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PubMed1995

Simulation of the 13C nuclear magnetic resonance spectra of trisaccharides using multiple linear regression analysis and neural networks.

Predictive models are developed for the 13C NMR chemical shifts of the carbon atoms comprising the central rings of 46 trisaccharide compounds. Thirty-nine trisaccharides are used as a training set for development of models using regression analysis and computational neural networks, and seven compounds are used as an external prediction set. The descriptors used in the models are developed directly from the molecular structures of the trisaccharides. Three different methods of descriptor selection are compared. The dependence of the models on the geometries of the trisaccharides is explored. The models developed with geometric descriptors are better than those developed without geometric descriptors, although the latter models are still of a comparable quality. Overall, the best model found is a neural network based on descriptors selected by multiple linear regression.

Algorithms↗
PubMed2026

Effects of different training modalities on lower-limb explosive power, acceleration, 20-m sprint performance, and change-of-direction ability in youth soccer players: a systematic review and network meta-analysis.

BACKGROUND: Youth soccer players repeatedly perform explosive actions, short accelerations, linear sprints, decelerations, and multidirectional movements. However, the comparative effects of different structured physical-conditioning programmes remain uncertain. METHODS: Seven databases were searched from inception to 3 July 2026 using a final expanded search strategy encompassing plyometric, strength or resistance, sprint, acceleration, speed, change-of-direction, neuromuscular, multicomponent, and combined training. Randomised controlled trials involving healthy youth soccer players were eligible. Intervention arms were classified using operational, content-based node definitions. Construct-restricted primary networks and expanded sensitivity networks were analysed using frequentist random-effects network meta-analysis. Hedges' adjusted g was preferentially calculated from post-intervention or final-follow-up means, standard deviations, and sample sizes. Estimates were presented so that positive values indicated better performance. P-scores were treated as descriptive ranking summaries. Risk of bias was assessed using an adapted study-level application of the five-domain RoB 2 framework, and confidence in the evidence was assessed using CINeMA. A post hoc strict-age sensitivity analysis excluded two age-boundary studies. RESULTS: Eighty-nine studies were included in the expanded quantitative analysis, of which 74 contributed to at least one construct-restricted primary network. The primary lower-limb explosive-power, acceleration, 20-m sprint, and planned change-of-direction networks included 55, 20, 25, and 38 studies, respectively. Compared with usual soccer training, plyometric training combined with sprint and/or change-of-direction training showed favourable estimates for lower-limb explosive power (SMD 0.79, 95% CI 0.55 to 1.03), acceleration (1.19, 0.90 to 1.49), 20-m sprint performance (0.80, 0.33 to 1.28), and planned change-of-direction ability (1.46, 1.13 to 1.80). Corresponding I² values were 34.6%, 21.8%, 65.0%, and 41.0%. Between-design inconsistency was detected in the 20-m sprint (P = 0.0036) and change-of-direction (P = 0.0007) networks. CINeMA confidence for these four comparisons was low, low, very low, and low, respectively. Expanded sensitivity networks showed substantially greater heterogeneity. The highest-ranked intervention differed across outcome domains but remained consistent within each outcome across the three analysis sets. Excluding the two age-boundary studies did not materially alter the principal estimates. CONCLUSIONS: Plyometric training combined with sprint and/or planned change-of-direction training produced favourable comparative estimates across the four performance outcomes. However, evidence for several nodes and active-versus-active comparisons was sparse, heterogeneity in programmes and outcomes was present, inconsistency was detected in some networks, and confidence in the evidence was low or very low. These limitations do not support a conclusion that any training category is universally superior. The findings should be interpreted as provisional category-level signals rather than definitive training prescriptions. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD420261347297, registered on 21 March 2026, https://www.crd.york.ac.uk/PROSPERO/view/CRD420261347297 .

Change-of-direction ability↗
PubMed2003

PAINT: a promoter analysis and interaction network generation tool for gene regulatory network identification.

We have developed a bioinformatics tool named PAINT that automates the promoter analysis of a given set of genes for the presence of transcription factor binding sites. Based on coincidence of regulatory sites, this tool produces an interaction matrix that represents a candidate transcriptional regulatory network. This tool currently consists of (1) a database of promoter sequences of known or predicted genes in the Ensembl annotated mouse genome database, (2) various modules that can retrieve and process the promoter sequences for binding sites of known transcription factors, and (3) modules for visualization and analysis of the resulting set of candidate network connections. This information provides a substantially pruned list of genes and transcription factors that can be examined in detail in further experimental studies on gene regulation. Also, the candidate network can be incorporated into network identification methods in the form of constraints on feasible structures in order to render the algorithms tractable for large-scale systems. The tool can also produce output in various formats suitable for use in external visualization and analysis software. In this manuscript, PAINT is demonstrated in two case studies involving analysis of differentially regulated genes chosen from two microarray data sets. The first set is from a neuroblastoma N1E-115 cell differentiation experiment, and the second set is from neuroblastoma N1E-115 cells at different time intervals following exposure to neuropeptide angiotensin II. PAINT is available for use as an agent in BioSPICE simulation and analysis framework (www.biospice.org), and can also be accessed via a WWW interface at www.dbi.tju.edu/dbi/tools/paint/.

Angiotensin II↗
PubMed2026

Comparative effectiveness of game-based learning modalities in nursing and medical education: a systematic review and Bayesian network meta-analysis.

BACKGROUND: Game-based learning (GBL) is increasingly used in healthcare education, but educators must choose among diverse modalities (e.g., quiz platforms, apps, serious games and metaverse environments). Comparative evidence on which modalities perform best across learning domains (knowledge, attitudes, and practice) remains limited. AIM: To compare the effects of distinct GBL modalities on knowledge, attitudes, and practice outcomes in nursing and medical education and to explore whether comparative effects differ by learner group (pre-licensure students and in-service professionals). DESIGN: PRISMA-NMA-aligned systematic review and Bayesian network meta-analysis. METHODS: We searched eight databases and trial registries through September 2, 2024, for randomized controlled trials comparing GBL with traditional teaching (TT). Outcomes were transformed to a 0-100 scale and analysed as change from baseline in Bayesian consistency models; random-effects models were selected using deviance information criterion (DIC). Risk of bias was assessed using RoB 2. We report mean differences (MDs) with 95% credible intervals (CrIs) versus TT, ranking probabilities, and subgroup NMAs by learner group. RESULTS: Thirty-one RCTs (n = 3439) were included; 15 contributed complete data to the network. Risk of bias was low in 15 trials and raised some concerns in 16. The network was modest for knowledge (11 trials) and sparse for attitudes (3) and practice (4). Compared with TT, metaverse-based learning showed improved attitudes (MD 15; 95% CrI 12 to 18), based on a single trial. For knowledge and practice, Kahoot-based quizzes (MD 9.1; 95% CrI -8.9 to 27) and app-based learning (MD 4.6; 95% CrI -4.4 to 14) had the highest estimated mean improvements, but credible intervals were wide and included the null for most comparisons. Subgroup rankings differed by learner group, but several comparisons were imprecise and uncertainty was substantial, particularly in sparse networks. CONCLUSIONS: GBL modalities may improve learning outcomes compared with TT, but relative effects appear domain-specific and the certainty of rankings is limited by sparse evidence and imprecision. Future trials should prioritise head-to-head comparisons, robust outcome measurement, and longer-term retention and transfer outcomes in both student and in-service populations.

Humans↗
PubMed1995

Interactive modelling and simulation of biochemical networks.

The analysis of biochemical processes can be supported using methods of modelling and simulation. New methods of computer science are discussed in this field of research. This paper presents a new method which allows the modelling and analysis of complex metabolic networks. Moreover, our simulation shell is based on this formalization and represents the first tool for the interactive simulation of metabolic processes.

Biochemical Phenomena↗
PubMed2003

Stability and flexibility in preschoolers' social networks: a dynamic analysis of socially directed behavior allocation.

The author studied preschoolers' social networks by investigating the allocation of children's social investment within and across time in a classroom of a French nursery school during an academic year. Observations of children's social exchanges during free play revealed that social behaviors were directed toward particular group members. After an important turnover in the peer group at the beginning of the school year, the social network became more structured. Children's strong associations were mostly same sex and small sized. Even if the stability of children's connections remained low, it increased over time. High-frequency partners as well as same-sex partners were more likely to be maintained over time. These findings as well as conceptual and methodological issues are discussed from a developmental perspective.

Child Behavior↗
PubMed1998

Neural network-assisted ("NNA") analysis of cervical smears: pooled effectiveness results and economic analysis.

Objective: To determine the sensitivity of cervical cancer smear screening with neural network-assisted ("NNA") rescreening.Methods: The Papnet system of NNA analysis of cervical smears has been in clinical use worldwide for over 3 years and has been the subject of over 22 published manuscripts reporting on data from over 202,000 smears. This investigation reviewed the results of these studies and classified each study according to study design using a systematic protocol based on reference validation, diagnostic threshold for abnormal, and outcome metric. This classification taxonomy allowed for weighted (based on number of cases in each study) pooling of studies for each study design class. The pooled effectiveness metrics were used to derive the sensitivity of cervical cancer screening with NNA rescreening, using a baseline unassisted screening sensitivity of 85%. Other effectiveness metrics determined by this analysis include NNA's sensitivity as a primary screener, comparisons with primary unassisted screening, and comparisons of NNA rescreening and unassisted rescreening.Results: Analyses of the weighted, pooled mean estimates for each of the principal outcome metrics indicate the sensitivity of cervical cancer screening with NNA ranges from 90% to 99%; most pooled estimates fall in the range of 97-99%. An economic analysis using the APL-based "Cervical Cancer Screen" computer model developed by Eddy (Eddy DM. Screening for cervical cancer. Ann Intern Med 1990;113:214-26) and these effectiveness estimates as inputs showed that NNA analysis involves an accepted level of resource expenditure (approximately $40,000 per life year saved) when added to unassisted screening on a triennial basis.Conclusion: The sensitivity of cervical cancer screening with NNA rescreening using the Papnet system yields sensitivities in excess of 90% and approaching 99%.

Journal Article↗
PubMed2026

Surgical outcomes and complications of fixation strategies for distal tibial fractures: a systematic review and network meta-analysis.

BACKGROUND: Multiple fixation options exist for distal tibial fractures, but the optimal approach remains controversial. Common techniques includeopen reduction and internal fixation(ORIF), minimally invasive plate osteosynthesis (MIPO), external fixation combined with limited open reduction and internal fixation (EF + LORIF), intramedullary nailing (IMN), and retrograde tibial nailing (RTN). METHODS: PubMed, Embase, Web of Science, and the Cochrane Library were searched through March 19, 2026. Network meta-analysis (R v4.5.1) assessed operation time, fracture healing time, malunion, delayed union/nonunion, and infection, reporting MDs or RRs with 95% CIs. RESULTS: Eleven randomized controlled trials and 18 cohort studies (2145 patients) were included. MIPO was associated with a longer operative time and a longer time to union than IMN-IP (MD = 8.23, 95% CI 0.44-16.01; and MD = 1.02, 95% CI 0.10-1.93, respectively). For malunion, ORIF had a lower risk than MIPO (RR = 0.30, 95% CI 0.11-0.82), whereas MIPO had a higher risk than EF + LORIF (RR = 3.26, 95% CI 1.08-9.80) and IMN-SP (RR = 4.03, 95% CI 1.30-12.48). ORIF, EF + LORIF, and IMN-SP also showed lower malunion risk than IMN-IP. No significant differences were observed for delayed union and nonunion. Infection risk was generally higher with ORIF and MIPO than with several comparators, particularly EF + LORIF and intramedullary nailing-based strategies. CONCLUSIONS: No single strategy was consistently superior. Operation time and impaired union ( delayed union and nonunion) did not differ significantly among techniques. MIPO may be associated with longer time to union than IMN-IP and higher malunion risk than EF + LORIF and IMN-SP. Infection risk appeared higher with ORIF and MIPO in network estimates, although several comparisons remained uncertain. Findings should be interpreted in light of imprecision and study-level heterogeneity. PROTOCOL REGISTRATION: INPLASY2025120055.

Humans↗
PubMed2005

Comparison of statistical analysis and Bayesian Networks in the evaluation of dissolution performance of BCS Class II model drugs.

This project compared the effect of formulation variables on the dissolution performance of model Biopharmaceutics Classification System (BCS) Class II drugs from hard gelatin capsules using statistical analysis and Bayesian networks. The drugs chosen for this study were carbamazepine (CAR), chlorpropamide (CHL), diazepam (DIA), ketoprofen (KET), and naproxen (NAP). Formulations contained anhydrous lactose, microcrystalline cellulose, sodium stearyl fumerate, sodium lauryl sulfate, and croscarmellose sodium. A Box-Behnken experimental design was used in the statistical analysis. The weakly acidic drugs were tested using USP apparatus II with capsule sinkers in 0.1M pH 6.8 Potassium Phosphate buffer. The weakly basic drugs were tested using USP apparatus I in 0.1N HCl buffer. Mean dissolution profiles were compared via calculation of the similarity factor. The Box-Behnken experimental design was found to be useful in assessing primary and secondary excipient effects on dissolution. The Bayesian Network developed for the dataset mirrored the key excipient effects on dissolution performance.

Bayes Theorem↗
PubMed1994

Application of neural networks for EEG analysis. Considerations and first results.

This paper presents the results of the practical use of artificial neural networks in the field of EEG analysis. It describes the general methodology of application as well as a case study of a discrimination of depressive and psychotic patients using 16-channel long-term EEG data prepared by classical pre-processing (spectral decomposition). This study shows advantages and current limits concerning different levels of generalisation capabilities using a representative application example.

Electroencephalography↗
PubMed2026

Comparative Efficacy of Different AI Systems for Polyp Detection by Size During Colonoscopy: Systematic Review and Network Meta-Analysis.

BACKGROUND: Colorectal cancer remains a leading cause of death despite being largely preventable through polypectomy. AI systems designed to enhance polyp detection during colonoscopy have shown promise, but the extent to which they improve detection of different-sized polyps remains unclear. OBJECTIVE: This study compared the size-stratified efficacy of AI-assisted colonoscopy vs standard colonoscopy using the Hartung-Knapp-Sidik-Jonkman (HKSJ) method, and generated exploratory rankings while acknowledging all cross-platform comparisons are indirect. METHODS: This systematic review and network meta-analysis (NMA) searched PubMed, Embase, Cochrane CENTRAL, and Web of Science from inception to July 25, 2026, supplemented by citation searching. We included randomized controlled trials (RCTs) comparing AI-assisted vs standard colonoscopy in adults (≥18 years of age), reporting mean polyp detection counts stratified by size (≤5 mm, 6-9 mm, and ≥10 mm). Two reviewers screened studies, extracted data, and assessed risk of bias using the Cochrane Risk of Bias 2.0. We conducted frequentist NMA using the HKSJ method with restricted maximum likelihood estimation, calculated 95% prediction intervals (PIs), and assessed heterogeneity using I2 and τ2. Certainty of evidence was rated using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) framework. RESULTS: A total of 13 RCTs (4156 participants) compared 8 AI systems to standard colonoscopy, forming a network without direct AI comparisons. For diminutive polyps (≤5 mm), AI showed a modest advantage (standardized mean difference [SMD] 0.21, 95% CI 0.07 to 0.35, 95% PI -1.12 to 1.54), but substantial heterogeneity (I2=86.6%) and wide PI crossing the null indicated high uncertainty. EndoScreener showed the most consistent evidence (SMD 0.36, 95% CI 0.18-0.54). For small and large polyps, effects were minimal (SMD 0.02, 95% CI -0.02 to 0.06, 95% PI -0.03 to 0.07; SMD 0.01, 95% CI 0.00-0.02, 95% PI -0.01 to 0.03). GRADE certainty was very low for diminutive polyps and low for small and large polyps. Sensitivity analysis excluding Tianjin YuJin did not materially change findings. CONCLUSIONS: AI may modestly enhance diminutive polyp detection, but effects on small and large polyps are minimal, with no platform superiority. Given very low to low certainty, findings are hypothesis-generating. This exploratory NMA provides size-stratified comparisons that can inform future head-to-head trial design. Unlike prior reviews aggregating all polyp sizes, we show the overall AI benefit is driven by diminutive polyp detection, providing a framework for targeted deployment-prioritizing AI for diminutive polyp screening, with limited value for larger lesions. Head-to-head trials are urgently needed. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD420251266932; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251266932.

Colonoscopy↗
PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗
PubMed1993

Constrained Markov networks for automated analysis of G-banded chromosomes.

Automated analysis of chromosome band patterns using probabilistic Markov networks has been reported in previous work. Band patterns are represented as strings of symbols. Inferred from a set of learning strings, a Markov network is a model of intraband and interband relations in these strings. The inference is entirely data-driven and is accomplished using dynamic programming. This paper presents a new model of chromosome band patterns, the constrained Markov network, which is a special case of its predecessor. Substantial experimental evidence of the superiority of the new model over the old is given in terms of equal results in centromere finding and improved results in classification for the 22 autosomes. Furthermore, a method for simplification of constrained Markov networks is shown to be of considerable importance with respect to computational complexity.

Centromere↗
PubMed2004

Network-based regulatory pathways analysis.

MOTIVATION: A useful approach to unraveling and understanding complex biological networks is to decompose networks into basic functional and structural units. Recent application of convex analysis to metabolic networks leads to the development of network-based metabolic pathway analysis and the decomposition of metabolic networks into metabolic extreme pathways that are true functional units of metabolic systems. Metabolic extreme pathways are derived from limited knowledge of the metabolic networks, but provide an integrated predictive description of metabolic networks. It is important to extend the concept of network-based metabolic pathways to genetic networks and develop mathematical procedures for network-based regulatory pathway analysis. RESULTS: We have established Kirchhoff's first law in genetic networks and introduced a concept of gene flows using matrix decomposition method. The Kirchhoff's first law provides the theoretical foundations for mathematical framework for development defining network-based regulatory pathways, and applying convex analysis in decomposing the genetic networks into regulatory extreme pathways. We presented a new approach to characterize the extreme pathway and developed a new algorithm for identifying a set of extreme pathways. Convex analysis and extreme pathway structure provide a unified framework for functional and structural analysis of metabolic and genetic networks, which will increase our ability to analyze, interpret and predict the function of metabolic and genetic networks. The proposed models for network-based regulatory pathway analysis have been applied to apoptosis regulatory network.

Algorithms↗
PubMed2002

Network meta-analysis for indirect treatment comparisons.

I present methods for assessing the relative effectiveness of two treatments when they have not been compared directly in a randomized trial but have each been compared to other treatments. These network meta-analysis techniques allow estimation of both heterogeneity in the effect of any given treatment and inconsistency ('incoherence') in the evidence from different pairs of treatments. A simple estimation procedure using linear mixed models is given and used in a meta-analysis of treatments for acute myocardial infarction.

Angioplasty↗
PubMedDate not supplied

Detection and deletion of motion artifacts in electrogastrogram using feature analysis and neural networks.

Electrogastrogram is a surface measurement of gastric myoelectrical activity, and electrogastrography has been an attractive method for physiological and pathophysiological studies of the stomach due to its noninvasive nature. Motion artifacts, however, ruin the electrogastrogram (EGG), and make the analysis very difficult and sometimes even impossible. They must be eliminated from EGG signals before analysis. Up to now, this can only be done by visual inspection, which is not only time-consuming but also subjective. In this study, a method using feature analysis and neural networks has been developed to realize automatic detection and elimination of the motion artifacts in EGG recordings by computer. Experiments were conducted to investigate the characteristics of different motion artifacts. Useful features were extracted, and different combinations of the features used as the input of the neural network were compared to obtain the optimal performance for the detection of motion artifacts using the artificial neural network.

Biomedical Engineering↗
PubMed2001

Integrated approach of an artificial neural network and numerical analysis to multiple equivalent current dipole source localization.

The authors have developed a PC-based multichannel electroencephalogram (EEG) measurement and analysis system. This system enables us (1) to simultaneously record a maximum of 64 channels of EEG data, (2) to measure three-dimensional positions of the recording electrodes, (3) to rapidly and precisely localize equivalent current dipoles (ECDs) responsible for the EEG data, and (4) to superimpose the localization results on magnetic resonance images. A new neural network and numerical analysis (NNN) approach to ECD localization is described which integrates a feedforward artificial neural network (ANN) and a numerical optimization (Powell's hybrid) method. It was shown that the ANN method has the advantages of high-speed localization and noise robustness, because in this approach: (1) ECD parameters are immediately initialized from the recorded EEG data by the ANN and (2) ECD parameters are accurately refined by the hybrid method. Our multiple ECD localization method was applied to sensory evoked potentials and event-related potentials using the present system.

Algorithms↗
PubMed1998

Application of artificial neural networks for quantitative analysis of image data in chest radiographs for detection of interstitial lung disease.

The authors have developed an automated computeraided diagnostic (CAD) scheme by using artificial neural networks (ANNs) on quantitative analysis of image data. Three separate ANNs were applied for detection of interstitial disease on digitized chest images. The first ANN was trained with horizontal profiles in regions of interest (ROIs) selected from normal and abnormal chest radiographs for distinguishing between normal and abnormal patterns. For training and testing of the second ANN, the vertical output patterns obtained from the 1st ANN were used for each ROI. The output value of the second ANN was used to distinguish between normal and abnormal ROIs with interstitial infiltrates. If the ratio of the number of abnormal ROIs to the total number of all ROIs in a chest image was greater than a specified threshold level, the image was classified as abnormal. In addition, the third ANN was applied to distinguish between normal and abnormal chest images. The combination of the rule-based method and the third ANN also was applied to the classification between normal and abnormal chest images. The performance of the ANNs was evaluated by means of receiver operating characteristic (ROC) analysis. The average Az value (area under the ROC curve) for distinguishing between normal and abnormal cases was 0.976 +/- 0.012 for 100 chest radiographs that were not used in training of ANNs. The results indicate that the ANN trained with image data can learn some statistical properties associated with interstitial infiltrates in chest radiographs.

Humans↗