Flagging unusual clusters based on linear mixed models using weighted and self-calibrated predictors.

Statistical models incorporating cluster-specific intercepts are commonly used in hierarchical settings, for example, observations clustered within patients or patients clustered within hospitals. Predicted values of these intercepts are often used to identify or "flag" extreme or outlying clusters, such as poorly performing hospitals or patients with rapid declines in their health. We consider a variety of flagging rules, assessing different predictors, and using different accuracy measures. Using theoretical calculations and comprehensive numerical evaluation, we show that previously proposed rules based on the 2 most commonly used predictors, the usual best linear unbiased predictor and fixed effects predictor, perform extremely poorly: the incorrect flagging rates are either unacceptably high (approaching 0.5 in the limit) or overly conservative (eg, much <0.05 for reasonable parameter values, leading to very low correct flagging rates). We develop novel methods for flagging extreme clusters that can control the incorrect flagging rates, including very simple-to-use versions that we call "self-calibrated." The new methods have substantially higher correct flagging rates than previously proposed methods for flagging extreme values, while controlling the incorrect flagging rates. We illustrate their application using data on length of stay in pediatric hospitals for children admitted for asthma diagnoses.

A Regularized Cox Hierarchical Model for Incorporating Annotation Information in Predictive Omic Studies.

Background: Associated with high-dimensional omics data there are often "meta-features" such as pathways and functional annotations that can be informative for predicting an outcome of interest. We extend to Cox regression the regularized hierarchical framework of Kawaguchi et al. (2022) (1) for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes. Methods: A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent. Results: In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level. Conclusions: The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

Clinging to the top: natal dispersal tracks climate gradient in a trailing-edge population of a migratory songbird.

PURPOSE: Trailing-edge populations at the low-latitude, receding edge of a shifting range face high extinction risk from climate change unless they are able to track optimal environmental conditions through dispersal. METHODS: We fit dispersal models to the locations of 3165 individually-marked black-throated blue warblers (Setophaga caerulescens) in the southern Appalachian Mountains in North Carolina, USA from 2002 to 2023. Black-throated blue warbler breeding abundance in this population has remained relatively stable at colder and wetter areas at higher elevations but has declined at warmer and drier areas at lower elevations. RESULTS: Median dispersal distance of young warblers was 917 m (range 23-3200 m), and dispersal tended to be directed away from warm and dry locations. In contrast, adults exhibited strong site fidelity between breeding seasons and rarely dispersed more than 100 m (range 10-1300 m). Consequently, adult dispersal kernels were much more compact and symmetric than natal dispersal kernels, suggesting adult dispersal is unlikely a driving force of declines in this population. CONCLUSION: Our findings suggest that directional natal dispersal may mitigate fitness costs for trailing-edge populations by allowing individuals to track changing climate and avoid warming conditions at warm-edge range boundaries.

Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search.

In multiple instance learning (MIL), a bag represents a sample that has a set of instances, each of which is described by a vector of explanatory variables, but the entire bag only has one label/response. Though many methods for MIL have been developed to date, few have paid attention to interpretability of models and results. The proposed Bayesian regression model stands on two levels of hierarchy, which transparently show how explanatory variables explain and instances contribute to bag responses. Moreover, two selection problems are simultaneously addressed; the instance selection to find out the instances in each bag responsible for the bag response, and the variable selection to search for the important covariates. To explore a joint discrete space of indicator variables created for selection of both explanatory variables and instances, the shotgun stochastic search algorithm is modified to fit in the MIL context. Also, the proposed model offers a natural and rigorous way to quantify uncertainty in coefficient estimation and outcome prediction, which many modern MIL applications call for. The simulation study shows the proposed regression model can select variables and instances with high performance (AUC greater than 0.86), thus predicting responses well. The proposed method is applied to the musk data for prediction of binding strengths (labels) between molecules (bags) with different conformations (instances) and target receptors. It outperforms all existing methods, and can identify variables relevant in modeling responses.

Establishment and application of drug utilization evaluation applying the weighted TOPSIS method: A study based on real-world data of tislelizumab.

BACKGROUND: How to comprehensively evaluate the rationality of drug use is a challenging issue. OBJECTIVE: To establish the evaluation index of the effective use of tislelizumab, so as to ensure its higher rationality and normalization in clinical application. METHODS: Based on the indications, drug instructions, and relevant guidelines of the National Basic Medical Insurance Restriction Catalogue, a retrospective analysis and evaluation of 286 cases of using tislelizumab injection in our hospital from January to December 2022 were conducted using the weighted technique for order of preference by similarity to ideal solution (TOPSIS) method. RESULTS: Among the 286 medical records evaluated, the main irrational manifestations were inappropriate indications (90 cases, 31.47%), auxiliary examination and laboratory examination did not meet the minimum requirements of combination chemotherapy drugs (40 cases, 13.99%), the drug course was not standard (39 cases, 13.64%). Among the included cases, 57.34% were reasonable cases (Ci⩾ 0.8), 10.84% were basic reasonable cases (0.6 ⩽Ci< 0.8), and 31.82% were unreasonable cases (Ci< 0.6). CONCLUSION: The TOPSIS method, with its attribute hierarchical model (AHM)-weighted approach, can be employed as the rational assessment technique for the injection of tislelizumab. The clinical application of tislelizumab in our hospital is still insufficient, which needs to be further improved management.

An easily implemented single-visit survey method for intermittently available and imperfectly detectable wildlife applied to the Florida east coast diamondback terrapin (Malaclemys terrapin tequesta).

Single-visit surveys of plots are often used for estimating the abundance of species of conservation concern. Less-than-perfect availability and detection of individuals can bias estimates if not properly accounted for. We developed field methods and a Bayesian model that accounts for availability and detection bias during single-visit visual plot surveys. We used simulated data to test the accuracy of the method under a realistic range of generating parameters and applied the method to Florida's east coast diamondback terrapin in the Indian River Lagoon system, where they were formerly common but have declined in recent decades. Simulations demonstrated that the method produces unbiased abundance estimates under a wide range of conditions that can be expected to occur in such surveys. Using terrapins as an example we show how to include covariates and random effects to improve estimates and learn about species-habitat relationships. Our method requires only counting individuals during short replicate surveys rather than keeping track of individual identity and is simple to implement in a variety of point count settings when individuals may be temporarily unavailable for observation. We provide examples in R and JAGS for implementing the model and to simulate and evaluate data to validate the application of the method under other study conditions.

A detailed hierarchical model of psychopathology in Chinese clinical sample: Based on the SCL-90-R measure.

OBJECTIVE: The Hierarchical Taxonomy of Psychopathology (HiTOP)model is an impressive effort to overcome shortcomings of traditional diagnostic systems. However, almost all of the quantitative empirical evidence used to structure the model comes from Western cultures and is built upon traditional diagnostic categories. This study aims to provide a detailed Chinese version of the HiTOP structure, ranging from symptoms based on The Symptom Checklist 90-R (SCL-90-R) up to the general factor. METHODS: We explored the detailed hierarchical structure of the SCL-90-R scale in adult (N = 34,222) and adolescent (N = 1973) clinical sample from Shanghai Mental Health Center, using extended bass-ackwards approach to draw the HiTOP model. RESULTS: The Chinese HiTOP structure had a general factor at the top, 4 higher-order spectra (Internalizing, Externalizing, Broad Thought Disorder and Somatization and Somatic Anxiety) and 6 subfactors (Distress, Somatoform, Hostility, Fear, Psychosis and OCD) across both adult and adolescent samples. In addition, the adult sample contained 2 other subfactors: a) Sleep, and b) Suicide and Guilt. At the symptom level, some items were posited to components diverged from the original SCL-90-R subscales. CONCLUSIONS: These findings offer the first description of the HiTOP structure in two Chinese samples and demonstrate that the SCL-90-R can be used to examine the HiTOP structure. The Somatization spectrum first emerged as a higher-order dimension, suggesting structural differences between Western and Eastern cultures. The results also suggest that transdiagnostic research should (1) further examine the positioning of somatoform symptoms using measures in other Eastern samples, and (2) place more emphasis on interpreting SCL-90-R results across different cultures.

Revisiting the complex time-varying effect of non-pharmaceutical interventions on COVID-19 transmission in the United States.

Introduction: Although the global COVID-19 emergency ended, the real-world effects of multiple non-pharmaceutical interventions (NPIs) and the relative contribution of individual NPIs over time were poorly understood, limiting the mitigation of future potential epidemics. Methods: Based on four large-scale datasets including epidemic parameters, virus variants, vaccines, and meteorological factors across 51 states in the United States from August 2020 to July 2022, we established a Bayesian hierarchical model with a spike-and-slab prior to assessing the time-varying effect of NPIs and vaccination on mitigating COVID-19 transmission and identifying important NPIs in the context of different variants pandemic. Results: We found that (i) the empirical reduction in reproduction number attributable to integrated NPIs was 52.0% (95%CI: 44.4, 58.5%) by August and September 2020, whereas the reduction continuously decreased due to the relaxation of NPIs in following months; (ii) international travel restrictions, stay-at-home requirements, and restrictions on gathering size were important NPIs with the relative contribution higher than 12.5%; (iii) vaccination alone could not mitigate transmission when the fully vaccination coverage was less than 60%, but it could effectively synergize with NPIs; (iv) even with fully vaccination coverage >60%, combined use of NPIs and vaccination failed to reduce the reproduction number below 1 in many states by February 2022 because of elimination of above NPIs, following with a resurgence of COVID-19 after March 2022. Conclusion: Our results suggest that NPIs and vaccination had a high synergy effect and eliminating NPIs should consider their relative effectiveness, vaccination coverage, and emerging variants.

Miss it like Messi: Extracting value from off-target shots in soccer.

Measuring soccer shooting skill is a challenging analytics problem due to the scarcity and highly contextual nature of scoring events. The introduction of more advanced data surrounding soccer shots has given rise to model-based metrics which better cope with these challenges. Specifically, metrics such as expected goals added, goals above expectation, and post-shot expected goals all use advanced data to offer an improvement over the classical conversion rate. However, all metrics developed to date assign a value of zero to off-target shots, which account for almost two-thirds of all shots, since these shots have no probability of scoring. We posit that there is non-negligible shooting skill signal contained in the trajectories of off-target shots and propose two shooting skill metrics that incorporate the signal contained in off-target shots. Specifically, we develop a player-specific generative model for shot trajectories based on a mixture of truncated bivariate Gaussian distributions. We use this generative model to compute metrics that allow us to attach non-zero value to off-target shots. We demonstrate that our proposed metrics are more stable than current state-of-the-art metrics and have increased predictive power.

Minimal clinically important change of knee flexion in people with knee osteoarthritis after non-surgical interventions using a meta-analytical approach.

BACKGROUND: Minimal clinically important change (MCIC) represents the minimum patient-perceived improvement in an outcome after treatment, in an individual or within a group over time. This study aimed to determine MCIC of knee flexion in people with knee OA after non-surgical interventions using a meta-analytical approach. METHODS: Four databases (MEDLINE, Cochrane, Web of Science and CINAHL) were searched for studies of randomised clinical trials of non-surgical interventions with intervention duration of ≤ 3 months that reported change in (Δ) (mean change between baseline and immediately after the intervention) knee flexion with Δ pain or Δ function measured using tools that have established MCIC values. The risk of bias in the included studies was assessed using version 2 of the Cochrane risk-of-bias tool for randomised trials (RoB 2). Bayesian meta-analytic models were used to determine relationships between Δ flexion with Δ pain and Δ function after non-surgical interventions and MCIC of knee flexion. RESULTS: Seventy-two studies (k = 72, n = 5174) were eligible. Meta-analyses included 140 intervention arms (k = 61, n = 4516) that reported Δ flexion with Δ pain using the visual analog scale (pain-VAS) and Δ function using the Western Ontario and McMaster Universities Osteoarthritis Index function subscale (function-WOMAC). Linear relationships between Δ pain at rest-VAS (0-100 mm) with Δ flexion were - 0.29 (- 0.44; - 0.15) (ß: posterior median (CrI: credible interval)). Relationships between Δ pain during activity VAS and Δ flexion were - 0.29 (- 0.41, - 0.18), and Δ pain-general VAS and Δ flexion were - 0.33 (- 0.42, - 0.23). The relationship between Δ function-WOMAC (out of 100) and Δ flexion was - 0.15 (- 0.25, - 0.07). Increased Δ flexion was associated with decreased Δ pain-VAS and increased Δ function-WOMAC. The point estimates for MCIC of knee flexion ranged from 3.8 to 6.4°. CONCLUSIONS: The estimated knee flexion MCIC values from this study are the first to be reported using a novel meta-analytical method. The novel meta-analytical method may be useful to estimate MCIC for other measures where anchor questions are problematic. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022323927.

Executive and hierarchical models for participatory response to health emergencies in the workplace: Lessons from COVID-19.

Introduction: Workplaces are high-risk environments for epidemic transmission, and the COVID-19 pandemic has highlighted the significant impacts that health emergencies can have on both the healthcare system and the economy. This study presents executive and hierarchical models for participatory response to health emergencies in the workplace, with a focus on COVID-19. Methods: The study was conducted in three phases. Content analysis of interviews with 101 stakeholders and national documents was used to identify key themes and dimensions for an executive model. A focus group discussion and review of international documents were then used to refine and expand the executive and hierarchical models. The alignment and trustworthiness of the final models, as well as feedback, were gathered from 117 informants working in various workplaces. Results: The executive model highlighted that context understanding, management commitment, and participation play critical roles in developing tailored prevention and response plans, and adequate support is necessary for successful plan implementation. Monitoring and review processes should be established to ensure proper functioning. The hierarchical model emphasizes the need for collaborative efforts from various stakeholders to effectively implement pandemic prevention and participatory response plans. Conclusion: Overall, the executive and hierarchical participatory models presented in this study provide a framework for effectively controlling pandemics and other health emergencies in the workplace, enhancing both health resilience and the sustainability of economic activities.

Dynamic models augmented by hierarchical data: an application of estimating HIV epidemics at sub-national level.

Dynamic models have been successfully used in producing estimates of HIV epidemics at the national level due to their epidemiological nature and their ability to estimate prevalence, incidence, and mortality rates simultaneously. Recently, HIV interventions and policies have required more information at sub-national levels to support local planning, decision-making and resource allocation. Unfortunately, many areas lack sufficient data for deriving stable and reliable results, and this is a critical technical barrier to more stratified estimates. One solution is to borrow information from other areas within the same country. However, directly assuming hierarchical structures within the HIV dynamic models is complicated and computationally time-consuming. In this article, we propose a simple and innovative way to incorporate hierarchical information into the dynamical systems by using auxiliary data. The proposed method efficiently uses information from multiple areas within each country without increasing the computational burden. As a result, the new model improves predictive ability and uncertainty assessment.

A generalized calibrated Bayesian hierarchical modeling approach to basket trials with multiple endpoints.

A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk-benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial-normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operating characteristics.

A review towards developing a hierarchical model for sustainable hospital solid waste management in rural areas of Zimbabwe.

Management of solid waste from rural hospitals is amongst problems affecting Zimbabwe due to diseases, population, and hospital increase. Solid waste from rural hospitals is receiving little attention translating to environmental health problems. Therefore, 101 secondary sources were used to write a paper aiming to proffer a hierarchical model to achieve sustainable solid waste management at rural hospitals. Rural hospitals' solid waste encompasses electronic waste, sharps, pharmaceutical, pathological, radioactive, chemical, infectious, and general waste. General solid waste from rural hospitals is between 77.35 and 79% whilst hazardous waste is between 21 and 22.65%. Solid waste increase add burden to nearly incapacitated rural hospitals. Rural hospital solid waste management processes include storage, transportation, treatment methods like autoclaving and chlorination, waste reduction alternatives, and disposal. Disposal strategies involve open pits, open burning, dumping, and incineration. Rural hospital solid waste management is guided by legislation, policies, guidelines, and conventions. Effectiveness of legal framework is limited by economic and socio-political problems. Rural hospital solid waste management remain inappropriate causing environmental health risks. Developed hierarchical model can narrow the route to attain sustainable management of rural hospitals' solid waste. Proposed hierarchical model consists of five-layered strategies and acted as a guide for identifying and ranking approaches to manage rural hospitals' solid waste. Additionally, Zimbabwean government, Environmental Management Agency and Ministry of Health is recommended to collaborate to provide sufficient resources to rural hospitals whilst enforcing legal framework. Integration of all hierarchical model's elements is essential whereas all-stakeholder involvement and solid waste minimisation approaches are significant at rural hospitals.

Bayesian Methods for Information Borrowing in Basket Trials: An Overview.

Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.

Modeling Risk Factors for Intraindividual Variability: A Mixed-Effects Beta-Binomial Model Applied to Cognitive Function in Older People in the English Longitudinal Study of Ageing.

Cognitive functioning in older age profoundly impacts quality of life and health. While most research on cognition in older age has focused on mean levels, intraindividual variability (IIV) around this may have risk factors and outcomes independent of the mean value. Investigating risk factors associated with IIV has typically involved deriving a summary statistic for each person from residual error around a fitted mean. However, this ignores uncertainty in the estimates, prohibits exploring associations with time-varying factors, and is biased by floor/ceiling effects. To address this, we propose a mixed-effects location scale beta-binomial model for estimating average probability and IIV in a word recall test in the English Longitudinal Study of Ageing. After adjusting for mean performance, an analysis of 9,873 individuals across 7 (mean = 3.4) waves (2002-2015) found IIV to be greater at older ages, with lower education, in females, with more difficulties in activities of daily living, in later birth cohorts, and when interviewers recorded issues potentially affecting test performance. Our study introduces a novel method for identifying groups with greater IIV in bounded discrete outcomes. Our findings have implications for daily functioning and care, and further work is needed to identify the impact for future health outcomes.

Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes.

Background: Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. Methods: To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative treatment benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. Results: We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analysis demonstrates the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. Conclusion: The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

Bayesian random-effects meta-analysis with empirical heterogeneity priors for application in health technology assessment with very few studies.

In Bayesian random-effects meta-analysis, the use of weakly informative prior distributions is of particular benefit in cases where only a few studies are included, a situation often encountered in health technology assessment (HTA). Suggestions for empirical prior distributions are available in the literature but it is unknown whether these are adequate in the context of HTA. Therefore, a database of all relevant meta-analyses conducted by the Institute for Quality and Efficiency in Health Care (IQWiG, Germany) was constructed to derive empirical prior distributions for the heterogeneity parameter suitable for HTA. Previously, an extension to the normal-normal hierarchical model had been suggested for this purpose. For different effect measures, this extended model was applied on the database to conservatively derive a prior distribution for the heterogeneity parameter. Comparison of a Bayesian approach using the derived priors with IQWiG's current standard approach for evidence synthesis shows favorable properties. Therefore, these prior distributions are recommended for future meta-analyses in HTA settings and could be embedded into the IQWiG evidence synthesis approach in the case of very few studies.

Bayesian inference for prediction of survival probability in prime-boost vaccination regimes.

We focus on Bayesian inference for survival probabilities in a prime-boost vaccination regime in the development of an Ebola vaccine. We are interested in the heterologous prime-boost regimen (unmatched vaccine deliverys using the same antigen) due to its demonstrated durable immunity, well-tolerated safety profile, and suitability as a population vaccination strategy. Our research is motivated by the need to estimate the survival probability given the administered dosage. To do so, we establish two key relationships. Firstly, we model the connection between the designed dose concentration and the induced antibody count using a Bayesian response surface model. Secondly, we model the association between the antibody count and the probability of survival when experimental subjects are exposed to the Ebola virus in a controlled setting using a Bayesian probability of survival model. Finally, we employ a combination of the two models with dose concentration as the predictor of the survival probability for a future vaccinated population. We implement our two-level Bayesian model in Stan, and illustrate its use with simulated and real-world data. Performance of this model is evaluated via simulation. Our work offers a new application of drug synergy models to examine prime-boost vaccine efficacy, and does so using a hierarchical Bayesian framework that allows us to use dose concentration to predict survival probability.

Robust knowledge-guided biclustering for multi-omics data.

Biclustering is a useful method for simultaneously grouping samples and features and has been applied across various biomedical data types. However, most existing biclustering methods lack the ability to integratively analyze multi-modal data such as multi-omics data such as genome, transcriptome and epigenome. Moreover, the potential of leveraging biological knowledge represented by graphs, which has been demonstrated to be beneficial in various statistical tasks such as variable selection and prediction, remains largely untapped in the context of biclustering. To address both, we propose a novel Bayesian biclustering method called Bayesian graph-guided biclustering (BGB). Specifically, we introduce a new hierarchical sparsity-inducing prior to effectively incorporate biological graph information and establish a unified framework to model multi-view data. We develop an efficient Markov chain Monte Carlo algorithm to conduct posterior sampling and inference. Extensive simulations and real data analysis show that BGB outperforms other popular biclustering methods. Notably, BGB is robust in terms of utilizing biological knowledge and has the capability to reveal biologically meaningful information from heterogeneous multi-modal data.