Bayesian Machine Learning Research Articles

Using a Graph Convolutional Neural Network Model to Identify Bile Salt Export Pump Inhibitors.

The bile salt export pump (BSEP) is a key transporter involved in the efflux of bile salts from hepatocytes to bile canaliculi. Inhibition of BSEP leads to the accumulation of bile salts within the hepatocytes, leading to possible cholestasis and drug-induced liver injury. Screening for and identification of chemicals that inhibit this transporter aid in understanding the safety liabilities of these chemicals. Moreover, computational approaches to identify BSEP inhibitors provide an alternative to the more resource-intensive, gold standard experimental approaches. Here, we used publicly available data to develop predictive machine learning models for the identification of potential BSEP inhibitors. Specifically, we analyzed the utility of a graph convolutional neural network (GCNN)-based approach in combination with multitask learning to identify BSEP inhibitors. Our analyses showed that the developed GCNN model performed better than the variable-nearest neighbor and Bayesian machine learning approaches, with a cross-validation receiver operating characteristic area under the curve of 0.86. In addition, we compared GCNN-based single-task and multitask models and evaluated their utility in addressing data limitation challenges commonly observed in bioactivity modeling. We found that multitask models performed better than single-task models and can be utilized to identify active molecules for targets with limited data availability. Overall, our developed multitask GCNN-based BSEP model provides a useful tool for prioritizing hits during early drug discovery and in risk assessment of chemicals.

A Review of Bayesian Machine Learning Principles, Methods, and Applications

A Review of Bayesian Machine Learning Principles, Methods, and Applications

A Review of Bayesian Machine Learning Principles, Methods, and Applications

A Review of Bayesian Machine Learning Principles, Methods, and Applications

Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia

The currently recommended dose of dexamethasone for patients with severe or critical COVID-19 is 6 mg per day (mg/d) regardless of patient features and variation. However, patients with severe or critical COVID-19 are heterogenous in many ways (e.g., age, weight, comorbidities, disease severity, and immune features). Thus, it is conceivable that a standardized dosing protocol may not be optimal. We assessed treatment effect heterogeneity in the COVID STEROID 2 trial, which compared 6 mg/d to 12 mg/d, using a causal inference framework with Bayesian Additive Regression Trees, a flexible modeling method that detects interactive effects and nonlinear relationships among multiple patient characteristics simultaneously. We found that 12 mg/d of dexamethasone, relative to 6 mg/d, was probably associated with better long-term outcomes (days alive without life support and mortality after 90 days) among the entire trial population (i.e., no signals of harm), and probably more beneficial among those without diabetes mellitus, that were older, were not using IL-6 inhibitors at baseline, weighed less, or had higher level respiratory support at baseline. This adds more evidence supporting the use of 12 mg/d in practice for most patients not receiving other immunosuppressants and that additional study of dosing could potentially optimize clinical outcomes.

215 Personalised predictions of patient-reported atopic dermatitis severity scores using a Bayesian machine learning approach

215 Personalised predictions of patient-reported atopic dermatitis severity scores using a Bayesian machine learning approach

Kernel method for gravity forward simulation in implicit probabilistic geologic modeling

Gravity is one of the most widely used geophysical data types in subsurface exploration. In the recent developments of stochastic geologic modeling, gravity data serve as an additional constraint to the model construction. The gravity data can be included in the modeling process as the likelihood function in a probabilistic joint inversion framework and allow the quantification of uncertainty in geologic modeling directly. A fast but also precise forward gravity simulation is essential to the success of the probabilistic inversion. Hence, we have developed a gravity kernel method, which is based on the widely adopted analytical solution on a discretized grid. As opposed to a globally refined regular mesh, we construct local tensor grids for individual gravity receivers, respecting the gravimeter locations and the local sensitivities. The kernel method is efficient in terms of computing and memory use for mesh-free implicit geologic modeling approaches. This design makes the method well suited for many-query applications, such as Bayesian machine learning using gradient information calculated from automatic differentiation. Optimal grid design without knowing the underlying geometry is not straightforward before evaluating the model. Therefore, we further provide a novel perspective on a refinement strategy for the kernel method based on the sensitivity of the cell to the corresponding receiver. Numerical results are presented and found superior performance compared to the conventional spatial convolution method.

β -decay half-lives of the r -process waiting-point isotones of N=81 and 82 nuclei

In this paper we perform systematic calculations of $\ensuremath{\beta}$-decay half-lives for the $r$-process waiting-point isotones of $N=81$ and 82, in the framework of nucleon-pair approximation. A phenomenological form of the Hamiltonian, which includes single-particle energies, pairing plus quadrupole interactions, and Gamow-Teller interactions is assumed, and the theoretical $\ensuremath{\beta}$-decay energies are taken based on the Bayesian machine learning. A good agreement with experimental values of half-lives is achieved by using the nucleon-pair truncated model space in which the $S$-pair condensation dominates. Our calculated half-lives are found to be very sensitive to $\ensuremath{\beta}$-decay energies on the one hand, and are quite robust with respect to extension of the model space and variation of interaction parameters on the other hand.

Bayesian Regularization Neural Network-Based Machine Learning Approach on Optimization of CRDI-Split Injection with Waste Cooking Oil Biodiesel to Improve Diesel Engine Performance

The present study utilized response surface methodology (RSM) and Bayesian neural network (BNN) to predict the characteristics of a diesel engine powered by a blend of biodiesel and diesel fuel. The biodiesel was produced from waste cooking oil using a biocatalyst synthesized from vegetable waste through the wet impregnation technique. A multilevel central composite design was utilized to predict engine characteristics, including brake thermal efficiency (BTE), nitric oxide (NO), unburned hydrocarbons (UBHC), smoke emissions, heat release rate (HRR), and cylinder peak pressure (CGPP). BNN and the logistic–sigmoid activation function were used to train the experimental data in the artificial neural network (ANN) model, and the errors and correlations of the predicted models were calculated. The study revealed that the biocatalyst was capable of producing a maximum yield of 93% at 55 °C under specific reaction conditions, namely a reaction time of 120 min, a stirrer speed of 900 rpm, a catalyst loading of 7 wt.%, and a molar ratio of 1:9. Further, the ANN model was found to exhibit comparably lower prediction errors (0.001–0.0024), lower MAPE errors (3.14–4.6%), and a strong correlation (0.984–0.998) compared to the RSM model. B100-80%-20% was discovered to be the best formulation for emission property, while B100-90%-10% was the best mix for engine performance and combustion at 100% load. In conclusion, this study found that utilizing the synthesized biocatalyst led to attaining a maximum biodiesel yield. Furthermore, the study recommends using ANN and RSM techniques for accurately predicting the characteristics of a diesel engine.

A Catalog of Exoplanet Atmospheric Retrieval Codes

Exoplanet atmospheric retrieval is a computational technique widely used to infer properties of planetary atmospheres from remote spectroscopic observations. Retrieval codes typically employ Bayesian sampling algorithms or machine learning approaches to explore the range of atmospheric properties (e.g., chemical composition, temperature structure, aerosols) compatible with an observed spectrum. However, despite the wide adoption of exoplanet retrieval techniques, there is currently no systematic summary of exoplanet retrieval codes in the literature. Here, we provide a catalog of the atmospheric retrieval codes published to date, alongside links to their respective code repositories where available. Our catalog will be continuously updated via a Zenodo archive.

Chasing the impact of the Gaia-Sausage-Enceladus merger on the formation of the Milky Way thick disc

ABSTRACT We employ our Bayesian Machine Learning framework BINGO (Bayesian INference for Galactic archaeOlogy) to obtain high-quality stellar age estimates for 68 360 red giant and red clump stars present in the 17th data release of the Sloan Digital Sky Survey, the APOGEE-2 high-resolution spectroscopic survey. By examining the denoised age-metallicity relationship of the Galactic disc stars, we identify a drop in metallicity with an increase in [Mg/Fe] at an early epoch, followed by a chemical enrichment episode with increasing [Fe/H] and decreasing [Mg/Fe]. This result is congruent with the chemical evolution induced by an early-epoch gas-rich merger identified in the Milky Way-like zoom-in cosmological simulation Auriga. In the initial phase of the merger of Auriga 18 there is a drop in metallicity due to the merger diluting the metal content and an increase in the [Mg/Fe] of the primary galaxy. Our findings suggest that the last massive merger of our Galaxy, the Gaia-Sausage-Enceladus, was likely a significant gas-rich merger and induced a starburst, contributing to the chemical enrichment and building of the metal-rich part of the thick disc at an early epoch.

Application of Bayesian machine learning for estimation of uncertainty in forecasted plume directions by atmospheric dispersion simulations

ABSTRACT When assisting emergency responses to a nuclear accident through atmospheric dispersion simulations, it is necessary to provide the prediction results and their uncertainties. This study develops an estimation method using machine learning for uncertainty in forecasted plume directions. The difference in plume directions derived from the meteorological forecast and analysis inputs was considered as the uncertainty in forecasted plume direction. Bayesian machine learning was used to predict the uncertainty based on the accumulated uncertainty estimation result in past cases. A three-day forecast simulation was conducted every day from 2015 to 2020, considering a hypothetical release of 137Cs from a nuclear facility to create training and test datasets for the machine learning. The findings reveal that the rate of good predictability was greater than 50% even in the forecast 36 h later when investigating the effectiveness of the Bayesian model on uncertainty prediction. The frequency of miss prediction of higher uncertainty was low (0.9%−7.9%) throughout the forecast period. However, the rate of over-prediction of uncertainty increased with forecast time up to 31.2%, which is acceptable as a conservative estimation. These results show that the Bayesian model in this study effectively estimates the uncertainty of plume directions predicted through atmospheric dispersion simulations.

Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems

A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed. The sparse Bayesian machine learning is used to update and derive an interpretable expression for the digital twin. Two approaches for updating the digital twin are proposed. The first approach makes use of both the input and output information from a dynamical system, whereas the second approach utilizes output-only observations to update the digital twin. Both methods use a library of candidate functions representing certain physics to infer new perturbation terms in the existing digital twin model. In both cases, the resulting expressions of updated digital twins are identical, and in addition, the epistemic uncertainties are quantified. In the first approach, the regression problem is derived from a state-space model, whereas in the latter case, the output-only information is treated as a stochastic process. The concepts of Itô calculus and Kramers-Moyal expansion are being utilized to derive the regression equation. The performance of the proposed approaches is demonstrated using highly nonlinear dynamical systems such as the crack-degradation problem. Numerical results demonstrated in this paper almost exactly identify the correct perturbation terms along with their associated parameters in the dynamical system. The probabilistic nature of the proposed approach also helps in quantifying the uncertainties associated with updated models. The proposed approaches provide an exact and explainable description of the perturbations in digital twin models, which can be directly used for better cyber-physical integration, long-term future predictions, degradation monitoring, and model-agnostic control.

Opportunistic Screening for Atrial Fibrillation on Routine Chest Computed Tomography.

Quantitative biomarkers from chest computed tomography (CT) can facilitate the incidental detection of important diseases. Atrial fibrillation (AFib) substantially increases the risk for comorbid conditions including stroke. This study investigated the relationship between AFib status and left atrial enlargement (LAE) on CT. A total of 500 consecutive patients who had undergone nongated chest CTs were included, and left atrium maximal axial cross-sectional area (LA-MACSA), left atrium anterior-posterior dimension (LA-AP), and vertebral body cross-sectional area (VB-Area) were measured. Height, weight, age, sex, and diagnosis of AFib were obtained from the medical record. Parametric statistical analyses and receiver operating characteristic curves were performed. Machine learning classifiers were run with clinical risk factors and LA measurements to predict patients with AFib. Eighty-five patients with a diagnosis of AFib were identified. Mean LA-MACSA and LA-AP were significantly larger in patients with AFib than in patients without AFib (28.63 vs. 20.53cm 2 , P <0.000001; 4.34 vs. 3.5cm, P <0.000001, respectively), both with area under the curves (AUCs) of 0.73. Multivariable logistic regression analysis including age, sex, and VB-Area with LA-MACSA improved the AUC for predicting AFib (AUC=0.77). An LA-MACSA threshold of 30cm 2 demonstrated high specificity for AFib diagnosis at 92% and sensitivity of 48%, and LA-AP threshold at 4.5cm demonstrated 90% specificity and 42% sensitivity. A Bayesian machine learning model using age, sex, height, body surface area, and LA-MACSA predicted AFib with an AUC of 0.743. LA-MACSA or LA-AP can be rapidly measured from routine chest CT, and when >30 cm 2 and >4.5cm, respectively, are specific indicators to predict patients at increased risk for AFib.

Vaccine hesitancy, parental concerns, and COVID-19 in a digital leisure context: Implications for out-of-school time

Due to the increasing prevalence of parental vaccine hesitancy or refusal, it is important to understand parental motives for vaccine hesitancy. This study examines social media conversations and commentaries regarding concerns about parenting, vaccine hesitancy, and the COVID-19 pandemic within an in-person leisure and recreation context: out-of-school time (OST) programs. A generative, probabilistic Bayesian machine learning model was used to analyze 31,925 tweets and group them into seven categories: Government, Feelings, School, Public Health, Christmas, Risk and Safety, and Families. As a result, recommendations for research and practice are discussed in connection to both OST programs and digital leisure, including a diverse range of vaccine hesitancy motivations related to children and parents, communication management strategies for OST professionals, and the impact of the politization of leisure in a digital leisure context.

Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer.

The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Molecular simulations of state-specific drug interactions with multiple cardiac ion channels to reveal mechanisms of arrhythmogenesis.

Unintended drug block of cardiac ion channels remains a major problem in drug development. The voltage-gated potassium channel KV11.1 also known as hERG is a major drug anti-target binding a diverse set of small molecule drugs that potently reduce the critical repolarizing current IKr. Many drugs that bind the hERG channel promote deadly arrythmias while some hERG blockers present significantly lower proarrhythmic risk. Two hypotheses were proposed to elucidate this discrepancy: (1) preferential drug binding to the inactivated state of the hERG channel confers greater proarrhythmic risk and (2) simultaneous drug binding to other cardiac ion channels can ameliorate the risk associated with hERG channel block. Here, we present a state-specific molecular modeling assessment of drug binding to different conformations of the hERG and voltage-gated sodium NaV1.5 and calcium CaV1.2 channels. We have developed structural models of these cardiac ion channels in open and inactivated conformations and performed all-atom molecular dynamics (MD) simulations to validate structural stabilities and assess ion conduction. Ligand docking was then performed using Site Identification by Ligand Competitive Saturation (SILCS), a pre-compute ensemble molecular docking technique. SILCS allows us to perform a high-throughput assessment of ligand binding affinities using molecular fragment energy maps derived from MD simulations of state-specific ion channel models. Bayesian machine learning was used to provide improved correlation of SILCS-computed affinities with experimental data. Using SILCS multi-ligand docking we also estimated interactions of drugs with sex hormones in the hERG channel pore to assess a potential molecular mechanism for an increased proarrhythmia risk in females. We aim to use SILCS computed state-specific drug affinity data to inform multi-scale functional kinetic models of cardiac electrophysiology to estimate emergent drug effects on the cardiac action potential and heart rhythm.

Diagnosis of Respiratory Changes in Cystic Fibrosis Using a Soft Voting Ensemble with Bayesian Networks and Machine Learning Algorithms

Diagnosis of Respiratory Changes in Cystic Fibrosis Using a Soft Voting Ensemble with Bayesian Networks and Machine Learning Algorithms

Comparative Analysis of CNN Models and Bayesian Optimization-Based Machine Learning Algorithms in Leaf Type Classification

In this study, the leaves are classified by various Machine Learning (ML) and Deep Learning (DL) based Convolutional Neural Networks (CNN) methods. In the proposed method, first, image pre-processing is performed to increase the accuracy of the posterior process. The obtained image is a grayscale image without noise as a result of the pre-processing. These preprocessed images are used in classification with ML and DL. The Speeded Up Robust Features (SURF) are extracted from the grayscale image for ML-based learning. The features are restructured as visual words using the Bag of Visual Words (BoVW) method. Then, histograms are generated for each image according to the frequency of the visual word. Those histograms represent the new feature data. The histogram features are classified by four different ML methods, Decision Tree (DT), k-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). Before using the ML methods, Bayesian Optimization (BO) method, which is one of the Hyperparameter Optimization (HO) algorithms, is applied to determine hyperparameters. In the classification process performed with four different ML algorithms, the best accuracy is achieved with the KNN algorithm as 98.09%. Resnet18, ResNet50, MobileNet, GoogLeNet, DenseNet, which are state-of-the-art CNN architectures, are used for DL-based learning. CNN models have higher accuracy than ML algorithms.

Multivariate Predictive Modelling of Mathematics Semestral Grade via Bayesian Networks Machine Learning Algorithm

Multivariate Predictive Modelling of Mathematics Semestral Grade via Bayesian Networks Machine Learning Algorithm

Retracted: Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers.

[This retracts the article DOI: 10.1155/2022/6722321.].