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PubMed1998

Diagnosing community-acquired pneumonia with a Bayesian network.

We present the development and the evaluation of a Bayesian network for the diagnosis of community-acquired pneumonia. The Bayesian network is intended to be part of a larger decision support system which assists emergency room physicians in the management of pneumonia patients. Minimal data entry from the nurse or the physician, timely availability of clinical parameters, and high accuracy were requirements we tried to meet. Data from more than 32,000 emergency room patients over a period of 2 years (June 1995-June 1997) were extracted from the clinical information system to train and test the Bayesian network. The network performed well in discriminating patients with pneumonia from patients with other diseases. The Bayesian network achieved a sensitivity of 95%, a specificity of 96.5%, an area under the receiver operating characteristic of 0.98, and a predictive value positive of 26.8%. Our feasibility study demonstrates that the proposed Bayesian network is an appropriate method to detect pneumonia patients with high accuracy. The study suggests that the proposed Bayesian network may represent a successful component within a larger decision support system for the management of community-acquired pneumonia.

Adult↗
PubMed1997

A network of web multimedia medical information servers for a medical school and university hospital.

Modern medicine requires a rapid access to information including clinical data from medical records, bibliographic databases, knowledge bases and nomenclature databases. This is especially true for University Hospitals and Medical Schools for training as well as for fundamental and clinical research for diagnosis and therapeutic purposes. This implies the development of local, national and international cooperation which can be enhanced via the use and access to computer networks such as Internet. The development of professional cooperative networks goes with the development of the telecommunication and computer networks and our project is to make these new tools and technologies accessible to the medical students both during the teaching time in Medical School and during the training periods at the University Hospital. We have developed a local area network which communicates between the School of Medicine and the Hospital which takes advantage of the new Web client-server technology both internally (Intranet) and externally by access to the National Research Network (RENATER in France) connected to the Internet network. The address of our public web server is http:(/)/www.med.univ-rennesl.fr.

Artificial Intelligence↗
PubMed2026

Exploring the mechanism of Acanthopanax in treating vertigo: A network pharmacology and molecular docking study.

Acanthopanax has therapeutic efficacy against vertigo; however, the underlying mechanism remains unclear. This study aimed to elucidate the mechanism by which Acanthopanax treats vertigo through integrated network pharmacology and molecular docking techniques, and retrieved all target genes of Acanthopanax for vertigo treatment from July to October 2025. Vertigo-related target genes were subsequently identified from public databases, including GeneCards and Online Mendelian Inheritance in Man. The intersection between Acanthopanax-derived targets and vertigo-related targets was analyzed to identify candidate target genes. Using the STRING platform, we constructed protein-protein interaction networks for the identified candidate targets and mined the core functional modules within these networks. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on candidate targets via the clusterProfiler package. A carp bile poisoning-liver injury target-pathway network was constructed via Cytoscape 3.8.2 software, network topology analysis was conducted, and the core components and targets were screened. The results found that A total of 295 candidate targets for the treatment of vertigo caused by Eleutherococcus senticosus were identified. Pathway enrichment analysis revealed that Eleutherococcus senticosus treatment for vertigo may be closely associated with pathways related to IL-17, TNF, phosphoinositide 3-kinase (PI3K)-Akt, p53, HIF-1, and Forkhead box O signaling. The core targets for the treatment of A. senticosus vertigo include TP53, AKT1, STAT3, TNF, and JUN. Network pharmacology and molecular docking studies suggest that A. senticosus may treat vertigo by regulating targets such as JUN, TNF, AKT1, STAT3, and STAT3 through pathways such as the IL-17, TNF, phosphoinositide 3-kinase-Akt, p53, HIF-1, and Forkhead box O signaling pathways. These mechanisms warrant further investigation in future o and in vitro studies.

Molecular Docking Simulation↗
PubMed2026

Acupoint Selection Patterns and Potential Mechanisms of Acupuncture in Knee Osteoarthritis: A Combined Data Mining and Network Pharmacology Study.

OBJECTIVE: To identify the core acupoint prescription and Kellgren-Lawrence (K-L) grade-dependent compatibility patterns of acupuncture for KOA through complex network analysis, and to predict the potential molecular mechanisms underlying the core prescription via network pharmacology. METHODS: Literature was retrieved from PubMed, EMbase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP, and SinoMed (inception to September 3, 2025). Frequency, association rule, complex network, and K-L grade subgroup analyses were applied. Potential targets of the core prescription were identified via network pharmacology and intersected with disease targets from OMIM, Therapeutic Target, GeneCards, and DrugBank. A protein-protein interaction (PPI) network was constructed, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the potential molecular mechanisms. RESULTS: We included 522 studies, yielding 582 prescriptions involving 123 acupoints. The core prescription comprised 24 acupoints, including Dubi (ST35), Neixiyan (EX-LE4), Liangqiu (ST34), Xuehai (SP10), Zusanli (ST36), Yanglingquan (GB34), Yinlingquan (SP9), among others. K-L subgroup analysis revealed ST35, GB34, SP9, and SP10 as universal core acupoints. The mild-to-moderate subgroup mainly used local acupoints, while the moderate-to-severe subgroup centered on ST35, with increased distal acupoint usage and higher degree values. Network pharmacology analysis identified 77 overlapping targets. Core targets included tumor necrosis factor (TNF), interleukin 6 (IL6), interleukin 1 beta (IL1B), tumor protein p53 (TP53), matrix metallopeptidase 9 (MMP9), signal transducer and activator of transcription 3 (STAT3), transforming growth factor beta 1 (TGFB1), caspase 3 (CASP3), and B-cell lymphoma 2 (BCL2), which were enriched in inflammation and immunity, cartilage metabolism, and tissue repair pathways. CONCLUSION: The core acupoint prescription for KOA features local acupoints combined with distal ones, exhibiting distinct patterns across K-L grades. Our computational findings suggest that core acupoints may potentially delay knee joint degeneration by synergistically regulating inflammation, cartilage metabolism, apoptosis, and tissue repair, although these predictions require experimental validation. These findings provide preliminary evidence and a theoretical basis for standardized clinical point selection and further mechanistic research.

KOA↗
PubMed2026

Elucidating the Mechanism of Xiaoqinglong Decoction in Chronic Urticaria Treatment: An Integrated Approach of Network Pharmacology, Bioinformatics Analysis, Molecular Docking, and Molecular Dynamics Simulations.

INTRODUCTION: Xiaoqinglong Decoction (XQLD) is a traditional Chinese medicinal formula commonly used to treat chronic urticaria (CU). However, its underlying therapeutic mechanisms remain incompletely characterized. This study employed an integrated approach combining network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations to identify the active components, potential targets, and related signaling pathways involved in XQLD's therapeutic action against CU, thereby providing a mechanistic foundation for its clinical application. METHODS: The active components of XQLD and their corresponding targets were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CU-related targets were retrieved from the OMIM and GeneCards databases. Subsequently, core components and targets were determined via protein-protein interaction (PPI) network analysis and component-target-pathway network construction. Topological analyses were performed using Cytoscape software to prioritize core nodes within these networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database to identify enriched biological processes and signaling pathways. Molecular docking was performed to evaluate binding interactions between key components and core targets, while molecular dynamics (MD) simulations were employed to assess the stability of the component-target complexes with the lowest binding energy. Finally, CU-related targets of XQLD were validated using datasets from the Gene Expression Omnibus (GEO) database. RESULTS: A total of 135 active components and 249 potential targets of XQLD were identified, alongside 1,711 CU-related targets. Core components, such as quercetin, kaempferol, beta-sitosterol, naringenin, stigmasterol, and luteolin, exhibited high degree values in the constructed networks. The core targets identified included AKT1, TNF, IL6, TP53, PTGS2, CASP3, BCL2, ESR1, PPARG, and MAPK3. GO and KEGG pathway enrichment analyses revealed the PI3K-Akt signaling pathway as a central regulatory mechanism. Molecular docking studies demonstrated strong binding affinities between active components and core targets, with the stigmasterol-AKT1 complex exhibiting the lowest binding energy (-11.4 kcal/mol) and high stability in MD simulations. Validation using GEO datasets identified 12 core genes shared between CU-related targets and XQLD-associated targets, including PTGS2 and IL6, which were also prioritized as core targets in the network pharmacology analyses. DISCUSSION: This study comprehensively integrates multidisciplinary approaches to clarify the potential molecular mechanisms of XQLD in treating CU, highlighting its multitarget and multipathway synergistic effects. Molecular docking and dynamics simulations confirm the stable interaction between stigmasterol and the core target AKT1. Additionally, GEO dataset analysis verifies the pathogenic relevance of targets such as PTGS2 and IL6, significantly enhancing the credibility of our findings. These results provide a modern scientific basis for the traditional therapeutic effects of XQLD on CU and have important implications for developing multitarget treatments for this condition. However, this study mainly relies on database mining and computational simulations. Further in vitro and in vivo experimental validations are needed to confirm the predicted component-target-pathway interactions. CONCLUSION: This study identifies the active components, potential targets, and pathways through which XQLD exerts therapeutic effects on CU. These findings provide a theoretical foundation for further mechanistic studies and support their clinical application in the treatment of CU.

Molecular Docking Simulation↗
PubMed2001

Where value lives in a networked world.

While many management thinkers proclaim an era of radical uncertainty, authors Sawhney and Parikh assert that the seemingly endless upheavals of the digital age are more predictable than that: today's changes have a common root, and that root lies in the nature of intelligence in networks. Understanding the patterns of intelligence migration can help companies decipher and plan for the inevitable disruptions in today's business environment. Two patterns in network intelligence are reshaping industries and organizations. First, intelligence is decoupling--that is, modern high-speed networks are pushing back-end intelligence and front-end intelligence toward opposite ends of the network, making the ends the two major sources of potential profits. Second, intelligence is becoming more fluid and modular. Small units of intelligence now float freely like molecules in the ether, coalescing into temporary bundles whenever and wherever necessary to solve problems. The authors present four strategies that companies can use to profit from these patterns: arbitrage allows companies to move intelligence to new regions or countries where the cost of maintaining intelligence is lower; aggregation combines formerly isolated pieces of infrastructure intelligence into a large pool of shared infrastructure provided over a network; rewiring allows companies to connect islands of intelligence by creating common information backbones; and reassembly allows businesses to reorganize pieces of intelligence into coherent, personalized packages for customers. By being aware of patterns in network intelligence and by acting rather than reacting, companies can turn chaos into opportunity, say the authors.

Commerce↗
PubMed2001

Introduction to artificial neural networks for physicians: taking the lid off the black box.

BACKGROUND: Over the past 5 years, a steady stream of publications has discussed the use of artificial neural networks (ANNs) for urologic and other medical applications. The pace of this research has increased recently, and deployed products based on this technology are now appearing. Before these tools can be widely accepted by clinicians and researchers, a deeper level of understanding of ANNs is necessary. This article attempts to lay some of the groundwork needed to facilitate this familiarity. METHODS: A short discussion of neural network history is included for background. This is followed by an in-depth discussion of how and why ANNs work. This discussion includes the relationship between ANNs and statistical regression. An investigation of issues associated with neural networks follows, applicable to both general and urologic-specific applications. RESULTS: Neural networks are computer models that have been studied extensively for over 50 years, with prostate cancer applications since 1994. From a biological viewpoint, ANNs are artificial analogues of data structures that exist in nervous systems. From a numeric viewpoint, ANNs are matrices of numbers whose values comprise knowledge that is distilled from historic databases. Many types of neural networks are analogous to well-known statistical methods. CONCLUSIONS: ANNs are complex numeric constructs, but no more complex than similar statistical methods. However, several issues associated with neural network derivation demand that developers apply rigorous engineering practices in their studies.

Humans↗
PubMed2001

Using historical data for bioprocess optimization: modeling wine characteristics using artificial neural networks and archived process information.

Optimization of fermentation processes is a difficult task that relies on an understanding of the complex effects of processing inputs on productivity and quality outputs. Because of the complexity of these biological systems, traditional optimization methods utilizing mathematical models and statistically designed experiments are less effective, especially on a production scale. At the same time, information is being collected on a regular basis during the course of normal manufacturing and process development that is rarely fully utilized. We are developing an optimization method in which historical process data is used to train an artificial neural network for correlation of processing inputs and outputs. Subsequently, an optimization routine is used in conjunction with the trained neural network to find optimal processing conditions given the desired product characteristics and any constraints on inputs. Wine processing is being used as a case study for this work. Using data from wine produced in our pilot winery over the past 3 years, we have demonstrated that trained neural networks can be used successfully to predict the yeast-fermentation kinetics, as well as chemical and sensory properties of the finished wine, based solely on the properties of the grapes and the intended processing. To accomplish this, a hybrid neural network training method, Stop Training with Validation (STV), has been developed to find the most desirable neural network architecture and training level. As industrial historical data will not be evenly spaced over the entire possible search space, we have also investigated the ability of the trained neural networks to interpolate and extrapolate with data not used during training. Because a company will utilize its own existing process data for this method, the result of this work will be a general fermentation optimization method that can be applied to fermentation processes to improve quality and productivity.

Carbohydrate Metabolism↗
PubMed1994

Application of neural networks to the classification of giant cell arteritis.

OBJECTIVE: Neural networks are a group of computer-based pattern recognition methods that have recently been applied to clinical diagnosis and classification. In this study, we applied one type of neural network, the backpropagation network, to the diagnostic classification of giant cell arteritis (GCA). METHODS: The analysis was performed on the 807 cases in the vasculitis database of the American College of Rheumatology. Classification was based on the 8 clinical criteria previously used for classification of this data set: 1) age > or = 50 years, 2) new localized headache, 3) temporal artery tenderness or decrease in temporal artery pulse, 4) polymyalgia rheumatica, 5) abnormal result on artery biopsy, 6) erythrocyte sedimentation rate > or = 50 mm/hour, 7) scalp tenderness or nodules, and 8) claudication of the jaw, of the tongue, or on swallowing. To avoid overtraining, network training was terminated when the generalization error reached a minimum. True cross-validation classification rates were obtained. RESULTS: Neural networks correctly classified 94.4% of the GCA cases (n = 214) and 91.9% of the other vasculitis cases (n = 593). In comparison, classification trees correctly classified 91.6% of the GCA cases and 93.4% of the other vasculitis cases. Neural nets and classification trees were compared by receiver operating characteristic (ROC) analysis. The ROC curves for the two methods crossed, indicating that the better classification method depended on the choice of decision threshold. At a decision threshold that gave equal costs to percentage increases in false-positive and false-negative results, the methods were not significantly different in their performance (P = 0.45). CONCLUSION: Neural networks are a potentially useful method for developing diagnostic classification rules from clinical data.

Diagnosis, Computer-Assisted↗
PubMed2002

Hybrid neural network modeling of a full-scale industrial wastewater treatment process.

In recent years, hybrid neural network approaches, which combine mechanistic and neural network models, have received considerable attention. These approaches are potentially very efficient for obtaining more accurate predictions of process dynamics by combining mechanistic and neural network models in such a way that the neural network model properly accounts for unknown and nonlinear parts of the mechanistic model. In this work, a full-scale coke-plant wastewater treatment process was chosen as a model system. Initially, a process data analysis was performed on the actual operational data by using principal component analysis. Next, a simplified mechanistic model and a neural network model were developed based on the specific process knowledge and the operational data of the coke-plant wastewater treatment process, respectively. Finally, the neural network was incorporated into the mechanistic model in both parallel and serial configurations. Simulation results showed that the parallel hybrid modeling approach achieved much more accurate predictions with good extrapolation properties as compared with the other modeling approaches even in the case of process upset caused by, for example, shock loading of toxic compounds. These results indicate that the parallel hybrid neural modeling approach is a useful tool for accurate and cost-effective modeling of biochemical processes, in the absence of other reasonably accurate process models.

Biodegradation, Environmental↗
PubMed1993

Application of artificial neural networks to chromosome classification.

This work presents an approach to the automatic classification of metaphase chromosomes using a multilayer perceptron neural network. Representation of the banding patterns by intuitively defined features is avoided. The inputs to the network are the chromosome size and centromeric index and a coarsely quantized representation of the chromosome banding profile. We demonstrate that following a fairly mechanical training procedure, the classification performance of the network compares favourably with a well-developed parametric classifier. The sensitivity of the network performance to variation in network parameters is investigated, and we show that a gain in efficiency is obtainable by an appropriate decomposition of the network. We discuss the flexibility of the classifier developed, its potential for enhancement, and how it may be adapted to suit the needs of current trends in karyotyping.

Algorithms↗
PubMed1994

Classification of wheat varieties by isoelectric focusing patterns of gliadins and neural network.

Classification of wheat varieties, using isoelectric focusing patterns of the gliadins, image processing and neural networks, is described. The method was compared to a statistical classification method, discriminant analysis. The isoelectric point and the area of each band were calculated by image processing. Different methods of presenting the electrophoretic patterns to the neural network were studied. The most effective method was transformation of the electrophoretic pattern to a small (11 x 47 pixels) representation of the original digitized image, which was presented to the neural network as a vector. The neural network was trained with a number of patterns and tested with new patterns from different electrophoretic runs of the same wheat varieties. In this study we used ten different wheat varieties and the neural network was able to classify 95.5% of the patterns correctly. The statistical classification method classified the same data set 91.8% correctly. We conclude that both the neural network and discriminant analysis were able to classify the patterns correctly with a high degree of certainty. The patterns that were misclassified were indistinguishable by visual inspection.

Discriminant Analysis↗
PubMed2003

Automatic assessment of levodopa-induced dyskinesias in daily life by neural networks.

We developed an objective and automatic procedure to assess the severity of levodopa-induced dyskinesia (LID) in patients with Parkinson's disease during daily life activities. Thirteen patients were continuously monitored in a home-like situation for a period of approximately 2.5 hours. During this time period, the patients performed approximately 35 functional daily life activities. Behavior of the patients was measured using triaxial accelerometers, which were placed at six different positions on the body. A neural network was trained to assess the severity of LID using various variables of the accelerometer signals. Neural network scores were compared with the assessment by physicians, who evaluated the continuously videotaped behavior of the patients off-line. The neural network correctly classified dyskinesia or the absence of dyskinesia in 15-minute intervals in 93.7, 99.7, and 97.0% for the arm, trunk, and leg, respectively. In the few cases of misclassification, the rating by the neural network was in the class next to that indicated by the physicians using the AIMS score (scale 0-4). Analysis of the neural networks revealed several new variables, which are relevant for assessing the severity of LID. The results indicate that the neural network can accurately assess the severity of LID and could distinguish LID from voluntary movements in daily life situations.

Activities of Daily Living↗
PubMed2006

Prediction of continuous B-cell epitopes in an antigen using recurrent neural network.

B-cell epitopes play a vital role in the development of peptide vaccines, in diagnosis of diseases, and also for allergy research. Experimental methods used for characterizing epitopes are time consuming and demand large resources. The availability of epitope prediction method(s) can rapidly aid experimenters in simplifying this problem. The standard feed-forward (FNN) and recurrent neural network (RNN) have been used in this study for predicting B-cell epitopes in an antigenic sequence. The networks have been trained and tested on a clean data set, which consists of 700 non-redundant B-cell epitopes obtained from Bcipep database and equal number of non-epitopes obtained randomly from Swiss-Prot database. The networks have been trained and tested at different input window length and hidden units. Maximum accuracy has been obtained using recurrent neural network (Jordan network) with a single hidden layer of 35 hidden units for window length of 16. The final network yields an overall prediction accuracy of 65.93% when tested by fivefold cross-validation. The corresponding sensitivity, specificity, and positive prediction values are 67.14, 64.71, and 65.61%, respectively. It has been observed that RNN (JE) was more successful than FNN in the prediction of B-cell epitopes. The length of the peptide is also important in the prediction of B-cell epitopes from antigenic sequences. The webserver ABCpred is freely available at www.imtech.res.in/raghava/abcpred/.

Amino Acid Sequence↗
PubMed1995

Use of neural networks in predicting the risk of coronary artery disease.

Artificial neural networks were created to predict the occurrence of coronary artery disease based on information from the serum lipid profile. The development of the networks involved a strategy which permitted learning from censored observations. The networks were developed with data from the Cholesterol Lowering Atherosclerosis Study, which followed serum lipoprotein levels and clinical events in 162 patients over a period of up to 10 years. Inputs consisted of seven different mean lipid values, and the desired output was the time period during which a complication of coronary artery disease was predicted to occur. Cross-validation was performed by splitting the data into separate training and testing sets, scoring the performance of the neural network strategy on the testing sets, and comparing scores with those obtained from Cox regression models developed on the same training data. Performance of the neural network strategy exceeded that of Cox regression in predicting clinical outcomes (66% vs 56%, McNemar's test P = 0.005). The network design provided an effective approach to predicting outcomes from a clinical trial with variable follow-up times.

Adult↗
PubMed2000

Using an artificial neural network to detect activations during ventricular fibrillation.

Ventricular fibrillation is a cardiac arrhythmia that can result in sudden death. Understanding and treatment of this disorder would be improved if patterns of electrical activation could be accurately identified and studied during fibrillation. A feedforward artificial neural network using backpropagation was trained with the Rule-Based Method and the Current Source Density Method to identify cardiac tissue activation during fibrillation. Another feedforward artificial neural network that used backpropagation was trained with data preprocessed by those methods and the Transmembrane Current Method. Staged training, a new method that uses different sets of training examples in different stages, was used to improve the ability of the artificial neural networks to detect activation. Both artificial neural networks were able to correctly classify more than 92% of new test examples. The performance of both artificial neural networks improved when staged training was used. Thus, artificial neural networks may beuseful for identifying activation during ventricular fibrillation.

Diagnosis, Computer-Assisted↗
PubMed1992

Vestibuloocular reflex arc analysis using an experimentally constrained neural network.

The primary function of the vestibuloocular reflex (VOR) is to maintain the stability of retinal images during head movements. This function is expressed through a complex array of dynamic and adaptive characteristics whose essential physiological basis is a disynaptic arc. We present a model of normal VOR function using a simple neural network architecture constrained by the physiological and anatomical characteristics of this disynaptic reflex arc. When tuned using a method of global optimization, this network is capable of exhibiting the broadband response characteristics observed in behavioral tests of VOR function. Examination of the internal units in the network show that this performance is achieved by rediscovering the solution to VOR processing first proposed by Skavenski and Robinson (1973). Type I units at the intermediate level of the network possess activation characteristics associated with either pure position or pure velocity. When the network is made more complex either through adding more pairs of internal units or an additional level of units, the characteristic division of unit activation properties into position and velocity types remains unchanged. Although simple in nature, the results of our simulations reinforce the validity of bottom-up approaches to modeling of neutral function. In addition, the architecture of the network is consistent with current ideas on the characteristics and site of adaptation of the reflex and should be compatible with current theories regarding learning rules for synaptic modification during VOR adaptation.

Computer Simulation↗
PubMed1992

A neural network model rapidly learning gains and gating of reflexes necessary to adapt to an arm's dynamics.

Effects of dynamic coupling, gravity, inertia and the mechanical impedances of the segments of a multi-jointed arm are shown to be neutralizable through a reflex-like operating three layer static feedforward network. The network requires the proprioceptively mediated actual state variables (here angular velocity and position) of each arm segment. Added neural integrators (and/or differentiators) can make the network exhibit dynamic properties. Then, actual feedback is not necessary and the network can operate in a pure feedforward fashion. Feedforward of an additional load can easily be implemented into the network using "descendent gating", and a negative feedback control loop added to the feedforward control reduces errors due to external noise. A training, which combines a least squared error based simultaneous learning rule (LSQ-rule) with a "self-imitation algorithm" based on direct inverse modeling, enables the network to acquire the whole inverse dynamics, limb parameters included, during one short training movement. The considerations presented also hold for multi-jointed manipulators.

Animals↗