While analyzing large clinical datasets allows for the identification of complex patterns to achieve increased risk prediction accuracy, it also presents challenges for existing risk modeling techniques due to patient heterogeneity and the ever-evolving volume and distributions of data. Bayesian nonparametric methods, such as the Dirichlet Process Mixture Model (DPMM), offer a promising solution for modeling data with mixed and overlapping distributions. However, the approach is computationally prohibitive when applied to large datasets, which greatly limits practical applications. In this study, we propose a scalable framework for efficiently constructing DPMMs from large clinical datasets. To improve computational efficiency, we divide the full dataset into smaller subsets and learn DPMMs within individual sets. Additionally, we adopt a recentered pseudo-barycenter to approximate the posterior density of the full dataset and design a new algorithm to generate a consistent clustering rule from the subset posteriors with unequal numbers of components. The method was validated through a simulation study and a case study predicting the survival of heart failure patients post-left ventricular assist device implantation. The results demonstrated improved accuracy compared to benchmark models such as the Cox proportional hazards model and random survival forests. Our modeling framework adaptively clusters patients with distinct risk profiles into subgroups and predicts their probabilities of developing adverse events from overlapping posterior mixtures, providing an effective approach for addressing patient heterogeneity and enhancing risk prediction accuracy.

MOTIVATION: Complex tissues are dynamic ecosystems consisting of molecularly distinct yet interacting cell types. Computational deconvolution aims to dissect bulk tissue data into cell type compositions and cell-specific expressions. With few exceptions, most existing deconvolution tools exploit supervised approaches requiring various types of references that may be unreliable or even unavailable for specific tissue microenvironments. RESULTS: We previously developed a fully unsupervised deconvolution method-Convex Analysis of Mixtures (CAM), that enables estimation of cell type composition and expression from bulk tissues. We now introduce CAM3.0 tool that improves this framework with three new and highly efficient algorithms, namely, radius-fixed clustering to identify reliable markers, linear programming to detect an initial scatter simplex, and a smart floating search for the optimum latent variable model. The comparative experimental results obtained from both realistic simulations and case studies show that the CAM3.0 tool can help biologists more accurately identify known or novel cell markers, determine cell proportions, and estimate cell-specific expressions, complementing the existing tools particularly when study- or datatype-specific references are unreliable or unavailable. AVAILABILITY AND IMPLEMENTATION: The open-source R Scripts of CAM3.0 is freely available at https://github.com/ChiungTingWu/CAM3/(https://github.com/Bioconductor/Contributions/issues/3205). A user's guide and a vignette are provided.

The rapid development of sensor technologies allows the acquisition of high dimensional sensing data. Multi-output modeling techniques have been developed to leverage the data for decision making. However, the data often contain segments of missing values, which cause great information loss and thus affect the modeling performance. This study explores the missing pattern and the correlation structure of missing segments and maximally exploits useful information in the data to improve multi-output modeling accuracy. Specifically, a new multi-output modeling method is developed based on Gaussian Process Latent Variable Model (GPLVM). A decision score is developed to seek an optimal modeling strategy and then a tailored Expectation-Maximization (EM) algorithm based on GPLVM is designed to estimate the missing segments while optimizing model parameters. The proposed method demonstrates superior performance in both a simulation study and a case study, which makes it a powerful tool to enable process automation. Note to Practitioners—In real-life applications, missing values are constantly present in multi-output sensing data, which greatly affects data-driven decision-making. Modeling of such data becomes more difficult when consecutive observations within or across different outputs are missing. Existing methods often discard the missing values and extract information only from the available observations. However, the pattern of missing values may contain important messages that can potentially boost the modeling performance. This research develops a new framework based on GPLVM to model multi-output data with segmented missing patterns. A tailored EM algorithm is developed to iteratively impute the missing values and optimize model parameters. In addition, a decision score that quantifies both the missing pattern and correlation is designed to determine an optimal modeling strategy. The proposed method can benefit many applications across different industries that require modeling of multi-output incomplete data, especially when the data have many segments of missing observations.

Cardiac ion channels are highly glycosylated membrane proteins with up to 30% of the protein's mass containing glycans. Heart diseases often accompany individuals with congenital disorders of glycosylation (CDG). However, cardiac dysfunction among CDG patients is not yet fully understood. There is an urgent need to study how aberrant glycosylation impacts cardiac electrical signaling. Our previous works reported that congenitally reduced sialylation achieved through deletion of the sialyltransferase gene, ST3Gal4, leads to altered gating of voltage-gated Na+and K+channels (Navand Kv, respectively). However, linking the impact of reduced sialylation on ion channel gating to the action potential (AP) is difficult without performing computer experiments. Also, decomposing the sum of K+ currents is difficult because of complex structures and components of Kvchannels (e.g., Kv4.2, and Kv1.5). In this study, we developed in-silico models to describe the functional role of reduced sialylation in both Navand Kvgating and the AP using in vitro experimental data. Modeling results showed that reduced sialylation changes Kvgating as follows: 1) The steady-state activation voltages of Kvisoforms are shifted to a more depolarized potential. 2) Aberrant K+currents (IKslow and Ito) contribute to a prolonged AP duration, and altered Na+current (INa) contributes to a shortened AP refractory period. This study contributes to a better understanding of the functional role of reduced sialylation in cardiac dysfunction that shows strong potential to provide new pharmaceutical targets for the treatment of CDG-related heart diseases.

Glycan structures account for up to 35% of the mass of cardiac sodium ( Nav ) channels. To question whether and how reduced sialylation affects Nav activity and cardiac electrical signaling, we conducted a series of in vitro experiments on ventricular apex myocytes under two different glycosylation conditions, reduced protein sialylation (ST3Gal4(-/-)) and full glycosylation (control). Although aberrant electrical signaling is observed in reduced sialylation, realizing a better understanding of mechanistic details of pathological variations in INa and AP is difficult without performing in silico studies. However, computer model of Nav channels and cardiac myocytes involves greater levels of complexity, e.g., high-dimensional parameter space, nonlinear and nonconvex equations. Traditional linear and nonlinear optimization methods have encountered many difficulties for model calibration. This paper presents a new statistical metamodeling approach for efficient computer experiments and optimization of Nav models. First, we utilize a fractional factorial design to identify control variables from the large set of model parameters, thereby reducing the dimensionality of parametric space. Further, we develop the Gaussian process model as a surrogate of expensive and time-consuming computer models and then identify the next best design point that yields the maximal probability of improvement. This process iterates until convergence, and the performance is evaluated and validated with real-world experimental data. Experimental results show the proposed algorithm achieves superior performance in modeling the kinetics of Nav channels under a variety of glycosylation conditions. As a result, in silico models provide a better understanding of glyco-altered mechanistic details in state transitions and distributions of Nav channels. Notably, ST3Gal4(-/-) myocytes are shown to have higher probabilities accumulated in intermediate inactivation during the repolarization and yield a shorter refractory period than WTs. The proposed statistical design of computer experiments is generally extensible to many other disciplines that involve large-scale and computationally expensive models.

Cardiac action potentials (AP) are produced by the orchestrated functions of ion channels. A slight change in ion channel activity may affect the AP waveform, thereby potentially increasing susceptibility to abnormal cardiac rhythms. Cardiac ion channels are heavily glycosylated, with up to 30% of a mature protein's mass comprised of glycan structures. However, little is known about how reduced glycosylation impacts the gating of hERG (human ether-a-go-go related gene) channel, which is partially responsible for late phase 2 and phase 3 of the AP. This paper integrates the data from in vitro experiments with in-silico models to predict the glycosylation modulation dynamics in hERG ion channels and cardiac electrical signals. The gating behaviors of hERG channels expressed in Chinese Hamster Ovary (CHO) cells were measured under four glycosylation conditions, i.e., full glycosylation, reduced sialylation, mannose-rich. and N-glycanase treated. Further, we developed in-silico models to simulate glycosylation-channel interactions and predict the effects of reduced glycosylation on multiscale cardiac processes (i.e., cardiac cells, 1-D and 2-D tissues). From the in-silico models, reduced glycosylation was shown to shorten the repolarization phase of cardiac APs, thereby influencing electrical propagation in cardiac fibers and tissues. In addition, the patterns of derived electrocardiogram show that reduced glycosylation of hERG channel shortens the QT interval and decreases the re-entry rate of spiral waves. This work suggests new pharmaceutical targets for the long QT syndrome and potentially other cardiac disorders.