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PubMed2006

The SBML ODE Solver Library: a native API for symbolic and fast numerical analysis of reaction networks.

The SBML ODE Solver Library (SOSlib) is a programming library for symbolic and numerical analysis of chemical reaction network models encoded in the Systems Biology Markup Language (SBML). It is written in ISO C and distributed under the open source LGPL license. The package employs libSBML structures for formula representation and associated functions to construct a system of ordinary differential equations, their Jacobian matrix and other derivatives. SUNDIALS' CVODES is incorporated for numerical integration and sensitivity analysis. Preliminary benchmarking results give a rough overview on the behavior of different tools and are discussed in the Supplementary Material. The native application program interface provides fine-grained interfaces to all internal data structures, symbolic operations and numerical routines, enabling the construction of very efficient analytic applications and hybrid or multi-scale solvers with interfaces to SBML and non SBML data sources. Optional modules based on XMGrace and Graphviz allow quick inspection of structure and dynamics.

Algorithms↗

PubMed2026

A systematic review and network meta-analysis of single nucleotide polymorphisms associated with oral submucous fibrosis risk.

BACKGROUND: Oral submucous fibrosis (OSF) is a chronic and insidious oral disease characterized by hyalinization of the subepithelial connective tissue and progressive fibrosis of the oral submucosa. It is a precancerous condition of oral squamous cell carcinoma. Studies have demonstrated that single nucleotide polymorphisms (SNPs) are closely associated with susceptibility to OSF. This study aims to comprehensively evaluate the association between SNPs and OSF risk and to rank the strength of the association between different genetic models and OSF susceptibility. METHODS: Literature related to OSF was comprehensively searched from PubMed, Web of Science, Embase, Cochrane Library, CNKI, and Wangfang databases up to July 2025. Full-text case-control studies with patients diagnosed with OSF were included. Quality assessment was performed to evaluate the risk of bias. RevMan 5.4, GeMTC 0.14.3, and STATA 17.0 were used for the pairwise and Bayesian network meta-analysis. RESULTS: A total of 24 studies with 2545 cases and 3772 controls, covering 13 SNPs in 11 genes, were included in our meta-analysis. We found that CYP1A1 rs4646903:T>C, CYP1A1 rs1048943:A>G, GSTT1 null genotype, GSTM1 null genotype, and XRCC3 rs861539:C>T were associated with an increased risk of OSF, while MMP2 rs243865:C>T and MMP3 rs3025058: 5A>6A were associated with a decreased risk of OSF. Further Bayesian network meta-analysis indicated the top 5 genetic models with the highest association with OSF risk in network group 1 were the dominant model, homozygous model, allelic model, and recessive model of CYP1A1 rs1048943:A>G (ranked 1-4), and the heterozygous/dominant model of CYP1A1 rs4646903:T>C (both ranked 5). While the allelic models of XRCC3 rs861539:C>T and MMP3 rs3025058: 5A>6A ranked first for predicting OSF in group 2 and group 3, respectively. CONCLUSION: Some specific SNPs are significantly related to the risk of OSF. Among them, the dominant model of CYP1A1 rs1048943:A>G may be the most strongly associated genetic model with OSF risk. Future large-sample, well-designed studies with detailed genotype data are needed to validate the roles of these SNPs in OSF risk.

Humans↗

PubMed1994

Protein secondary structure from circular dichroism spectroscopy. Combining variable selection principle and cluster analysis with neural network, ridge regression and self-consistent methods.

Different approaches to improve the analysis of protein secondary structure from circular dichroism spectra are compared. Grouping proteins based on the similarity of their circular dichroism spectra, using cluster analysis methods, was utilized as a new way of implementing variable selection. The performance of three basic methods (neural networks, ridge regression and singular value decomposition) was evaluated in combination with three approaches to improve the predictions; namely, variable selection, cluster analysis and the self-consistent method. Cluster analysis performed on the basis set proteins resulted in three clusters, subanalyses of which provide a new way of performing variable selection. The neural network with two hidden layers performed better than that with one hidden layer and was combined with variable selection. Inclusion of the variable selection principle improved the performance of all three basic methods. While the neural network method performed slightly better than the other two methods at the basic level, the inclusion of variable selection led to similar performance indices for all three methods.

Circular Dichroism↗

PubMed1997

Coherency and connectivity in oscillating neural networks: linear partialization analysis.

This paper studies the relation between the functional synaptic connections between two artificial neural networks and the correlation of their spiking activities. The model neurons had realistic non-oscillatory dynamic properties and the networks showed oscillatory behavior as a result of their internal synaptic connectivity. We found that both excitation and inhibition cause phase locking of the oscillating activities. When the two networks excite each other the oscillations synchronize with zero phase lag, whereas mutual inhibition between the networks resulted in an anti-phase (half period phase difference) synchronization. Correlations between the activities of the two networks can also be caused by correlated external inputs driving the systems (common input). Our analysis shows that when the networks exhibit oscillatory behavior and the rate of the common input is smaller than a characteristic network oscillator frequency, the cross-correlation functions between the activities of two systems still carry information about the mutual synaptic connectivity. This information can be retrieved with linear partialization, removing the influence of the common input. We further explored the network responses to periodic external input. We found that when the input is of a frequency smaller than a certain threshold, the network responds with bursts at the same frequency as the input. Above the threshold, the network responds with a fraction of the input frequency. This frequency threshold, characterizing the oscillatory properties of the network, is also found to determine the limit to which linear partialization works.

Computer Simulation↗

PubMed2005

Skin capillary network recognition and analysis by means of neural algorithms.

BACKGROUND: The intra-dermal capillary network can be easily assessed by a computerized videomicroscope system. Nevertheless, finding capillary loops automatically in an image is a difficult yet important first step in order to achieve microcirculation analysis. METHODS: A detection system was tested by combining videocapillaroscopy and principal component analysis (PCA). Our goal was to build a generic detector of capillary associated with a retinally connected neural network filter. The filter examines small windows of an image, and decides with this detector whether each window contains a capillary or not. RESULTS: Comparisons with manual detections showed that the system has a detection rate of 82% on test set A containing 100 good-quality images of the scalp. A detection rate of 65% was obtained on test set B containing 50 images with noisy background and large artifacts. The performance was increased by a color detector with a detection rate of 71% on the last test. These results correspond to a false detection rate lower than or equal to 10%. CONCLUSION: This neural filter system is capable of real-time processing; it recognizes capillaries anywhere in an image, and operates successfully under wide range of lighting and noisy conditions.

Algorithms↗

PubMed2025

Comparative Efficacy of Janus Kinase Inhibitors Indicated for Severe Alopecia Areata: A Bayesian Network Meta-Analysis and Matching-Adjusted Indirect Comparison.

Systemic Janus kinase inhibitors (JAKIs) have markedly advanced the therapeutic landscape for alopecia areata (AA). Although baricitinib and ritlecitinib are approved in the United States (US) and Europe, and deuruxolitinib in the US for severe AA, the lack of head-to-head randomized controlled trials (RCTs) limits evidence-based prescribing decisions. Moreover, prior meta-analyses excluded data on certain oral JAKIs or incorporated findings from agents and dosing regimens that were abandoned, investigational, clinically ineffective, or associated with unacceptable safety profiles. To compare the efficacy of oral JAKIs, limited to FDA, EMA, or MHRA approved drugs and doses-baricitinib (2 and 4 mg QD), ritlecitinib (50 mg QD), and deuruxolitinib (8 mg BID)-for severe AA, using advanced indirect comparison methodologies. A systematic review was performed following PRISMA 2020 guidelines (CRD420251116775). Bayesian network meta-analysis (NMA) synthesized data from RCTs reporting Week 24 outcomes on Severity of Alopecia Tool (SALT) ≤ 10 and SALT ≤ 20 thresholds. Multilevel network meta-regression (ML-NMR) evaluated heterogeneity and adjusted for baseline imbalances. Additionally, unanchored matching-adjusted indirect comparisons (MAIC) were conducted using individual patient-level data from THRIVE trials. Surface under the cumulative ranking (SUCRA) values were calculated to rank treatments. Seven RCTs (n = 4560 participants) were included. Deuruxolitinib 8 mg significantly outperformed baricitinib 2 and 4 mg on both SALT endpoints. Differences with ritlecitinib 50 mg were directionally favorable for deuruxolitinib but not statistically significant in NMA and ML-NMR models. MAICs confirmed superior odds for deuruxolitinib versus baricitinib 2 mg (OR = 71.55) and ritlecitinib (OR = 18.27) for SALT ≤ 20. SUCRA rankings also consistently favored deuruxolitinib. Among approved oral JAKIs, deuruxolitinib 8 mg shows the highest short-term efficacy for severe AA. These findings provide preliminary evidence to guide treatment decisions but should be interpreted as exploratory pending confirmation.

Humans↗

PubMed2001

The use of neural networks and logistic regression analysis for predicting pathological stage in men undergoing radical prostatectomy: a population based study.

PURPOSE: Clinical under staging occurs in 40% to 60% of patients who undergo radical prostatectomy for prostate cancer. To decrease under staging several methods of predicting pathological stage preoperatively have been developed based on statistical logistic regression analysis and neural networks. To our knowledge none has been validated in our homogeneous regional patient population to date. We created logistic regression and neural network models, and implemented and adapted them into our practice. We also compared the 2 methods to determine their value and practicality in daily clinical practice. We present the results of our novel approach for predicting pathological staging of prostate adenocarcinoma. MATERIALS AND METHODS: Between 1986 and 1999, 600 white men from the Aragon region of Spain underwent surgery for prostate cancer; of whom 468 were selected for study. Predictive study variables included patient age, clinical stage, biopsy Gleason score and preoperative prostate specific antigen (PSA). The predicted result included in analysis was organ confined or nonorgan confined disease. Data were analyzed by multivariate logistic regression and a supervised neural network (multilayer perceptron and radial basis function). Results were compared by comparing the areas under the receiver operating characteristics curves. RESULTS: We generated 5 logistic regression models. The model created with clinical staging, Gleason biopsy score and PSA distributed in 5 categories (p <0.001) with an area under the receiver operating characteristics curve of 0.840 proved to be most predictive of pathological stage. Similarly of the 6 neural network models evaluated the radial basis function model, which included age, clinical stage, Gleason biopsy score and preoperative PSA distributed in 5 categories with an area under the curve of 0.882, proved the most predictive but not superior to the logistic regression model. The difference in the area under the curves in the 2 chosen models was 0.042 (p = 0.1). CONCLUSIONS: It is possible to generate useful predictive models of organ confined disease using logistic regression or neural networks with high indexes of clinical and statistical validity. However, using these variables neural networks did not prove to be better than logistic regression analysis. Therefore, better predictive variables must be identified, preferably nonlinear characteristics with respect to the probability of organ confined tumor, to generate better predictive models using neural networks.

Aged↗

PubMed2026

Comparative safety of lipid-lowering drugs alone or in combination: insights from a systematic review and network meta-analysis.

BACKGROUND AND AIMS: Although the safety profile of lipid-lowering therapies (LLTs) is known, there are no comprehensive comparative assessments. We aimed to compare the risk of muscle-related events, diabetes, liver dysfunction, and cognitive disorders among LLTs through a network meta-analysis. METHODS AND RESULTS: Databases were searched from inception to May 2025. Eligible studies included adult patients, using statins, ezetimibe, PCSK9 monoclonal antibodies (PCSK9mAbs), inclisiran, bempedoic acid, or their combinations as intervention, reporting the information about any of the selected adverse events, a total sample size of ≥200 subjects, and had ≥1 month of intervention. Pooled estimates were assessed by fixed effects model within a frequentist setting. Pooled relative risks (RR) and their 95% confidence interval were estimated. A total of 303,397 subjects from 153 RCTs were included. Bempedoic acid ranked the lowest risk of myalgia (vs PCSK9mAbs, RR 0.80 [0.69, 0.93]). PCSK9mAbs were associated with lower incidence of creatine kinase (CK) elevation, diabetes, and liver dysfunction comparing to statins (statins vs PCSK9mAbs, RR 1.44 [1.14, 1.81], RR 1.13 [1.05, 1.22], and RR 1.38 [1.17, 1.62], respectively). In terms of muscle-related events and cognitive disorders, no significant risk differences were found among treatments and their combinations. CONCLUSIONS: PCSK9mAbs appear to have a more favourable safety profile regarding the risk of CK elevation, diabetes, and liver dysfunction. Bempedoic acid seem to be a better choice for subjects with high risk of myalgia. This information can be valuable when selecting therapy for specific patient subgroups at higher risk of certain adverse events.

Humans↗

PubMed1998

Issues in Bayesian Analysis of Neural Network Models

Stemming from work by Buntine and Weigend (1991) and MacKay (1992), there is a growing interest in Bayesian analysis of neural network models. Although conceptually simple, this problem is computationally involved. We suggest a very efficient Markov chain Monte Carlo scheme for inference and prediction with fixed&hyphenarchitecture feedforward neural networks. The scheme is then extended to the variable architecture case, providing a data&hyphendriven procedure to identify sensible architectures.

Journal Article↗

PubMed2006

Cross-species analysis of biological networks by Bayesian alignment.

Complex interactions between genes or proteins contribute a substantial part to phenotypic evolution. Here we develop an evolutionarily grounded method for the cross-species analysis of interaction networks by alignment, which maps bona fide functional relationships between genes in different organisms. Network alignment is based on a scoring function measuring mutual similarities between networks, taking into account their interaction patterns as well as sequence similarities between their nodes. High-scoring alignments and optimal alignment parameters are inferred by a systematic Bayesian analysis. We apply this method to analyze the evolution of coexpression networks between humans and mice. We find evidence for significant conservation of gene expression clusters and give network-based predictions of gene function. We discuss examples where cross-species functional relationships between genes do not concur with sequence similarity.

Algorithms↗

PubMed2006

Morphological development of Aspergillus niger in submerged citric acid fermentation as a function of the spore inoculum level. Application of neural network and cluster analysis for characterization of mycelial morphology.

BACKGROUND: Although the citric acid fermentation by Aspergillus niger is one of the most important industrial microbial processes and various aspects of the fermentation appear in a very large number of publications since the 1950s, the effect of the spore inoculum level on fungal morphology is a rather neglected area. The aim of the presented investigations was to quantify the effects of changing spore inoculum level on the resulting mycelial morphology and to investigate the physiology that underlines the phenomena. Batch fermentations were carried out in a stirred tank bioreactor, which were inoculated directly with spores in concentrations ranging from 10(4) to 10(9) spores per ml. Morphological features, evaluated by digital image analysis, were classified using an artificial neural network (ANN), which considered four main object types: globular and elongated pellets, clumps and free mycelial trees. The significance of the particular morphological features and their combination was determined by cluster analysis. RESULTS: Cell volume fraction analysis for the various inoculum levels tested revealed that by rising the spore inoculum level from 10(4) to 10(9) spores per ml, a clear transition from pelleted to dispersed forms occurs. Glucosamine formation and release by the mycelium appears to be related to spore inoculum level. Maximum concentrations detected in fermentations inoculated with 10(4) and 10(5) spores/ml, where pellets predominated. At much higher inoculum levels (10(8), 10(9) spores/ml), lower dissolved oxygen levels during the early fermentation phase were associated with slower ammonium ions uptakes and significantly lower glucosamine concentrations while the mycelium developed in dispersed morphologies. A big increase in the main and total hyphal lengths and branching frequency was observed in mycelial trees as inoculum levels rise from 10(4) to 10(9) spores/ml, while in aggregated forms particle sizes and their compactness decreased. CONCLUSION: The methods used in this study, allowed for the detailed quantification of the transition between the two extreme morphological forms. The impact of spore inoculum level on the detailed characteristics of the particular morphological forms produced was high. Control of mycelial morphology is often regarded as a prerequisite to ensure increased productivities in industrial applications. The research described here demonstrates that adjusting the spore inoculum level controls effectively mycelial morphology.

Journal Article↗

PubMed1996

Computerized fetal heart rate analysis and neural networks in antepartum fetal surveillance.

The use of computers for analysis of fetal heart rate has developed rapidly, enabling reproducible, objective analysis of fetal heart rate patterns. The problems of observer differences in visual assessment of fetal heart rate, and the continuum of antepartum and intrapartum fetal condition can be clarified through the use of such approaches. Computerized fetal heart rate analysis presents opportunities to perform precise evaluations of the effects of environmental conditions, medications, and disease states on fetal heart rate parameters.

Computer Communication Networks↗

PubMed2000

Artificial neural network algorithm for analysis of rutherford backscattering data

Rutherford backscattering (RBS) is a nondestructive, fully quantitative technique for accurately determining the compositional depth profile of thin films. The inverse RBS problem, which is to determine from the data the corresponding sample structure, is, however, in general ill posed. Skilled analysts use their knowledge and experience to recognize recurring features in the data and relate them to features in the sample structure. This is then followed by a detailed quantitative analysis. We have developed an artificial neural network (ANN) for the same purpose, applied to the specific case of Ge-implanted Si. The ANN was trained with thousands of constructed spectra of samples for which the structure is known. It thus learns how to interpret the spectrum of a given sample, without any knowledge of the physics involved. The ANN was then applied to experimental data from samples of unknown structure. The quantitative results obtained were compared with those given by traditional analysis methods and are excellent. The major advantage of ANNs over those other methods is that, after the time-consuming training phase, the analysis is instantaneous, which opens the door to automated on-line data analysis. Furthermore, the ANN was able to distinguish two different classes of data which are experimentally difficult to analyze. This opens the door to automated on-line optimization of the experimental conditions.

Journal Article↗

PubMed1997

Application of image analysis and neural networks to the pathology diagnosis of intraductal proliferative lesions of the breast.

We studied whether a computer-assisted system using a combination of data collection by image analysis and analysis by neural networks can differentiate benign and malignant breast lesions. Forty-six intraductal lesions of the breast were studied by pathologists and by the computer-assisted system. Histological evaluation was performed independently by three pathologists, and the lesions were classified into pathologically malignant (n = 12), undetermined (n = 13), and benign (n = 21). Computerized nuclear image analysis was performed using the CAS200 (Cell Analysis Systems, Elmhurst, IL) system to obtain data on nuclear morphometric and textural features. A neural network was constructed using the morphometric and texture data obtained from teaching cases of malignant and benign lesions. Then data for unknown cases were classified by the constructed neural network into neural network-malignant (n = 11), -undetermined (n = 5), and -benign (n = 30). The agreement rate between the diagnosis by pathologists and judgment by the computer-assisted system was 75%, excluding pathologically undetermined lesions. There were four false-negative but no false-positive results. False-negative cases had nuclei that were quite different from those of the teaching cases. The agreement rate obtained using either morphometric data or texture data only was lower than that using a combination of both. Selection of appropriate teaching data and incorporation of both morphometric and textural parameters seemed important for obtaining more accurate results. The present data suggest that development of a computer-assisted histopathological diagnosis system for practical use may be possible.

Breast↗

PubMed2026

Molecular targeted therapy in combination with chemotherapy for the treatment of platinum-resistant/refractory ovarian cancer (PROC): a systematic review and network meta-analysis.

BACKGROUND: Although single-agent chemotherapy is the most common approach for treating platinum-resistant or refractory ovarian cancer (PROC), there is growing evidence that combining molecular targeted agents with chemotherapy is beneficial, especially for certain patient groups. However, the most effective combination regimen remains elusive. OBJECTIVES: This Bayesian network meta-analysis (NMA) aims to identify the best combination therapy for PROC. METHODS: Relevant studies were searched in PubMed, EMBASE, Web of Science and the Cochrane Central Register of Controlled Trials from their inception until October 2024. The primary outcomes were overall survival (OS), progression-free survival (PFS) and adverse events (AEs). Statistical analyses were performed using the GEMTC package (1.0-2) and R 4.2.0. This review was registered in PROSPERO (CRD42023428414). RESULTS: Our analysis of 22 randomized controlled trials (RCTs) (n = 3408) demonstrated that chemotherapy combinations with bevacizumab (hazard ratio (HR) = 0.52-0.65), sorafenib (HR = 0.65, 95% confidence interval (CI): 0.45-0.93) or adavosertib (HR = 0.56, 95%CI: 0.35-0.90) significantly improved OS and PFS versus chemotherapy alone. Notably, adavosertib + gemcitabine was associated with an increased risk of grade 3-4 AEs (relative risk (RR) = 1.8, 95%CI: 1.3-2.7), but these were generally manageable. CONCLUSIONS: Bevacizumab-based combinations demonstrate consistent benefits across multiple regimens for PROC. Paclitaxel + bevacizumab emerges as the optimal balance of efficacy and safety. Topotecan + sorafenib could be an alternative for patients who are ineligible for anti-angiogenic therapy.

Humans↗

PubMed1997

Outcome after severe head injury: an analysis of prediction based upon comparison of neural network versus logistic regression analysis.

More reliable prediction of outcome would be helpful for clinicians who treat severely head-injured patients. To determine if neural network modeling would improve outcome prediction compared with standard logistic regression analysis and to determine if data available 24 h after severe head injury allows better prediction than data obtained within 6 h, we tested the ability of both techniques at these two times to predict outcome (dead versus alive) at 6 months. One thousand sixty-six consecutive patients with Glasgow Coma Scale scores of 8 or less during the first 24 h after injury were randomly divided into two groups. Data from the first group (n = 799) were used to develop the models; data from the second group (n = 267) were used to test the accuracy, sensitivity, and specificity of the models by comparing predicted and actual outcomes. The 6-month mortality rate was 63.5%. Our findings confirm the importance of age, Glasgow Coma Scale scores, and hypotension in predicting outcome. Using data available at 24 h improved the predictive power of both models compared with admission data; at both time points, however, the differences in the results obtained with the two models were negligible. We conclude that outcome (dead versus alive) at 6 months after severe head injury can be predicted with logistic regression or neural network models based on data available at 24 h. Critical therapeutic decisions, such as cessation of therapy, should be based on the patient's status 1 day after injury and only rarely on admission status alone.

Adolescent↗

PubMed2006

Prediction of bone mass gain by bone turnover parameters after parathyroidectomy for primary hyperparathyroidism: neural network software statistical analysis.

BACKGROUND: Primary hyperparathyroidism (pHPT) is the most frequent endocrine hypersecretion disease, and parathyroidectomy is the only curative option, since pharmacologic therapy reduces hypercalcemia but does not impede parathyroid hormone hypersecretion. According to guidelines from the National Institutes of Health, parathyroidectomy is associated with bone mass increase in some asymptomatic patients, while in others bone mass is not changed after surgery. Therefore, we performed the present study in an attempt to elucidate whether a preoperative biochemical bone parameter can be predictive of a significant vertebral bone mass increase in patients with pHPT. METHODS: For each patient we analyzed the following preoperative parameters: parathyroid hormone, urinary calcium excretion, urinary type I collagen cross-linked N-telopeptide (NTX), osteocalcin, and vertebral computerized bone mineralography. All patients underwent vertebral computerized bone mineralography 12 months after the operation. Statistical analysis was carried out by a neural network program, an event-predicting software modeled on human brain neuronal connections, which is able to examine independent statistical parameters. RESULTS: The patients presenting with high preoperative bone turnover (especially high NTX levels) will have a 5% vertebral bone mass gain in 83.33% of cases after surgery, independently of the National Institutes of Health guidelines. CONCLUSIONS: A high preoperative NTX level seems to be the best predictor parameter for postoperative vertebral bone mass gain in patients with pHPT. Our study also illustrates that neural network software may be a valuable method to help elucidate which pHPT patients should undergo surgical treatment.

Aged↗

PubMed1999

[The cooperation system for drug analysis by computer network].

Generally, the drugs are analyzed by the forensic medicine department in the most of autopsy cases. The forensic medicine department cannot always perform toxicological analysis because a shortage of a verified personnel, appropriate instruments, and financial resources. In order to overcome these difficulties and lack of resources, we developed a computer network called ml-poison. This network consists of the staff of forensic medicine, hospitals, governmental agencies and research facilities of police departments, etc. This network offers immediate and valuable information and expertise to these inexperienced in dealing with cases of poisoning. In cases in which the toxicological analysis cannot be performed in a facility, the network will recommend a facility where the analysis can be performed. Up this point, the network order system has assisted in many cases of poisoning. Although the effectiveness of the network for toxicological analysis has been proven, we still must deal with severed difficult problems: 1. The number of facilities assisting network orders is limited. 2. Who will pay the expenses involved in the analysis. 3. How to maintain security of the system. 4. What agency will assume responsibility for the management of the system.

Computer Communication Networks↗