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PubMed2004

Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data.

The dissection of complex biological systems is a challenging task, made difficult by the size of the underlying molecular network and the heterogeneous nature of the control mechanisms involved. Novel high-throughput techniques are generating massive data sets on various aspects of such systems. Here, we perform analysis of a highly diverse collection of genomewide data sets, including gene expression, protein interactions, growth phenotype data, and transcription factor binding, to reveal the modular organization of the yeast system. By integrating experimental data of heterogeneous sources and types, we are able to perform analysis on a much broader scope than previous studies. At the core of our methodology is the ability to identify modules, namely, groups of genes with statistically significant correlated behavior across diverse data sources. Numerous biological processes are revealed through these modules, which also obey global hierarchical organization. We use the identified modules to study the yeast transcriptional network and predict the function of >800 uncharacterized genes. Our analysis framework, SAMBA (Statistical-Algorithmic Method for Bicluster Analysis), enables the processing of current and future sources of biological information and is readily extendable to experimental techniques and higher organisms.

Amino Acids↗
PubMed2026

Comparative safety and operative efficiency of surgical approaches for open reduction and internal fixation of mandibular condylar fractures: a systematic review and network meta-analysis.

BACKGROUND: The optimal surgical approach for mandibular condyle fractures remains controversial, particularly regarding the trade-off between facial nerve safety and surgical efficiency. METHODS: Our systematic review and frequentist NMA analyzed studies evaluating open reduction and internal fixation (ORIF) for mandibular condyle fractures. The primary safety outcome was transient or persistent (≥6 months) facial nerve weakness. The efficiency outcome was operative exposure time. Surgical approaches were categorized, and random-effects NMAs estimated odds ratios (ORs) and mean differences (MDs). Treatments were ranked using P-scores, and clustered rankings explored safety-efficiency relationships. RESULTS: Overall, 121 studies (n = 6659 patients) were included. For facial nerve safety, endoscope-assisted (EA), preauricular transmasseteric anteroparotid (PATMA), and high submandibular (HSMA) approaches ranked highest, while the retromandibular transparotid (RMTA) approach carried a higher risk. Analyses restricted to persistent weakness yielded consistent results. For exposure time, submandibular (SM), HSMA, and retromandibular transmasseteric anteroparotid (RMTMA) approaches were most efficient. Clustered ranking analysis identified HSMA and RMTMA as achieving the best overall balance between safety and efficiency. Subgroup analyses confirmed the overall hierarchy, though evidence for high-level and intracapsular fractures remains limited. CONCLUSIONS: HSMA and RMTMA offer an optimal compromise between safety and efficiency for extracapsular condylar fractures, while EA may minimize nerve injury risk where technical expertise allows. High-quality comparative trials - particularly for intracapsular fractures - are warranted.

Humans↗
PubMed2026

Comparative effectiveness and safety of pharmacological interventions for sleep outcomes in chronic non-cancer pain: a systematic review and network meta-analysis.

Sleep disturbances are highly prevalent among individuals with chronic non-cancer pain and are associated with worse pain severity and poorer prognosis. The comparative trade-offs between the effectiveness and safety of available pharmacotherapies for sleep outcomes in this population remain poorly defined. Ninety-eight RCTs involving 28,920 participants (mean age 53.2 years, 71.2% female) were included. Moderate-certainty evidence demonstrated that melatonin significantly improved sleep quality compared with placebo (standardized mean difference [SMD] = -0.60, 95%CI: -0.98, -0.22). Ten agents (e.g., amitriptyline, oxycodone, gabapentin, pregabalin, duloxetine) also showed statistically significant improvements in subjective sleep quality (SMD = -0.24 to -1.07), but most effects were supported by low-certainty evidence and were accompanied by an increased risk of adverse events (odds ratio [OR] = 1.90 to 37.00). Conversely, melatonin was not associated with an increased risk (OR = 0.88, 95%CI: 0.19, 3.94). Our findings indicate that melatonin shows promise as a safe, adjunctive option for improving sleep quality in this population, but larger, condition-specific trials are warranted to confirm these effects. Other pharmacological agents are limited by lower-certainty and unfavorable safety profiles. These results should be interpreted cautiously given limited direct comparisons, heterogeneous chronic pain populations, the high proportion of trials at high risk of bias, and the predominance of subjective sleep outcomes.

Humans↗
PubMed2003

A novel approach to the recognition of protein architecture from sequence using Fourier analysis and neural networks.

A novel method is presented for the prediction of protein architecture from sequence using neural networks. The method involves the preprocessing of protein sequence data by numerically encoding it and then applying a Fourier transform. The encoded and transformed data are then used to train a neural network to recognize a number of different protein architectures. The method proved significantly better than comparable alternative strategies such as percentage dipeptide frequency, but is still limited by the size of the data set and the input demands of a neural network. Its main potential is as a complement to existing fold recognition techniques, with its ability to identify global symmetries within protein structures its greatest strength.

Algorithms↗
PubMed2025

Incorporating prior information in gene expression network-based cancer heterogeneity analysis.

Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as "direct" and "indirect," where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.

Humans↗
PubMed2006

Global comparative transcriptome analysis identifies gene network regulating secondary xylem development in Arabidopsis thaliana.

Our knowledge of the genetic control of wood formation (i.e., secondary growth) is limited. Here, we present a novel approach to unraveling the gene network regulating secondary xylem development in Arabidopsis, which incorporates complementary platforms of comparative-transcriptome analyses such as "digital northern" and "digital in situ" analysis. This approach effectively eliminated any genes that are expressed in either non-stem tissues/organs ("digital northern") or phloem and non-vascular regions ("digital in situ"), thereby identifying 52 genes that are upregulated only in the xylem cells of secondary growth tissues as "core xylem gene set". The proteins encoded by this gene set participate in signal transduction, transcriptional regulation, cell wall metabolism, and unknown functions. Five of the seven signal transduction-related genes represented in the core xylem gene set encode the essential components of ROP (Rho-related GTPase from plants) signaling cascade. Furthermore, the analysis of promoter sequences of the core xylem gene set identified a novel cis-regulatory element, ACAAAGAA. The functional significances of this gene set were verified by several independent experimental and bioinformatics methods.

Arabidopsis↗
PubMed2026

Comparative effects of pharmacological interventions in the prophylactic treatment of tension-type headache: systematic review and network meta-analysis.

BACKGROUND: Tension-type headache (TTH) is the most common neurological disorder. The comparative effect of pharmacological interventions for TTH prophylaxis remains unclear. We aimed to assess the comparative effects of pharmacological interventions in the prophylactic treatment of TTH. METHODS: Ovid Medline, Embase, and Cochrane were searched from inception to 12 December, 2025. Randomized controlled trials (RCTs) of medications compared to placebo or another medication for preventing TTH were included. The primary outcome was headache days per month. A Bayesian random-effect model was employed as the primary analysis of chronic TTH. RESULTS: Thirty-five RCTs were included, 33 (88.6%) RCTs involved chronic TTH patients, and 24 RCTs provided available data for meta-analysis. Amitriptyline 100 mg presented more reduction of monthly headache days than placebo at 4 and 8 weeks (4 weeks: MD -6.59, 95% CrI -11.22 to -0.64; 8 weeks: MD -6.14, 95% CrI -10.27 to -0.87). BTX-A 100 U can reduce monthly headache days (MD -3.79, 95% CrI -7.16 to -0.33). Amitriptyline 100 mg was the highest-ranked treatment for monthly headache days at 4 (SUCRA 0.85), 8 (SUCRA 0.85), and 24 (SUCRA 0.87) weeks; 12 weeks was lidocaine 25 ml (SUCRA 0.75). Amitriptyline 100 mg and BTX-A 500 U showed a higher adverse event rate than placebo. CONCLUSION: Amitriptyline 100 mg and BTX-A 100 U may be options to reduce monthly headache days in patients with chronic TTH. Given the low to very low certainty of evidence, high risk of bias, and high heterogeneity, more studies are needed. TRIAL REGISTRATION: PROSPERO (CRD42025639586).

Humans↗
PubMed2026

The efficacy of non-invasive brain stimulation interventions in obsessive-compulsive disorder management: A network meta-analysis of randomized controlled trials.

Non-invasive brain stimulation (NIBS) has been widely used as an alternative treatment for obsessive compulsive disorder (OCD). However, the most effective NIBS parameters are unclear. To compare the efficacy of NIBS in OCD. We conducted a systematic review and network meta-analyses (NMA) to combine direct and indirect comparisons of NIBS.Systematic searches were conducted in Cochrane CENTRAL, EMBASE, PubMed, and Web of Science from inception to June 20, 2025. Forty-two randomized sham-controlled trials (n = 1456) were included. All statistical analyses were conducted with R statistical software. Bayesian NMAs mainly using the BUGSnet package and gemtc package. Five NIBS protocols produced statistically significant reductions in Yale-Brown Obsessive Compulsive Scale (Y-BOCS) scores compared with sham stimulation: high-frequency rTMS over the FzFCz (Hf-rTMS-FzFCz; MD -11.77, 95% CrI -20.62 to -3.09), low-frequency rTMS over F3F4 (Lf-rTMS-F3F4; MD -9.93, 95% CrI -18.07 to -1.65), low-frequency rTMS over FCz (Lf-rTMS-FCz; MD -3.25, 95% CrI -6.06 to -0.40), high-frequency deep TMS over FzFC (Hf-dTMS-FzFC; MD -6.48, 95% CrI -12.32 to -0.50), and 2 mA anodal tDCS over F3 with cathodal over Fp2 (MD -9.34, 95% CrI -16.01 to -3.03).For secondary outcomes, high-frequency deep rTMS over FzFCz produced the largest reduction both in depressive symptoms (SMD -1.24, 95% CrI -1.92 to -0.55) and anxiety scores (SMD -1.88, 95% CrI -2.62 to -1.11), but had no effect on Clinical Global Impression-Severity (CGI-S) scores.Specific NIBS protocols are safe and effective adjunctive treatments for OCD, with promising yet inconclusive improvements in comorbid depressive symptoms. Further high-quality, head-to-head trials are needed.

Humans↗
PubMed1990

The application of neural networks to myoelectric signal analysis: a preliminary study.

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multidegree of freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameters for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least squares (SLS) algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single site MES based on two features, specifically the first time series parameter, and the signal power. Using these features, the perceptron is trained to distinguish between four separate arm functions. The two-dimensional decision boundaries used by the perceptron classifier are delineated. It is also demonstrated that the perceptron is able to rapidly compensate for variations when new data are incorporated into the training set. This adaptive quality suggests that perceptrons may provide a useful tool for future MES analysis.

Algorithms↗
PubMed1992

Bifurcation analysis of a neural network model.

This paper describes the analysis of the well known neural network model by Wilson and Cowan. The neural network is modeled by a system of two ordinary differential equations that describe the evolution of average activities of excitatory and inhibitory populations of neurons. We analyze the dependence of the model's behavior on two parameters. The parameter plane is partitioned into regions of equivalent behavior bounded by bifurcation curves, and the representative phase diagram is constructed for each region. This allows us to describe qualitatively the behavior of the model in each region and to predict changes in the model dynamics as parameters are varied. In particular, we show that for some parameter values the system can exhibit long-period oscillations. A new type of dynamical behavior is also found when the system settles down either to a stationary state or to a limit cycle depending on the initial point.

Biological Clocks↗
PubMed2002

Toward an understanding of microbial communities through analysis of communication networks.

Bacteria receive signals from diverse members of their biotic environment. They sense their own species through the process of quorum sensing, which detects the density of bacterial cells and regulates functions such as bioluminescence, virulence, and competence. Bacteria also respond to the presence of other microorganisms and eukaryotic hosts. Most studies of microbial communication focus on signaling between the microbe and one other organism for empirical simplicity and because few experimental systems offer the opportunity to study communication among various types of organisms. But in the real biological world, microorganisms must carry on multiple molecular conversations simultaneously between diverse organisms, thereby constructing communication networks. We propose that biocontrol of plant disease, the process of suppressing disease through application of a microorganism, offers a model for the study of communication among multiple organisms. Successful biocontrol requires the sending and receiving of signals between the biocontrol agent and the pathogen, plant host, and microbial community surrounding the host. We are using Bacillus cereus, a biocontrol agent, and the organisms it must interact with, to dissect a communication network. This system offers an excellent starting point for study because its members are defined and well studied. An understanding of signaling in the B. cereus biocontrol system may provide a model for network communication among organisms that share a habitat and provide a new angle of analysis for understanding the interconnections that define communities.

Bacillus cereus↗
PubMed1987

Extending the quasi-steady state concept to analysis of metabolic networks.

A means is proposed for evaluating enzyme effectiveness in vivo via a simplified dynamic description of the metabolic reaction network within which the enzyme operates. The basis of the method is application of sensitivity analysis to a quasi-steady approximation of a complete dynamic model, and its implementation centers on interpreting the transient relations of selected intermediates following a perturbation to the system of interest: for many important situations such relations can be simply interpreted to give a useful global measure of enzyme effectiveness. This method is found to be successful for estimating phosphofructokinase and pyruvate kinase activity in the human red cell, and it appears promising as a basis for developing a means for detecting enzyme abnormalities caused by environmental or genetic factors. This method may also prove useful for comparative studies of glycolysis in different types of cells. The analysis presented is based on available models of red cell glycolysis, but the results are not highly sensitive to ambiguities in the system model. The approach suggested appears to provide an effective means for describing system dynamics and determining the behavior of an individual enzyme in an intact system by making a first-order allowance for interaction with the system as a whole. Requirements for success of this approach remain to be identified in detail, but effective time-scale separation is probably the key.

Erythrocytes↗
PubMed2005

The use of discriminant analysis and neural networks to forecast the severity of the Poaceae pollen season in a region with a typical Mediterranean climate.

Biological particles in the air such as pollen grains can cause environmental problems in the allergic population. Medical studies report that a prior knowledge of pollen season severity can be useful in the management of pollen-related diseases. The aim of this work was to forecast the severity of the Poaceae pollen season by using weather parameters prior to the pollen season. To carry out the study a historical database of 21 years of pollen and meteorological data was used. First, the years were grouped into classes by using cluster analysis. As a result of the grouping, the 21 years were divided into 3 classes according to their potential allergenic load. Pre-season meteorological variables were used, as well as a series of characteristics related to the pollen season. When considering pre-season meteorological variables, winter variables were separated from early spring variables due to the nature of the Mediterranean climate. Second, a neural network model as well as a discriminant linear analysis were built to forecast Poaceae pollen season severity, according to the three classes previously defined. The neural network yielded better results than linear models. In conclusion, neural network models could have a high applicability in the area of prevention, as the allergenic potential of a year can be determined with a high degree of reliability, based on a series of meteorological values accumulated prior to the pollen season.

Climate↗
PubMed2005

Raman spectroscopy for diagnosis of atherosclerosis: a rapid analysis using neural networks.

Near-infrared Raman spectroscopy (NIRS) is one of the novel techniques that has a potential for in vivo diagnosis of atherosclerosis in human arteries. For such real time clinical applications, a rapid collection and analysis of the data is needed. One of the major problems with the fast data collection is that the noise generated by the detector has the same level as the Raman signal from the tissue, which makes the analysis difficult. In this work, NIRS measurements have been carried out on a total of 60 samples from human coronary arteries. Raman spectral data with the correlated histopathological analysis have been used as a basis to stimulate the cases of severe noise conditions. The main objective of this paper is the comparison of different processing algorithms that have been developed based on either wavelet transformation or principal component analysis for compressing the Raman spectral vectors and a rapid data classification based on different neural network architectures. The developed algorithms found to provide promising diagnosis results with classification errors smaller than 5%, even in the cases of Raman data with collection times as small as 20 ms. It has been concluded that the developed algorithms would be very much useful in the development of Raman spectroscopy systems for in vivo biological applications.

Algorithms↗
PubMed2004

Application of artificial neural networks to the analysis of dynamic MR imaging features of the breast.

The discriminative ability of established diagnostic criteria for MRI of the breast is assessed, and their relative relevance using artificial neural networks (ANNs) is determined. A total of 89 women with 105 histopathologically verified breast lesions (73 invasive cancers, 2 in situ cancers, and 30 benign lesions) were included in this study. A T1-weighted 3D FLASH sequence was acquired before and seven times after the intravenous administration of gadopentetate dimeglumine at a dose of 0.2 mmol/kg body weight. ANN models were built to test the discriminative ability of kinetic, morphologic, and combined MR features. The subjects were randomly divided into two parts: a training set of 59 lesions and a verification set of 46 lesions. The training set was used for learning, and the performance of each model was evaluated on the verification set by measuring the area under the ROC curve (Az). An optimally minimized model was constructed using the most relevant input variables that were determined by the automatic relevance determination (ARD) method. ANN models were compared with the performance of a human reader. Margin type, time-to-peak enhancement, and washout ratio showed the highest discriminative ability among diagnostic criteria and comprised the minimized model. Compared with the expert radiologist (Az = 0.799), using the same prediction scale, the minimized ANN model performed best (Az = 0.771), followed by the best kinetic (Az = 0.743), the maximized (Az = 0.727), and the morphologic model (Az = 0.678). The performance of a neural network prediction model is comparable to that of an expert radiologist. A neurostatistical approach is preferred for the analysis of diagnostic criteria when many parameters are involved and complex nonlinear relationships exist in the data set.

Adult↗
PubMed2003

Identification of all steady states in large networks by logical analysis.

The goal of generalized logical analysis is to model complex biological systems, especially so-called regulatory systems, such as genetic networks. This theory is mainly characterized by its capacity to find all the steady states of a given system and the functional positive and negative circuits, which generate multistationarity and a cycle in the state sequence graph, respectively. So far, this has been achieved by exhaustive enumeration, which severely limits the size of the systems that can be analysed. In this paper, we introduce a mathematical function, called image function, which allows the calculation of the value of the logical parameter associated with a logical variable depending on the state of the system. Thus the state table of the system is represented analytically. We then show how all steady states can be derived as solutions to a system of steady-state equations. Constraint programming, a recent method for solving constraint satisfaction problems, is applied for that purpose. To illustrate the potential of our approach, we present results from computer experiments carried out on very large randomly-generated systems (graphs) with hundreds, or even thousands, of interacting components, and show that these systems can be solved using moderate computing time. Moreover, we illustrate the approach through two published applications, one of which concerns the computation times of all steady states for a large genetic network.

Arabidopsis↗
PubMed1999

Quantitative analysis of cytokeratin network topology in the MCF7 cell line.

BACKGROUND: In the MCF7 human breast cancer cell line, several patterns of cytokeratin networks are observed, depending on the intracellular localization. Our hypothesis is that architectural variations of cytokeratin networks depend on local tensions or forces appearing spontaneously in the cytoplasm. The aim of this work was to discriminate between the different patterns and to quantitate these variations. MATERIALS AND METHODS: Image analysis procedures were developed to extract cytokeratin filament networks visualized by immunofluorescence and confocal microscopy. Two methods were used to segment sets of curvilinear objects. The first, the "mesh-approach," based on classical methods of mathematical morphology, takes into account global network topology. The second, the "filament-approach" (novel), is meant to account for individual element morphology. These methods and their combination allow the computation of several features at two levels of geometry: global (network topology) and local (filament morphology). RESULTS: Variations in cytokeratin networks are characterized by their connectivity, density, mesh structure, and filament shape. The connectivity and the density of a network describe its location in a local "stress-force" zone or in a "relaxed" zone. The mesh structure characterizes the intracellular localization of the network. Moreover, the filament shape reflects the intracellular localization and the occurrence of a "stress-force" zone. CONCLUSIONS: These features permitted the quantitation of differences within the network patterns and within the specific filament shapes according to the intracellular localization. Further experiments on cells submitted to external forces will test the hypothesis that the architectural variations of intermediate filaments reflect intracytoplasmic tensions.

Algorithms↗
PubMed2005

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-a case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake.

Bayesian regularized BP neural network(BRBPNN) technique was applied in the chlorophyll-a prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS 11.0 software, the BRBPNN model was established between chlorophyll-a and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.00078426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll-a declined in the order of alga amount > secchi disc depth (SD) > electrical conductivity (EC). Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-a concentration, total phosphorus (TP) and total nitrogen (TN) were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data(chlorophyll-a prediction) and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.

Bayes Theorem↗