AD pathology.This paper provides a comprehensive examination of the utilization of AI,CB,and SB in the diagnosis of AD,including the use of imaging omics for early detection,drug discovery methods such as lecanemab,and complementary therapies like phototherapy.This review offers novel perspectives and potential avenues for further research in the realm of translational AD studies.展开更多
It is challenging to identify comorbidity patterns and mechanistically investigate disease associations based on health-related data that are often sparse,large-scale,and multimodal.Adopting a systems biology approach...It is challenging to identify comorbidity patterns and mechanistically investigate disease associations based on health-related data that are often sparse,large-scale,and multimodal.Adopting a systems biology approach,embedding-based algorithms provide a new perspective to examine diseases under a unified framework by mapping diseases into a highdimensional space as embedding vectors.These vectors and their constituted disease space encode pathological information and enable a quantitative and systemic measurement of the similarity between any pair of diseases,opening up an avenue for numerous types of downstream analyses.Here,we exemplify its potential through applications in discovering hidden disease associations,assisting in genetic parameter estimation,facilitating data-driven disease classifications,and transforming genetic association studies of diseases in consideration of comorbidities.While underscoring the power and versatility of this approach,we also discuss the challenges posed by medical context,requirements of online training and result validation,and research opportunities in constructing foundation models from multimodal disease data.With continued innovation and exploration,disease embedding has the potential to transform the fields of disease association analysis and even pathology studies by providing a holistic representation of patient health status.展开更多
Since the boom of biomedical big data studies,various big data processing technologies have been developed rapidly.As an important form of knowledge representation,ontology has become an important means for the utiliz...Since the boom of biomedical big data studies,various big data processing technologies have been developed rapidly.As an important form of knowledge representation,ontology has become an important means for the utilization and integration of biomedical big data.The emergence of new technologies for ontology development has resulted in the generation of many biomedical ontologies by many ontology development communities.The Open Biological and Biomedical Ontology Foundry,an academic organization for bio-ontology developers,has provided a set of principles to guide community-based open ontology construction.The Open Biological and Biomedical Ontology Foundry have also built many widely used ontologies,such as Gene Ontology,Human Phenotype Ontology,and Chemical Entities of Biological Interest.Other various ontology repositories have also been created and used to support ontology reuse.Many efficient tools for ontology applications,such as data annotation and terms mapping,have also been developed.High quality ontologies are also being used to develop new methods and tools for biomedical data analysis.The applications of Gene Ontology and Human Phenotype Ontology for data analysis and integration in recent years are reviewed here.To promote the development and applications of biomedical ontologies in China,a research community,OntoChina,was founded recently.OntoChina aims to support the development of reference ontologies,especially bilingual and Chinese translated ontologies.OntoChina also encourages ontology developers to follow the Open Biological and Biomedical Ontology Foundry principles.展开更多
Researchers and practitioners are increasingly interested in the application of artificial intelligence(AI)to drive advancements in the pharmaceutical sector and elevate it to the required level.The pharmaceutical sec...Researchers and practitioners are increasingly interested in the application of artificial intelligence(AI)to drive advancements in the pharmaceutical sector and elevate it to the required level.The pharmaceutical sector is significantly impacted by drug research and discovery,which also has an impact on several human health problems.AI has been a key instrument in the analysis of a large volume of high-dimensional data in recent years because of progress in experimental techniques and computer hardware.Due to the exponential increase in the volume of biomedical data,it is beneficial to integrate AI in all phases of pharmacological research and development.AI’s capacity to find novel treatments more quickly and cheaply has enabled big data in biomedicine to drive a revolution in drug research and development.The use of AI in the pharmaceutical sector has developed over the past several years and is predicted to become more widespread.AI can improve drug development processes and formulations while saving time and money.This study aims to help determine the extent to which using AI in pharmaceuticals enhances health care results and patient-specific treatment.In addition to this in-depth examination,this study highlights the potential of AI,related issues,and its future application in the pharmaceutical industry.展开更多
With the progression of modern information techniques,such as next generation sequencing(NGS),Internet of Everything(IoE)based smart sensors,and artificial intelligence algorithms,data-intensive research and applicati...With the progression of modern information techniques,such as next generation sequencing(NGS),Internet of Everything(IoE)based smart sensors,and artificial intelligence algorithms,data-intensive research and applications are emerging as the fourth paradigm for scientific discovery.However,we facemany challenges to practical application of this paradigm.In this article,10 challenges to data-intensive discovery and applications in precision medicine and healthcare are summarized and the future perspectives on next generation medicine are discussed.展开更多
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/158/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R235)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4340237DSR06).
文摘The biomedical data classification process has received significant attention in recent times due to a massive increase in the generation of healthcare data from various sources.The developments of artificial intelligence(AI)and machine learning(ML)models assist in the effectual design of medical data classification models.Therefore,this article concentrates on the development of optimal Stacked Long Short Term Memory Sequence-toSequence Autoencoder(OSAE-LSTM)model for biomedical data classification.The presented OSAE-LSTM model intends to classify the biomedical data for the existence of diseases.Primarily,the OSAE-LSTM model involves min-max normalization based pre-processing to scale the data into uniform format.Followed by,the SAE-LSTM model is utilized for the detection and classification of diseases in biomedical data.At last,manta ray foraging optimization(MRFO)algorithm has been employed for hyperparameter optimization process.The utilization of MRFO algorithm assists in optimal selection of hypermeters involved in the SAE-LSTM model.The simulation analysis of the OSAE-LSTM model has been tested using a set of benchmark medical datasets and the results reported the improvements of the OSAELSTM model over the other approaches under several dimensions.
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R203)Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:22UQU4340237DSR03.
文摘Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in healthcare.Biome-dical data is usually examined by physicians for decision making process in patient treatment.Since manual diagnosis is a tedious and time consuming task,numerous automated models,using Artificial Intelligence(AI)techniques,have been presented so far.With this motivation,the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI(BDC-CMBOAI)technique.The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data.Besides,the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection(CMBO-FS)technique to derive a useful subset of features.In addition,Ridge Regression(RR)model is also utilized as a classifier to identify the existence of disease.The novelty of the current work is its designing of CMBO-FS model for data classification.Moreover,CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy.The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.
文摘The development of artificial intelligence(AI)and the mining of biomedical data complement each other.From the direct use of computer vision results to analyze medical images for disease screening,to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries,the boundaries of both are constantly expanding,and their connections are becoming closer.Therefore,the theme of the 2024 Annual Quantitative Biology Conference is set as“Biomedical Data and AI”,and was held in Chengdu,China from July 15 to 17,2024.
文摘On October 18,2017,the 19th National Congress Report called for the implementation of the Healthy China Strategy.The development of biomedical data plays a pivotal role in advancing this strategy.Since the 18th National Congress of the Communist Party of China,China has vigorously promoted the integration and implementation of the Healthy China and Digital China strategies.The National Health Commission has prioritized the development of health and medical big data,issuing policies to promote standardized applica-tions and foster innovation in"Internet+Healthcare."Biomedical data has significantly contributed to preci-sion medicine,personalized health management,drug development,disease diagnosis,public health monitor-ing,and epidemic prediction capabilities.
文摘National Population Health Data Center(NPHDC)is one of China's 20 national-level science data centers,jointly designated by the Ministry of Science and Technology and the Ministry of Finance.Operated by the Chinese Academy of Medical Sciences under the oversight of the National Health Commission,NPHDC adheres to national regulations including the Scientific Data Management Measures and the National Science and Technology Infrastructure Service Platform Management Measures,and is committed to collecting,integrating,managing,and sharing biomedical and health data through openaccess platform,fostering open sharing and engaging in international cooperation.
基金supported by the China Fundamental Research Funds for the Central Universities(No.2662022XXYJ001,2662022JC004,2662023XXPY005)。
文摘As a new data management paradigm,knowledge graphs can integrate multiple data sources and achieve quick responses,reasoning and better predictions in drug discovery.Characterized by powerful contagion and a high rate of morbidity and mortality,porcine reproductive and respiratory syndrome(PRRS)is a common infectious disease in the global swine industry that causes economically great losses.Traditional Chinese medicine(TCM)has advantages in low adverse effects and a relatively affordable cost of application,and TCM is therefore conceived as a possibility to treat PRRS under the current circumstance that there is a lack of safe and effective approaches.Here,we constructed a knowledge graph containing common biomedical data from humans and Sus Scrofa as well as information from thousands of TCMs.Subsequently,we validated the effectiveness of the Sus Scrofa knowledge graph by the t-SNE algorithm and selected the optimal model(i.e.,transR)from six typical models,namely,transE,transR,DistMult,ComplEx,RESCAL and RotatE,according to five indicators,namely,MRR,MR,HITS@1,HITS@3 and HITS@10.Based on embedding vectors trained by the optimal model,anti-PRRSV TCMs were predicted by two paths,namely,VHC-Herb and VHPC-Herb,and potential anti-PRRSVTCMs were identified by retrieving the HERB database according to the phar-macological properties corresponding to symptoms of PRRS.Ultimately,Dan Shen's(Salvia miltiorrhiza Bunge)capacity to resist PRRSV infection was validated by a cell experiment in which the inhibition rate of PRRSV exceeded90%when the concentrations of Dan Shen extract were 0.004,0.008,0.016 and 0.032 mg/mL.In summary,this is the first report on the Sus Scrofa knowledge graph including TCM information,and our study reflects the important application values of deep learning on graphs in the swine industry as well as providing accessible TCM resources for PRRS.
基金Supported by GSU Molecular Basis of Disease Graduate Fellow, 2011-2012
文摘tmbalanced data is a common and serious problem in many biomedical classification tasks. It causes a bias on the training of classifiers and results in lower accuracy of minority classes prediction. This problem has attracted a lot of research interests in the past decade. Unfortunately, most research efforts only concentrate on 2-class problems. In this paper, we study a new method of formulating a multiclass Support Vector Machine （SVM） problem for imbalanced biomedical data to improve the classification performance. The proposed method applies cost-sensitive approach and ramp loss function to the Crammer and Singer multiclass SVM formulation. Experimental results on multiple biomedical datasets show that the proposed solution can effectively cure the problem when the datasets are noisy and highly imbalanced.
基金supported in part by the Natural Science Foundation of Henan Province(No.14A520042)Scientific Research Foundation of the Higher Education Institutions of Henan Province(No.18A520021)+1 种基金the National Natural Science Foundation of China(No.61802114)the National Key Technology R&D Program of China(No.2015BAK01B06)
文摘In recent years, there have been rapid developments in various bioinformatics technologies, which have led to the accumulation of a large amount of biomedical data. The biomedical data can be analyzed to enhance assessment of at-risk patients and improve disease diagnosis, treatment, and prevention. However, these datasets usually have many features, which contain many irrelevant or redundant information. Feature selection is a solution that involves finding the optimal subset, which is known to be an NP problem because of the large search space. Considering this, a new feature selection approach based on Binary Chemical Reaction Optimization algorithm （BCRO） and k-Nearest Neighbors （KNN） classifier is presented in this paper. Tabu search is integrated with CRO framework to enhance local search capacity. KNN is adopted to evaluate the quality of selected candidate subset. The results for an experiment conducted on nine standard medical datasets demonstrate that the proposed approach outperforms other state-of-the-art methods.
基金support from the National Natural Science Foundation of China(Grant No.:71901169)the Shaanxi Province Innovative Talents Promotion Plan-Youth Science and Technology Nova Project(Grant No.:2022KJXX-50).
文摘Cross-border data transmission in the biomedical area is on the rise,which brings potential risks and management challenges to data security,biosafety,and national security.Focusing on cross-border data security assessment and risk management,many countries have successively issued relevant laws,regulations,and assessment guidelines.This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement.From the perspective of a single organization,the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors.Then,the risk assessment index system of cross-border data flow is constructed.A case study of risk assessment in 358 biomedical organizations is carried out,and the suggestions for data management are offered.This study is condusive to improving security monitoring and the early warning of the cross-border data flow,thereby realizing the safe and orderly global flow of biomedical data.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 2/279/43)Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R151),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Atherosclerosis diagnosis is an inarticulate and complicated cognitive process.Researches on medical diagnosis necessitate maximum accuracy and performance to make optimal clinical decisions.Since the medical diagnostic outcomes need to be prompt and accurate,the recently developed artificial intelligence(AI)and deep learning(DL)models have received considerable attention among research communities.This study develops a novel Metaheuristics with Deep Learning Empowered Biomedical Atherosclerosis Disease Diagnosis and Classification(MDL-BADDC)model.The proposed MDL-BADDC technique encompasses several stages of operations such as pre-processing,feature selection,classification,and parameter tuning.Besides,the proposed MDL-BADDC technique designs a novel Quasi-Oppositional Barnacles Mating Optimizer(QOBMO)based feature selection technique.Moreover,the deep stacked autoencoder(DSAE)based classification model is designed for the detection and classification of atherosclerosis disease.Furthermore,the krill herd algorithm(KHA)based parameter tuning technique is applied to properly adjust the parameter values.In order to showcase the enhanced classification performance of the MDL-BADDC technique,a wide range of simulations take place on three benchmarks biomedical datasets.The comparative result analysis reported the better performance of the MDL-BADDC technique over the compared methods.
基金the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups Project under Grant Number(71/43)Princess Nourah Bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R203)Princess Nourah Bint Abdulrahman University,Riyadh,Saudi Arabia.The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4310373DSR29).
文摘With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.
基金supported by the National Key R&D Program of China(Grant Nos.2022YFF1202004,2022YFC2503304,and 2022YFC2406803)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA0460403)+1 种基金the National Natural Science Foundation of China(Grant Nos.82270126 and 81870097)the Beijing Natural Science Foundation(Grant No.7252091).
文摘The hematopoietic system has long served as an excellent model for biological and medical research,owing to its highly organized hierarchical structure,accessibility for sampling,and rapid cellular turnover.These features have enabled pivotal discoveries in stem cell biology,oncogenic transformation,and targeted therapies,exemplified by milestones such as the identification of the BCR-ABL fusion gene and the successful development of molecular-targeted treatments.
文摘AI is revolutionizing the current paradigm of pharmaceutical research,addressing the challenges encountered at all stages of the process.AI driven drug discovery is based on biomedical big data and new algorithms to identify drug targets,screen and optimize active compounds,analyze drug properties,and facilitate drug production and quality control.
文摘Autism spectrum disorder(ASD)is regarded as a neurological disorder well-defined by a specific set of problems associated with social skills,recurrent conduct,and communication.Identifying ASD as soon as possible is favourable due to prior identification of ASD permits prompt interferences in children with ASD.Recognition of ASD related to objective pathogenicmutation screening is the initial step against prior intervention and efficient treatment of children who were affected.Nowadays,healthcare and machine learning(ML)industries are combined for determining the existence of various diseases.This article devises a Jellyfish Search Optimization with Deep Learning Driven ASD Detection and Classification(JSODL-ASDDC)model.The goal of the JSODL-ASDDC algorithm is to identify the different stages of ASD with the help of biomedical data.The proposed JSODLASDDC model initially performs min-max data normalization approach to scale the data into uniform range.In addition,the JSODL-ASDDC model involves JSO based feature selection(JFSO-FS)process to choose optimal feature subsets.Moreover,Gated Recurrent Unit(GRU)based classification model is utilized for the recognition and classification of ASD.Furthermore,the Bacterial Foraging Optimization(BFO)assisted parameter tuning process gets executed to enhance the efficacy of the GRU system.The experimental assessment of the JSODL-ASDDC model is investigated against distinct datasets.The experimental outcomes highlighted the enhanced performances of the JSODL-ASDDC algorithm over recent approaches.
文摘The promise that big data will revolutionize scientific discovery and technology innovation is now being widely recognized. With the explosive growth of biomedical data, life science is being transformed into a digital science in which novel insights are gained from in-depth data analysis and modeling. Extensive and innovative utilization of biomedical big data is a key to the success of precision medicine. Therefore, constructing a centralized national-level biomedical big data infrastructure becomes crucial and urgent for China. Such infrastructure should achieve superb capacity of safe data storage, standardized data processing and quality control, systematic data integration across multiple types, and in-depth data mining and effective data sharing. Full data chain service including information retrieval, knowledge discovery and technology support can be provided to data centers, research institutes and healthcare industries. Relying on Shanghai Institutes for Biological Sciences, agreements have been signed that a main node of the infrastructure will be located in Shanghai, and a backup node will be set up in Guizhou Province. After a construction period of five years, the infrastructure should greatly enhance China＇s core competence in collection, interpretation and application of biomedical big data.
基金funded by the Major Program of the National Natural Science Foundation of China(62394314)the National Natural Science Foundation of China(82071214)the Fund from Kangning Hospital(SLC202304,China).
文摘Alzheimer's disease(AD)is characterized by cognitive and functional deterioration,with pathological features such as amyloid-beta(Aβ)aggregates in the extracellular spaces of parenchymal neurons and intracellular neurofibrillary tangles formed by the hyperphosphorylation of tau protein.Despite a thorough investigation,current treatments targeting the reduction of Aβproduction,promotion of its clearance,and inhibition of tau protein phosphorylation and aggregation have not met clinical expectations,posing a substantial obstacle in the development of drugs for AD.Recently,artificial intelligence(AI),computational biology(CB),and systems biology(SB)have emerged as promising methodologies in AD research.Their capacity to analyze extensive and varied datasets facilitates the identification of intricate patterns,thereby enriching our comprehension of AD pathology.This paper provides a comprehensive examination of the utilization of AI,CB,and SB in the diagnosis of AD,including the use of imaging omics for early detection,drug discovery methods such as lecanemab,and complementary therapies like phototherapy.This review offers novel perspectives and potential avenues for further research in the realm of translational AD studies.
基金National Natural Science Foundation of China,Grant/Award Number:32470720Innovation Program of Chinese Academy of Agricultural Sciences,Grant/Award Number:CAASASTIP-2021-AGIS+13 种基金Chinese University of Hong KongGrant/Award Numbers:4937025,4937026,5501517,5501329Research Grants Council of the Hong Kong Special Administrative RegionChinaGrant/Award Number:CUHK 24204023Innovation and Technology Commission of the Hong Kong Special Administrative Region,ChinaGrant/Award Number:GHP/065/21SZRMGS in CUHK,Grant/Award Numbers:8601603,8601663Shun Hing Institute of Advanced Engineering(SHIAE),Grant/Award Number:BME-p1-24The King Abdullah University of Science and Technology Office of Research Administration,Grant/Award Numbers:REI/1/5234-01-01,REI/1/5289-01-01,REI/1/5404-01-01,REI/1/5414-01-01,REI/1/5992-01-01,URF/1/4663-01-01The King Abdullah University of Science and Technology Center of Excellence for Smart Health(KCSH)Grant/Award Number:5932The King Abdullah University of Science and Technology Center of Excellence on Generative AIGrant/Award Number:5940。
文摘It is challenging to identify comorbidity patterns and mechanistically investigate disease associations based on health-related data that are often sparse,large-scale,and multimodal.Adopting a systems biology approach,embedding-based algorithms provide a new perspective to examine diseases under a unified framework by mapping diseases into a highdimensional space as embedding vectors.These vectors and their constituted disease space encode pathological information and enable a quantitative and systemic measurement of the similarity between any pair of diseases,opening up an avenue for numerous types of downstream analyses.Here,we exemplify its potential through applications in discovering hidden disease associations,assisting in genetic parameter estimation,facilitating data-driven disease classifications,and transforming genetic association studies of diseases in consideration of comorbidities.While underscoring the power and versatility of this approach,we also discuss the challenges posed by medical context,requirements of online training and result validation,and research opportunities in constructing foundation models from multimodal disease data.With continued innovation and exploration,disease embedding has the potential to transform the fields of disease association analysis and even pathology studies by providing a holistic representation of patient health status.
基金This work was supported by Chinese Academy of Medical Science(CAMS)Innovation Fund for Medical Sciences(CIFMS)(No.2018-I2M-AI-009 to XY)Independent Subject Project Funded by Basic Scientific Research Fund of Chinese Academy of Chinese Medical Science(No.zz110318 to YZ)the University of Michigan Global Reach Award(to YH).
文摘Since the boom of biomedical big data studies,various big data processing technologies have been developed rapidly.As an important form of knowledge representation,ontology has become an important means for the utilization and integration of biomedical big data.The emergence of new technologies for ontology development has resulted in the generation of many biomedical ontologies by many ontology development communities.The Open Biological and Biomedical Ontology Foundry,an academic organization for bio-ontology developers,has provided a set of principles to guide community-based open ontology construction.The Open Biological and Biomedical Ontology Foundry have also built many widely used ontologies,such as Gene Ontology,Human Phenotype Ontology,and Chemical Entities of Biological Interest.Other various ontology repositories have also been created and used to support ontology reuse.Many efficient tools for ontology applications,such as data annotation and terms mapping,have also been developed.High quality ontologies are also being used to develop new methods and tools for biomedical data analysis.The applications of Gene Ontology and Human Phenotype Ontology for data analysis and integration in recent years are reviewed here.To promote the development and applications of biomedical ontologies in China,a research community,OntoChina,was founded recently.OntoChina aims to support the development of reference ontologies,especially bilingual and Chinese translated ontologies.OntoChina also encourages ontology developers to follow the Open Biological and Biomedical Ontology Foundry principles.
文摘Researchers and practitioners are increasingly interested in the application of artificial intelligence(AI)to drive advancements in the pharmaceutical sector and elevate it to the required level.The pharmaceutical sector is significantly impacted by drug research and discovery,which also has an impact on several human health problems.AI has been a key instrument in the analysis of a large volume of high-dimensional data in recent years because of progress in experimental techniques and computer hardware.Due to the exponential increase in the volume of biomedical data,it is beneficial to integrate AI in all phases of pharmacological research and development.AI’s capacity to find novel treatments more quickly and cheaply has enabled big data in biomedicine to drive a revolution in drug research and development.The use of AI in the pharmaceutical sector has developed over the past several years and is predicted to become more widespread.AI can improve drug development processes and formulations while saving time and money.This study aims to help determine the extent to which using AI in pharmaceuticals enhances health care results and patient-specific treatment.In addition to this in-depth examination,this study highlights the potential of AI,related issues,and its future application in the pharmaceutical industry.
基金This work was supported by the regional innovation cooperation between Sichuan and Guangxi Provinces(Grant No.2020YFQ0019)the National Natural Science Foundation of China(Grant No.32070671).
文摘With the progression of modern information techniques,such as next generation sequencing(NGS),Internet of Everything(IoE)based smart sensors,and artificial intelligence algorithms,data-intensive research and applications are emerging as the fourth paradigm for scientific discovery.However,we facemany challenges to practical application of this paradigm.In this article,10 challenges to data-intensive discovery and applications in precision medicine and healthcare are summarized and the future perspectives on next generation medicine are discussed.
