Search

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time-varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross-sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research., (© 2024 The Author(s). International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.)

Background : In the last decades, the increasing number of adolescent and young adult (AYA) survivors of breast cancer (BC) has highlighted the cardiotoxic role of cancer therapies, making cardiovascular diseases (CVDs) among the most frequent, although rare, long-term sequalae. Leveraging innovative artificial intelligence (AI) tools and real-world data (RWD), we aimed to develop a causally interpretable model to identify young BC survivors at risk of developing CVDs. Methods : We designed and trained a Bayesian network (BN), an AI model, making use of expert knowledge and data from population-based (1036 patients) and clinical (339 patient) cohorts of female AYA (i.e., aged 18 to 39 years) 1-year survivors of BC, diagnosed in 2009-2019. The performance achieved by the BN model was validated against standard classification metrics, and two clinical applications were proposed. Results: The model showed a very good classification performance and a clear causal semantic. According to the predictions made by the model, focusing on the 25% of AYA BC survivors at higher risk of developing CVDs, we could identify 81% of the patients who would actually develop it. Moreover, a desktop-based app was implemented to calculate the individual patient's risk. Conclusions: In this study, we developed the first causal model for predicting the CVD risk in AYA survivors of BC, also proposing an innovative AI approach that could be useful for all researchers dealing with RWD. The model could be pivotal for clinicians who aim to plan personalized follow-up strategies for AYA BC survivors.

The relationship between skin diseases and mental illnesses has been extensively studied using cross-sectional epidemiological data. Typically, such data can only measure association (rather than causation) and include only a subset of the diseases we may be interested in. In this paper, we complement the evidence from such analyses by learning an overarching causal network model over twelve health conditions from the Google Search Trends Symptoms public data set. We learned the causal network model using a dynamic Bayesian network, which can represent both cyclic and acyclic causal relationships, is easy to interpret and accounts for the spatio-temporal trends in the data in a probabilistically rigorous way. The causal network confirms a large number of cyclic relationships between the selected health conditions and the interplay between skin and mental diseases. For acne, we observe a cyclic relationship with anxiety and attention deficit hyperactivity disorder (ADHD) and an indirect relationship with depression through sleep disorders. For dermatitis, we observe directed links to anxiety, depression and sleep disorders and a cyclic relationship with ADHD. We also observe a link between dermatitis and ADHD and a cyclic relationship between acne and ADHD. Furthermore, the network includes several direct connections between sleep disorders and other health conditions, highlighting the impact of the former on the overall health and well-being of the patient. The average R 2 for a condition given the values of all conditions in the previous week is 0.67: in particular, 0.42 for acne, 0.85 for asthma, 0.58 for ADHD, 0.87 for burn, 0.76 for erectile dysfunction, 0.88 for scars, 0.57 for alcohol disorders, 0.57 for anxiety, 0.53 for depression, 0.74 for dermatitis, 0.60 for sleep disorders and 0.66 for obesity. Mapping disease interplay, indirect relationships, and the key role of mediators, such as sleep disorders, will allow healthcare professionals to address disease management holistically and more effectively. Even if we consider all skin and mental diseases jointly, each disease subnetwork is unique, allowing for more targeted interventions., (© 2024. The Author(s).)

Background: The effect of estrogen and beta-human chorionic gonadotropin on micropapillary thyroid carcinoma (mPTC) is not defined. Pregnancy and menopause could represent critical moments during active surveillance (AS) for women with mPTC., Objective: To evaluate the effect of either pregnancy or menopause on growth of mPTCs on AS., Patients and Methods: Women with mPTC on AS who became pregnant or underwent menopause during AS were evaluated in this retrospective observational study. The primary outcome was disease progression according to the AS protocol. The secondary outcome was the shrinkage of mPTCs. We compared the menopause group of patients with 2 unmatched control groups: (1) the pre-menopause group of patients on AS who had not experienced menopause yet and (2) the post-menopause group of patients who started AS while already in menopause., Results: Five patients who became pregnant and 9 who underwent menopause during AS were enrolled. No patient from either group had a disease progression, and all pregnant patients showed stable disease after pregnancy. Four patients of the menopause group (44%) experienced mPTC shrinkage. The percentage of patients with mPTC shrinkage was significantly higher in the menopause group than in the 2 control groups., Conclusions: mPTC AS appears to be safe and feasible in patients who become pregnant or undergo menopause during surveillance. Our data suggest a possible association between menopause and mPTC shrinkage during AS., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Endocrine Society.)

Tension type headache (TTH) is a prevalent but poorly understood pain disease. Current understanding supports the presence of multiple associations underlying its pathogenesis. Our aim was to compare competing multivariate pathway models that explains the complexity of TTH. Headache features (intensity, frequency, or duration - headache diary), headache-related disability (Headache Disability Inventory-HDI), anxiety/depression (Hospital Anxiety and Depression Scale), sleep quality (Pittsburgh Sleep Quality Index), widespread pressure pain thresholds (PPTs) and trigger points (TrPs) were collected in 208 individuals with TTH. Four latent variables were formed from the observed variables - Distress (anxiety, depression), Disability (HDI subscales), Severity (headache features), and Sensitivity (all PPTs). Structural equation modelling (SEM) and Bayesian network (BN) analyses were used to build and compare a theoretical (model theory ) and a data-driven (model BN ) latent variable model. The model BN (root mean square error of approximation [RMSEA] = 0.035) provided a better statistical fit than model theory (RMSEA = 0.094). The only path common between model bn and model theory was the influence of years with pain on TrPs. The model BN revealed that the largest coefficient magnitudes were between the latent variables of Distress and Disability (β=1.524, P = .006). Our theoretical model proposes a relationship whereby psycho-physical and psychological factors result in clinical features of headache and ultimately affect disability. Our data-driven model proposes a more complex relationship where poor sleep, psychological factors, and the number of years with pain takes more relevance at influencing disability. Our data-driven model could be leveraged in clinical trials investigating treatment approaches in TTH. PERSPECTIVE: A theoretical model proposes a relationship where psycho-physical and psychological factors result in clinical manifestations of headache and ultimately affect disability. A data-driven model proposes a more complex relationship where poor sleep, psychological factors, and number of years with pain takes more relevance at influencing disability., (Copyright © 2022 United States Association for the Study of Pain, Inc. Published by Elsevier Inc. All rights reserved.)

Objective: The purpose of this study was to develop a data-driven Bayesian network approach to understand the potential multivariate pathways of the effect of manual physical therapy in women with carpal tunnel syndrome (CTS)., Methods: Data from a randomized clinical trial (n = 104) were analyzed comparing manual therapy including desensitization maneuvers of the central nervous system versus surgery in women with CTS. All variables included in the original trial were included in a Bayesian network to explore its multivariate relationship. The model was used to quantify the direct and indirect pathways of the effect of physical therapy and surgery on short-term, mid-term, and long-term changes in the clinical variables of pain, related function, and symptom severity., Results: Manual physical therapy improved function in women with CTS (between-groups difference: 0.09; 95% CI = 0.07 to 0.11). The Bayesian network showed that early improvements (at 1 month) in function and symptom severity led to long-term (at 12 months) changes in related disability both directly and via complex pathways involving baseline pain intensity and depression levels. Additionally, women with moderate CTS had 0.14-point (95% CI = 0.11 to 0.17 point) poorer function at 12 months than those with mild CTS and 0.12-point (95% CI = 0.09 to 0.15 point) poorer function at 12 months than those with severe CTS., Conclusion: Current findings suggest that short-term benefits in function and symptom severity observed after manual therapy/surgery were associated with long-term improvements in function, but mechanisms driving these effects interact with depression levels and severity as assessed using electromyography. Nevertheless, it should be noted that between-group differences depending on severity determined using electromyography were small, and the clinical relevance is elusive. Further data-driven analyses involving a broad range of biopsychosocial variables are recommended to fully understand the pathways underpinning CTS treatment effects., Impact: Short-term effects of physical manual therapy seem to be clinically relevant for obtaining long-term effects in women with CTS., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Physical Therapy Association. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Purpose: Individualised physiotherapy is an effective treatment for low back pain. We sought to determine how this treatment works by using randomised controlled trial data to develop a Bayesian Network model., Methods: 300 randomised controlled trial participants (153 male, 147 female, mean age 44.1) with low back pain (of duration 6-26 weeks) received either individualised physiotherapy or advice. Variables with potential to explain how individualised physiotherapy works were included in a multivariate Bayesian Network model. Modelling incorporated the intervention period (0-10 weeks after study commencement-"early" changes) and the follow-up period (10-52 weeks after study commencement-"late" changes). Sequences of variables in the Bayesian Network showed the most common direct and indirect recovery pathways followed by participants with low back pain receiving individualised physiotherapy versus advice., Results: Individualised physiotherapy directly reduced early disability in people with low back pain. Individualised physiotherapy exerted indirect effects on pain intensity, recovery expectations, sleep, fear, anxiety, and depression via its ability to facilitate early improvement in disability. Early improvement in disability, led to an early reduction in depression both directly and via more complex pathways involving fear, recovery expectations, anxiety, and pain intensity. Individualised physiotherapy had its greatest influence on early change variables (during the intervention period)., Conclusion: Individualised physiotherapy for low back pain appears to work predominately by facilitating an early reduction in disability, which in turn leads to improvements in other biopsychosocial outcomes. The current study cannot rule out that unmeasured mechanisms (such as tissue healing or reduced inflammation) may mediate the relationship between individualised physiotherapy treatment and improvement in disability. Further data-driven analyses involving a broad range of plausible biopsychosocial variables are recommended to fully understand how treatments work for people with low back pain., Trials Registration: ACTRN12609000834257., Competing Interests: The authors have declared that no competing interests exist.

Active surveillance (AS) is considered an alternative to immediate surgery in micropapillary thyroid carcinoma (mPTC). However, the definition of clinical mPTC progression during AS is controversial. We evaluated changes in tumor size using both tumor diameters and volume in 109 patients with mPTC followed in an AS protocol for a mean period of 31 ± 18 months. At the time of data lock, 19/109 (17.4%) mPTC reached and maintained a volume increase of ≥50%. However, only 3/19 (15.7%) showed progression, according to the diameter increase. The remaining 16 showed a slight diameter growth without reaching the original protocol progression criteria. The mean mPTC growth rate in stable cases was 0.37 mm 3 /month, while it was significantly greater in the mPTC, which achieved a volume change ≥50% with respect to the other. The two mPTC that developed a significant diameter increase had a growth rate of 41 and 18 mm 3 /month. Instead, the growth rates of the three mPTC that developed lymph node metastases were 0, 2.5 and 16 mm 3 /month. The ≥50% volume increase appears to be a too sensitive marker of disease progression, with a downstream higher surgery rate. The assessment of growth rate could distinguish mPTC with high and low growth rates, which would allow us to tailor the algorithm of the evaluations to a more appropriate timing.

This paper adopts a novel, interdisciplinary approach to explore the relationship between stress-related psychosocial factors, physiological markers and occurrence of injury in athletes using a repeated measures prospective design. At four data collection time-points, across 1-year of a total 2-year data collection period, athletes completed measures of major life events, the reinforcement sensitivity theory personality questionnaire, muscle stiffness, heart rate variability and postural stability, and reported any injuries they had sustained since the last data collection. Two Bayesian networks were used to examine the relationships between variables and model the changes between data collection points in the study. Findings revealed muscle stiffness to have the strongest relationship with injury occurrence, with high levels of stiffness increasing the probability of sustaining an injury. Negative life events did not increase the probability of injury occurrence at any single time-point; however, when examining changes between time points, increases in negative life events did increase the probability of injury. In addition, the combination of increases in negative life events and muscle stiffness resulted in the greatest probability of sustaining an injury. Findings demonstrated the importance of both an interdisciplinary approach and a repeated measures design to furthering our understanding of the relationship between stress-related markers and injury occurrence., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Fisher, Gittoes, Evans, Bitchell, Mullen and Scutari.)

Management of thyroid nodules in the era of precision medicine is continuously changing. Neck ultrasound plays a pivotal role in the diagnosis and several ultrasound stratification systems have been proposed in order to predict malignancy and help clinicians in therapeutic and follow-up decision. Ultrasound elastosonography is another powerful diagnostic technique and can be an added value to stratify the risk of malignancy of thyroid nodules. Moreover, the development of new techniques in the era of "Deep Learning," has led to a creation of machine-learning algorithms based on ultrasound examinations that showed similar accuracy to that obtained by expert radiologists. Despite new technologies in thyroid imaging, diagnostic surgery in 50-70% of patients with indeterminate cytology is still performed. Molecular tests can increase accuracy in diagnosis when performed on "indeterminate" nodules. However, the more updated tools that can be used to this purpose in order to "rule out" (Afirma GSC) or "rule in" (Thyroseq v3) malignancy, have a main limitation: the high costs. In the last years various image-guided procedures have been proposed as alternative and less invasive approaches to surgery for symptomatic thyroid nodules. These minimally invasive techniques (laser and radio-frequency ablation, high intensity focused ultrasound and percutaneous microwave ablation) results in nodule shrinkage and improvement of local symptoms, with a lower risk of complications and minor costs compared to surgery. Finally, ultrasound-guided ablation therapy was introduced with promising results as a feasible treatment for low-risk papillary thyroid microcarcinoma or cervical lymph node metastases., (Copyright © 2020 Tumino, Grani, Di Stefano, Di Mauro, Scutari, Rago, Fugazzola, Castagna and Maino.)

Background: Patient engagement helps to improve health outcomes and health care quality. However, the overall relationships among patient engagement measures and health outcomes remain unclear. This study aims to integrate expert knowledge and survey data for the identification of measures that have extensive associations with other variables and can be prioritized to engage patients., Methods: We used the 2014 International Health Policy Survey (IHPS), which provided information on elder adults in 11 countries with details in patient characteristics, healthcare experiences, and patient-physician communication. Patient engagement or support was measured with eight variables including patients' treatment choices, involvement, and treatment priority setting. Three types of care were identified: primary, specialist and chronic illness care. Specialists were doctors specializing in one area of health care. Chronic illness included eight chronic conditions surveyed. Expert knowledge was used to assist variable selection. We used Bayesian network models consisting of nodes that represented variables of interest and arcs that represented their relationships., Results: Among 25,530 participants, the mean age was 68.51 years and 57.40% were females. The distributions of age, sex, education, and patient engagement were significantly different across countries. For chronic illness care, written plans provided by professionals were linked to treatment feasibility and helpfulness. Whether professionals contacted patients was associated with the availability of professionals they could reach for chronic illness care. For specialist care, if specialists provided treatment choices, patients were more likely to be involved and discuss about what mattered to them., Conclusion: The strategies to engage patients may depend on the types of care, specialist or chronic illness care. For the study on the observational IHPS data, network modeling is useful to integrate expert knowledge. We suggest considering other theory-based patient engagement in major surveys, as well as engaging patients in their healthcare by providing written plans and actively communicating with patients for chronic illnesses, and encouraging specialists to discuss and provide treatment options., Competing Interests: The authors have declared that no competing interests exist.

In this paper we use Bayesian networks to determine and visualise the interactions among various Class III malocclusion maxillofacial features during growth and treatment. We start from a sample of 143 patients characterised through a series of a maximum of 21 different craniofacial features. We estimate a network model from these data and we test its consistency by verifying some commonly accepted hypotheses on the evolution of these disharmonies by means of Bayesian statistics. We show that untreated subjects develop different Class III craniofacial growth patterns as compared to patients submitted to orthodontic treatment with rapid maxillary expansion and facemask therapy. Among treated patients the CoA segment (the maxillary length) and the ANB angle (the antero-posterior relation of the maxilla to the mandible) seem to be the skeletal subspaces that receive the main effect of the treatment.

Background: There is a growing emphasis on the need to engage patients in order to improve the quality of health care and improve health outcomes. However, we are still lacking a comprehensive understanding on how different measures of patient experiences interact with one another or relate to health status. This study takes a network perspective to 1) study the associations between patient characteristics and patient experience in health care and 2) identify factors that could be prioritized to improve health status., Methods: This study uses data from the two-year panels from the Medical Expenditure Panel Survey (MEPS) initiated between 2004 and 2011 in the United States. The 88 variables regarding patient health and experience with health care were identified through the MEPS documentation. Sex, age, race/ethnicity, and years of education were also included for analysis. The bnlearn package within R (v3.20) was used to 1) identify the structure of the network of variables, 2) assess the model fit of candidate algorithms, 3) cross-validate the network, and 4) fit conditional probabilities with the given structure., Results: There were 51,023 MEPS interviewees aged 18 to 85 years (mean = 44, 95% CI = 43.9 to 44.2), with years of education ranging from 1 to 19 (mean = 7.4, 95% CI = 7.40 to 7.46). Among all, 55% and 74% were female and white, respectively. There were nine networks identified and 17 variables not linked to others, including death in the second years, sex, entry years to the MEPS, and relations of proxies. The health status in the second years was directly linked to that in the first years. The health care ratings were associated with how often professionals listened to them and whether professionals' explanation was understandable., Conclusions: It is feasible to construct Bayesian networks with information on patient characteristics and experiences in health care. Network models help to identify significant predictors of health care quality ratings. With temporal relationships established, the structure of the variables can be meaningful for health policy researchers, who search for one or a few key priorities to initiate interventions or health care quality improvement programs.

The prediction of phenotypic traits using high-density genomic data has many applications such as the selection of plants and animals of commercial interest; and it is expected to play an increasing role in medical diagnostics. Statistical models used for this task are usually tested using cross-validation, which implicitly assumes that new individuals (whose phenotypes we would like to predict) originate from the same population the genomic prediction model is trained on. In this paper we propose an approach based on clustering and resampling to investigate the effect of increasing genetic distance between training and target populations when predicting quantitative traits. This is important for plant and animal genetics, where genomic selection programs rely on the precision of predictions in future rounds of breeding. Therefore, estimating how quickly predictive accuracy decays is important in deciding which training population to use and how often the model has to be recalibrated. We find that the correlation between true and predicted values decays approximately linearly with respect to either FST or mean kinship between the training and the target populations. We illustrate this relationship using simulations and a collection of data sets from mice, wheat and human genetics., Competing Interests: The authors have declared that no competing interests exist.

Models for genome-wide prediction and association studies usually target a single phenotypic trait. However, in animal and plant genetics it is common to record information on multiple phenotypes for each individual that will be genotyped. Modeling traits individually disregards the fact that they are most likely associated due to pleiotropy and shared biological basis, thus providing only a partial, confounded view of genetic effects and phenotypic interactions. In this article we use data from a Multiparent Advanced Generation Inter-Cross (MAGIC) winter wheat population to explore Bayesian networks as a convenient and interpretable framework for the simultaneous modeling of multiple quantitative traits. We show that they are equivalent to multivariate genetic best linear unbiased prediction (GBLUP) and that they are competitive with single-trait elastic net and single-trait GBLUP in predictive performance. Finally, we discuss their relationship with other additive-effects models and their advantages in inference and interpretation. MAGIC populations provide an ideal setting for this kind of investigation because the very low population structure and large sample size result in predictive models with good power and limited confounding due to relatedness., (Copyright © 2014 by the Genetics Society of America.)

Molecular entities work in concert as a system and mediate phenotypic outcomes and disease states. There has been recent interest in modelling the associations between molecular entities from their observed expression profiles as networks using a battery of algorithms. These networks have proven to be useful abstractions of the underlying pathways and signalling mechanisms. Noise is ubiquitous in molecular data and can have a pronounced effect on the inferred network. Noise can be an outcome of several factors including: inherent stochastic mechanisms at the molecular level, variation in the abundance of molecules, heterogeneity, sensitivity of the biological assay or measurement artefacts prevalent especially in high-throughput settings. The present study investigates the impact of discrepancies in noise variance on pair-wise dependencies, conditional dependencies and constraint-based Bayesian network structure learning algorithms that incorporate conditional independence tests as a part of the learning process. Popular network motifs and fundamental connections, namely: (a) common-effect, (b) three-chain, and (c) coherent type-I feed-forward loop (FFL) are investigated. The choice of these elementary networks can be attributed to their prevalence across more complex networks. Analytical expressions elucidating the impact of discrepancies in noise variance on pairwise dependencies and conditional dependencies for special cases of these motifs are presented. Subsequently, the impact of noise on two popular constraint-based Bayesian network structure learning algorithms such as Grow-Shrink (GS) and Incremental Association Markov Blanket (IAMB) that implicitly incorporate tests for conditional independence is investigated. Finally, the impact of noise on networks inferred from publicly available single cell molecular expression profiles is investigated. While discrepancies in noise variance are overlooked in routine molecular network inference, the results presented clearly elucidate their non-trivial impact on the conclusions that in turn can challenge the biological significance of the findings. The analytical treatment and arguments presented are generic and not restricted to molecular data sets.

Objective: Modelling the associations from high-throughput experimental molecular data has provided unprecedented insights into biological pathways and signalling mechanisms. Graphical models and networks have especially proven to be useful abstractions in this regard. Ad hoc thresholds are often used in conjunction with structure learning algorithms to determine significant associations. The present study overcomes this limitation by proposing a statistically motivated approach for identifying significant associations in a network., Methods and Materials: A new method that identifies significant associations in graphical models by estimating the threshold minimising the L1 norm between the cumulative distribution function (CDF) of the observed edge confidences and those of its asymptotic counterpart is proposed. The effectiveness of the proposed method is demonstrated on popular synthetic data sets as well as publicly available experimental molecular data corresponding to gene and protein expression profiles., Results: The improved performance of the proposed approach is demonstrated across the synthetic data sets using sensitivity, specificity and accuracy as performance metrics. The results are also demonstrated across varying sample sizes and three different structure learning algorithms with widely varying assumptions. In all cases, the proposed approach has specificity and accuracy close to 1, while sensitivity increases linearly in the logarithm of the sample size. The estimated threshold systematically outperforms common ad hoc ones in terms of sensitivity while maintaining comparable levels of specificity and accuracy. Networks from experimental data sets are reconstructed accurately with respect to the results from the original papers., Conclusion: Current studies use structure learning algorithms in conjunction with ad hoc thresholds for identifying significant associations in graphical abstractions of biological pathways and signalling mechanisms. Such an ad hoc choice can have pronounced effect on attributing biological significance to the associations in the resulting network and possible downstream analysis. The statistically motivated approach presented in this study has been shown to outperform ad hoc thresholds and is expected to alleviate spurious conclusions of significant associations in such graphical abstractions., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Aging is accompanied by considerable heterogeneity with possible co-expression of differentiation pathways. The present study investigates the interplay between crucial myogenic, adipogenic, and Wnt-related genes orchestrating aged myogenic progenitor differentiation (AMPD) using clonal gene expression profiling in conjunction with Bayesian structure learning (BSL) techniques. The expression of three myogenic regulatory factor genes (Myogenin, Myf-5, MyoD1), four genes involved in regulating adipogenic potential (C/EBPα, DDIT3, FoxC2, PPARγ), and two genes in the Wnt signaling pathway (Lrp5, Wnt5a) known to influence both differentiation programs were determined across 34 clones by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR). Three control genes were used for normalization of the clonal expression data (18S, GAPDH, and B2M). Constraint-based BSL techniques, namely (a) PC Algorithm, (b) Grow-shrink (GS) algorithm, and (c) Incremental Association Markov Blanket (IAMB) were used to model the functional relationships (FRs) in the form of acyclic networks from the clonal expression profiles. A novel resampling approach that obviates the need for a user-defined confidence threshold is proposed to identify statistically significant FRs at small sample sizes. Interestingly, the resulting acyclic network consisted of FRs corresponding to myogenic, adipogenic, Wnt-related genes and their interaction. A significant number of these FRs were robust to normalization across the three house-keeping genes and the choice of the BSL technique. The results presented elucidate the delicate balance between differentiation pathways (i.e., myogenic as well as adipogenic) and possible cross-talk between pathways in AMPD.

Background: There has been recent interest in capturing the functional relationships (FRs) from high-throughput assays using suitable computational techniques. FRs elucidate the working of genes in concert as a system as opposed to independent entities hence may provide preliminary insights into biological pathways and signalling mechanisms. Bayesian structure learning (BSL) techniques and its extensions have been used successfully for modelling FRs from expression profiles. Such techniques are especially useful in discovering undocumented FRs, investigating non-canonical signalling mechanisms and cross-talk between pathways. The objective of the present study is to develop a graphical user interface (GUI), NATbox: Network Analysis Toolbox in the language R that houses a battery of BSL algorithms in conjunction with suitable statistical tools for modelling FRs in the form of acyclic networks from gene expression profiles and their subsequent analysis., Results: NATbox is a menu-driven open-source GUI implemented in the R statistical language for modelling and analysis of FRs from gene expression profiles. It provides options to (i) impute missing observations in the given data (ii) model FRs and network structure from gene expression profiles using a battery of BSL algorithms and identify robust dependencies using a bootstrap procedure, (iii) present the FRs in the form of acyclic graphs for visualization and investigate its topological properties using network analysis metrics, (iv) retrieve FRs of interest from published literature. Subsequently, use these FRs as structural priors in BSL (v) enhance scalability of BSL across high-dimensional data by parallelizing the bootstrap routines., Conclusion: NATbox provides a menu-driven GUI for modelling and analysis of FRs from gene expression profiles. By incorporating readily available functions from existing R-packages, it minimizes redundancy and improves reproducibility, transparency and sustainability, characteristic of open-source environments. NATbox is especially suited for interdisciplinary researchers and biologists with minimal programming experience and would like to use systems biology approaches without delving into the algorithmic aspects. The GUI provides appropriate parameter recommendations for the various menu options including default parameter choices for the user. NATbox can also prove to be a useful demonstration and teaching tool in graduate and undergraduate course in systems biology. It has been tested successfully under Windows and Linux operating systems. The source code along with installation instructions and accompanying tutorial can be found at http://bioinformatics.ualr.edu/natboxWiki/index.php/Main_Page.