Reliable Prediction Model Research Articles

AI-powered prediction of HCC recurrence after surgical resection: Personalised intervention opportunities using patient-specific risk factors.

Hepatocellular carcinoma (HCC) recurrence following surgical resection remains a significant clinical challenge, necessitating reliable predictive models to guide personalised interventions. In this study, we sought to harness the power of artificial intelligence (AI) to develop a robust predictive model for HCC recurrence using comprehensive clinical datasets. Leveraging data from 958 patients across multiple centres in Australia and Hong Kong, we employed a multilayer perceptron (MLP) as the optimal classifier for model generation. Through rigorous internal cross-validation, including a cohort from the Chinese University of Hong Kong (CUHK), our AI model successfully identified specific pre-surgical risk factors associated with HCC recurrence. These factors encompassed hepatic synthetic function, liver disease aetiology, ethnicity and modifiable metabolic risk factors, collectively contributing to the predictive synergy of our model. Notably, our model exhibited high accuracy during cross-validation (.857 ± .023) and testing on the CUHK cohort (.835), with a notable degree of confidence in predicting HCC recurrence within accurately classified patient cohorts. To facilitate clinical application, we developed an online AI digital tool capable of real-time prediction of HCC recurrence risk, demonstrating acceptable accuracy at the individual patient level. Our findings underscore the potential of AI-driven predictive models in facilitating personalised risk stratification and targeted interventions to mitigate HCC recurrence by identifying modifiable risk factors unique to each patient. This model aims to aid clinicians in devising strategies to disrupt the underlying carcinogenic network driving recurrence.

AURKB promotes immunogenicity and immune infiltration in clear cell renal cell carcinoma

BackgroundChromatin regulators (CRs) are capable of causing epigenetic alterations, which are significant features of cancer. However, the function of CRs in controlling Clear Cell Renal Cell Carcinoma (ccRCC) is not well understood. This research aims to discover a CRs prognostic signature in ccRCC and to elucidate the roles of CRs-related genes in tumor microenvironment (TME).MethodsExpression profiles and relevant clinical annotations were retrieved from the Cancer Genome Atlas (TCGA) and UCSC Xena platform for progression-free survival (PFS) data. The R package "limma" was used to identify differentially expressed CRs. A predictive model based on five CRs was developed using LASSO-Cox analysis. The model's predictive power and applicability were validated using K-M curves, ROC curves, nomograms, comparisons with other models, stratified survival analyses, and validation with the ICGC cohort. GO and GSEA analyses were performed to investigate mechanisms differentiating low and high riskScore groups. Immunogenicity was assessed using Tumor Mutational Burden (TMB), immune cell infiltrations were inferred, and immunotherapy was evaluated using immunophenogram analysis and the expression patterns of human leukocyte antigen (HLA) and checkpoint genes. Differentially expressed CRs (DECRs) between low and high riskScore groups were identified using log2|FC|> 1 and FDR < 0.05. AURKB, one of the high-risk DECRs and a component of our prognostic model, was selected for further analysis.ResultsWe constructed a 5 CRs signature, which demonstrated a strong capacity to predict survival and greater applicability in ccRCC. Elevated immunogenicity and immune infiltration in the high riskScore group were associated with poor prognosis. Immunotherapy was more effective in the high riskScore group, and certain chemotherapy medications, including cisplatin, docetaxel, bleomycin, and axitinib, had lower IC50 values. Our research shows that AURKB is critical for the immunogenicity and immune infiltration of the high riskScore group.ConclusionOur study produced a reliable prognostic prediction model using only 5 CRs. We found that AURKB promotes immunogenicity and immune infiltration. This research provides crucial support for the development of prognostic biomarkers and treatment strategies for ccRCC.

Addressing Data Scarcity in Solar Energy Prediction with Machine Learning and Augmentation Techniques

The accurate prediction of global horizontal irradiance (GHI) is crucial for optimizing solar power generation systems, particularly in mountainous areas with complex topography and unique microclimates. These regions face significant challenges due to limited reliable data and the dynamic nature of local weather conditions, which complicate accurate GHI measurement. The scarcity of precise data impedes the development of reliable solar energy prediction models, impacting both economic and environmental outcomes. To address these data scarcity challenges in solar energy prediction, this paper focuses on various locations in Europe and Asia Minor, predominantly in mountainous regions. Advanced machine learning techniques, including random forest (RF) and extreme gradient boosting (XGBoost) regressors, are employed to effectively predict GHI. Additionally, optimizing training data distribution based on cloud opacity values and integrating synthetic data significantly enhance predictive accuracy, with R2 scores ranging from 0.91 to 0.97 across multiple locations. Furthermore, substantial reductions in root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE) underscore the improved reliability of the predictions. Future research should refine synthetic data generation, optimize additional meteorological and environmental parameter integration, extend methodology to new regions, and test for predicting global tilted irradiance (GTI). The studies should expand training data considerations beyond cloud opacity, incorporating sky cover and sunshine duration to enhance prediction accuracy and reliability.

An Investigation of Molecular Property Prediction and Classification Based on Machine Learning Algorithms

With the rapid advancement of science and technology, chemical and physical research has entered an era of complexity and high dimensionality, where traditional research paradigms struggle to optimize vast chemical parameter searches. The Machine Chemist platform was developed in this context, leveraging big data and intelligent models to automate chemical synthesis, characterization, and testing processes. This study aims to predict the y1, y2 and y3 properties and classes of 2,580 molecules based on their physicochemical properties and improve model accuracy through data analysis and modeling. Data preprocessing involved removing missing values, duplicates, and outliers using the quartile method, resulting in an analyzable dataset. A scatter plot of y2 and id suggested a univariate polynomial function relationship, leading to the construction of a univariate nonlinear regression model. The model achieved a high prediction accuracy, with a low root mean square error and a high coefficient of determination. Additionally, the relationship between class and y1~y3 , x1~x100 indicators was examined, revealing mostly nonlinear relationships. A random forest model was established to classify 2,580 molecules into 1 to 4 classes based on the properties of 200,000 chemical molecules. The model's performance was evaluated using decision trees, confusion matrices, precision, and recall metrics. Finally, the SHAP method assessed the impact of feature indicators on classification outcomes, contributing to the development of a reliable molecular category prediction model.

3DSGIMD: An accurate and interpretable molecular property prediction method using 3D spatial graph focusing network and structure-based feature fusion

A comprehensive representation of molecular structure is essential for establishing accurate and reliable molecular property prediction models. However, fully extracting and learning intrinsic molecular structure information, especially spatial structure features, remains a challenging task, leading that many molecular property prediction models still have no enough accuracy for the real application. In this study, we developed an innovative and interpretable deep learning method, termed 3DSGIMD, which predicted the molecular properties by integrating and learning the spatial structure and substructure information of molecules at multiple levels, and generated the focusing weights by aggregating spatial and adjacency information of molecules to improve understanding of prediction results. We evaluated the model on 10 public datasets and 14 cell-based phenotypic screening datasets. Extensive experimental results indicated that 3DSGIMD achieved superior or comparable predictive performance compared with some existing models, and the individually designed components contributed significantly to the advanced performance of the model. In addition, we also provided insight into the interpretability of our model via visualizing the focusing weights and perturbation analysis, and the results showed that 3DSGIMD can pinpoint crucial local structures and bits of molecular descriptors associated with the predicted properties. In summary, 3DSGIMD is a competitive molecular property prediction method that holds the potential to aid drug design and optimization.

A Federated Learning-Based Industrial Health Prognostics for Heterogeneous Edge Devices Using Matched Feature Extraction

Data-driven industrial health prognostics require rich training data to develop accurate and reliable predictive models. However, stringent data privacy laws and the abundance of edge industrial data necessitate decentralized data utilization. Thus, the industrial health prognostics field is well suited to significantly benefit from federated learning (FL), a decentralized and privacy-preserving learning technique. However, FL-based health prognostics tasks have hardly been investigated due to the complexities of meaningfully aggregating model parameters trained from heterogeneous data to form a high performing federated model. Specifically, data heterogeneity among edge devices, stemming from dissimilar degradation mechanisms and unequal dataset sizes, poses a critical statistical challenge for developing accurate federated models. We propose a pioneering FL-based health prognostic model with a feature similarity-matched parameter aggregation algorithm to discriminatingly learn from heterogeneous edge data. The algorithm searches across the heterogeneous locally trained models and matches neurons with probabilistically similar feature extraction functions first, before selectively averaging them to form the federated model parameters. As the algorithm only averages similar neurons, as opposed to conventional naive averaging of coordinate-wise neurons, the distinct feature extractors of local models are carried over with less dilution to the resultant federated model. Using both cyclic degradation data of Li-ion batteries and non-cyclic data of turbofan engines, we demonstrate that the proposed method yields accuracy improvements as high as 44.5\% and 39.3\% for state-of-health estimation and remaining useful life estimation, respectively.

Semantic and traditional feature fusion for software defect prediction using hybrid deep learning model

Software defect prediction aims to find a reliable method for predicting defects in a particular software project and assisting software engineers in allocating limited resources to release high-quality software products. While most earlier research has concentrated on employing traditional features, current methodologies are increasingly directed toward extracting semantic features from source code. Traditional features often fall short in identifying semantic differences within programs, differences that are essential for the development of reliable and effective prediction models. In contrast, semantic features cannot present statistical metrics about the source code, such as the code size and complexity. Thus, using only one kind of feature negatively affects prediction performance. To bridge the gap between the traditional and semantic features, we propose a novel defect prediction model that integrates traditional and semantic features using a hybrid deep learning approach to address this limitation. Specifically, our model employs a hybrid CNN-MLP classifier: the convolutional neural network (CNN) processes semantic features extracted from projects’ abstract syntax trees (ASTs) using Word2vec. In contrast, the traditional features extracted from the dataset repository are processed by a multilayer perceptron (MLP). Outputs of CNN and MLP are then integrated and fed into a fully connected layer for defect prediction. Extensive experiments are conducted on various open-source projects to validate CNN-MLP’s effectiveness. Experimental results indicate that CNN-MLP can significantly enhance defect prediction performance. Furthermore, CNN-MLP’s improvements outperform existing methods in non-effort-aware and effort-aware cases.

Machine learning-based model to predict severe acute kidney injury after total aortic arch replacement for acute type A aortic dissection

BackgroundSevere acute kidney injury (AKI) after total aortic arch replacement (TAAR) is related to adverse outcomes in patients with acute type A aortic dissection (ATAAD). However, the early prediction of severe AKI remains a challenge. This study aimed to develop a novel model to predict severe AKI after TAAR in ATAAD patients using machine learning algorithms. MethodsA total of 572 ATAAD patients undergoing TAAR were enrolled in this retrospective study, and randomly divided into a training set (70 %) and a validation set (30 %). Lasso regression, support vector machine-recursive feature elimination and random forest algorithms were used to screen indicators for severe AKI (defined as AKI stage III) in the training set, respectively. Then the intersection indicators were selected to construct models through artificial neural network (ANN) and logistic regression. The AUC-ROC curve was employed to ascertain the prediction efficacy of the ANN and logistic regression models. ResultsThe incidence of severe AKI after TAAR was 22.9 % among ATAAD patients. The intersection predictors identified by different machine learning algorithms were baseline serum creatinine and ICU admission variables, including serum cystatin C, procalcitonin, aspartate transaminase, platelet, lactic dehydrogenase, urine N-acetyl-β-d-glucosidase and Acute Physiology and Chronic Health Evaluation II score. The ANN model showed a higher AUC-ROC than logistic regression (0.938 vs 0.908, p < 0.05). Furthermore, the ANN model could predict 89.1 % of severe AKI cases beforehand. In the validation set, the superior performance of the ANN model was further confirmed in terms of discrimination ability (AUC = 0.916), calibration curve analysis and decision curve analysis. ConclusionThis study developed a novel and reliable clinical prediction model for severe AKI after TAAR in ATAAD patients using machine learning algorithms. Importantly, the ANN model showed a higher predictive ability for severe AKI than logistic regression.

Efficient and Accurate pH Determination with pH Test Strips Based on Machine Learning.

The determination of pH values is crucial in various fields, such as analytical chemistry, medical diagnostics, and biochemical research. pH test strips, renowned for their convenience and cost-effectiveness, are commonly utilized for pH qualitative estimation. Recently, quantitative methods for determining pH values using pH test strips have been developed. However, these methods can be prone to errors due to environmental factors, such as lighting conditions, which affect the imaging quality of the pH test strips. To address these challenges, we developed an innovative approach that combines machine learning techniques with pH test strips for the quantitative determination of pH values. Our method involves extracting artificial features from the pH test strip images and combining them across multiple dimensions for comprehensive analysis. To ensure optimal feature selection, we developed a feature selection strategy based on SHAP importance. This strategy helps in identifying the most relevant features that contribute to accurate pH prediction. Furthermore, we integrated multiple machine learning algorithms, employing a robust stacking fusion strategy to establish a highly reliable pH value prediction model. Our proposed method automates the determination of pH values through pH test strips, effectively overcoming the limitations associated with environmental lighting interference. Experimental results demonstrate that this method is convenient, effective, and highly reliable for the determination of pH values.

Predicting hazard degree levels of metro operation accidents based on ordered constraint Apriori-RF method

To explore the non-linear relationship between risk sources and the hazard degree levels of accidents, and to precisely predict the hazard impact of metro operation accidents, we propose the ordered constraint Apriori-RF method for forecasting metro operation accident hazard degree levels. Firstly, the hazard degree of metro operation accidents is quantified from three dimensions: casualties, train delays, and facility damages. K-means clustering is then applied to categorize hazard degree levels. Secondly, the ordered constraint Apriori algorithm is employed to mine valid association rules between metro operation risk sources and accident hazard degree levels. These valid association rules are subsequently employed in the random forest algorithm for training, establishing a reliable and accurate prediction model. Finally, the method is validated using metro accident data from a city in China. The research results indicate that the ordered constraint Apriori-RF method enhances the effectiveness of association rule mining by 74.9% and exhibits higher computational efficiency. The predicted values of the ordered constraint Apriori-RF method have small errors. Compared to traditional Random Forest algorithms, the root mean square error is reduced by 14%, and the weighted root mean square error is reduced by 36%, demonstrating the higher accuracy of the ordered constraint Apriori-RF method and its clear advantages. The research findings provide a precise and effective method for quantitatively predicting the hazard degree levels of metro operation accidents, holding significant theoretical and practical value in ensuring metro operation safety and implementing accident mitigation and prevention measures.

Reliability analysis of GAN based transmit modules for active array antenna of phased array radar

Reliability is one of the most important requirements in our day to day life considering consistency, availability and failure free performance of the product over it’s define mission time. As complexity of the system increases, design for reliable systems is a big challenge. The objective of the reliability prediction analysis is to evaluate the predicted reliability of the active transmit receive modules (TRMs) under specified operating conditions, and to demonstrate that the predicted reliability meets the requirements, also to identify any parts present in the design which leads to higher failure rates. The research shows reliability of generative adversarial network (GAN) based TRMs covering from design to finalization of components as early as practicable in today's short product lifecycles. Using the reliability prediction process, we describe a method for providing design engineers with reliability feedback on their decisions. Using a conventional reliability prediction model, the Telcordia (Bellcore) parts stress prediction model, and some standard rules of thumb, we describe an initial implementation of this technique. It provides systematic identification of likely modes of failure, possible effects of each failure, and the criticality of each failure with regard to reliability, system readiness, mission success, and demand for maintenance/logistic support.

Assessing and predicting water quality index with key water parameters by machine learning models in coastal cities, China

The water quality index (WQI) is a widely used tool for comprehensive assessment of river environments. However, its calculation involves numerous water quality parameters, making sample collection and laboratory analysis time-consuming and costly. This study aimed to identify key water parameters and the most reliable prediction models that could provide maximum accuracy using minimal indicators. Water quality from 2020 to 2023 were collected including nine biophysical and chemical indicators in seventeen rivers in Yancheng and Nantong, two coastal cities in Jiangsu Province, China, adjacent to the Yellow Sea. Linear regression and seven machine learning models (Artificial Neural Network (ANN), Self-Organizing Maps (SOM), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGB) and Stochastic Gradient Boosting (SGB)) were developed to predict WQI using different groups of input variables based on correlation analysis. The results indicated that water quality improved from 2020 to 2022 but deteriorated in 2023, with inland stations exhibiting better conditions than coastal ones, particularly in terms of turbidity and nutrients. The water environment was comparatively better in Nantong than in Yancheng, with mean WQI values of approximately 55.3–72.0 and 56.4–67.3, respectively. The classifications "Good" and "Medium" accounted for 80 % of the records, with no instances of "Excellent" and 2 % classified as "Bad". The performance of all prediction models, except for SOM, improved with the addition of input variables, achieving R2 values higher than 0.99 in models such as SVM, RF, XGB, and SGB. The most reliable models were RF and XGB with key parameters of total phosphorus (TP), ammonia nitrogen (AN), and dissolved oxygen (DO) (R2 = 0.98 and 0.91 for training and testing phase) for predicting WQI values, and RF using TP and AN (accuracy higher than 85 %) for WQI grades. The prediction accuracy for "Medium" and "Low" water quality grades was highest at 90 %, followed by the "Good" level at 70 %. The model results could contribute to efficient water quality evaluation by identifying key water parameters and facilitating effective water quality management in river basins.

Novel decision tree models predict the overall survival of patients with submandibular gland cancer.

While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months. We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated. A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points. Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies.

Impact of Feature Selection on the Prediction of Global Horizontal Irradiation under Ouarzazate City Climate

AbstractEnsuring accurate forecasts of Global Horizontal Irradiance (GHI) stands as a pivotal aspect in optimizing the efficient utilization of solar energy resources. Machine learning techniques offer promising prospects for predicting global horizontal irradiance. However, within the realm of machine learning,the importance of feature selection cannot be overestimated, as it is crucial in determining performance and reliability of predictive models. To address this, a comprehensive machine learning algorithm has been developed, leveraging advanced feature importance techniques to forecast GHI data with precision. The proposed models draw upon historical data encompassing solar irradiance characteristics and environmental variables within the Ouarzazate region, Morocco, spanning from 1st January 2018, to 31 December 2018, with readings taken at 60-minute intervals. The findings underscore the profound impact of feature selection on enhancing the predictive capabilities of machine learning models for GHI forecasting. By identifying and prioritizing the most informative features, the models exhibit significantly enhanced accuracy metrics, thereby bolstering the reliability, efficiency, and practical applicability of GHI forecasts. This advancement not only holds promise for optimizing solar energy utilization but also contributes to the broader discourse on leveraging machine learning for renewable energy forecasting and sustainability initiatives.

Development and validation of a nomogram predictive model for cognitive impairment in cerebral small vessel disease: a comprehensive retrospective analysis.

Cerebral small vessel disease (CSVD) is a common neurodegenerative condition in the elderly, closely associated with cognitive impairment. Early identification of individuals with CSVD who are at a higher risk of developing cognitive impairment is crucial for timely intervention and improving patient outcomes. The aim of this study is to construct a predictive model utilizing LASSO regression and binary logistic regression, with the objective of precisely forecasting the risk of cognitive impairment in patients with CSVD. The study utilized LASSO regression for feature selection and logistic regression for model construction in a cohort of CSVD patients. The model's validity was assessed through calibration curves and decision curve analysis (DCA). A nomogram was developed to predict cognitive impairment, incorporating hypertension, CSVD burden, apolipoprotein A1 (ApoA1) levels, and age. The model exhibited high accuracy with AUC values of 0.866 and 0.852 for the training and validation sets, respectively. Calibration curves confirmed the model's reliability, and DCA highlighted its clinical utility. The model's sensitivity and specificity were 75.3 and 79.7% for the training set, and 76.9 and 74.0% for the validation set. This study successfully demonstrates the application of machine learning in developing a reliable predictive model for cognitive impairment in CSVD. The model's high accuracy and robust predictive capability provide a crucial tool for the early detection and intervention of cognitive impairment in patients with CSVD, potentially improving outcomes for this specific condition.

Use of GC-IMS and Stoichiometry to Characterize Flavor Volatiles in Different Parts of Lueyang Black Chicken during Slaughtering and Cutting.

Chilled and cut chicken is preferred by consumers for its safeness and readiness to cook. To evaluate the quality characteristics of various chilled chicken products, differences in volatile organic components (VOCs) of six different cut parts (breast, back, leg, heart, liver, and gizzard) of Lueyang black chicken were characterized through gas chromatography-ion mobility spectroscopy (GC-IMS) combined with stoichiometry. A total of 54 peaks in the signal of VOCs were detected by GC-IMS, and 43 VOCs were identified by qualitative analysis. There were 22 aldehydes (20.66-54.07%), 8 ketones (25.74-62.87%), 9 alcohols (4.17-14.69%), 1 ether (0.18-2.22%), 2 esters (0.43-1.54%), and 1 furan (0.13-0.52%), in which aldehydes, ketones, and alcohols were the main categories. Among the six cut parts, the relative content of aldehydes (54.07%) was the highest in the gizzard, and the relative content of ketones (62.87%) was the highest in the heart. Meanwhile, the relative content of alcohols (14.69%) was the highest in the liver. Based on a stable and reliable predictive model established by orthogonal partial least squares-discriminant analysis (OPLS-DA), 3-hydroxy-2-butanone (monomer and dimer), acetone, 2-butanone monomer, hexanal (monomer and dimer), isopentyl alcohol monomer, and n-hexanol monomer were picked out as characteristic VOCs based on variable importance in projection (VIP value > 1.0, p < 0.05). Principal component analysis (PCA) and the clustering heatmap indicated that the characteristic VOCs could effectively distinguish the six cut parts of Lueyang black chicken. The specific VOCs responsible for flavor differences among six different cut parts of Lueyang black chicken were hexanal (monomer and dimer) for the gizzard, 2-butanone monomer and hexanal dimer for the breast, hexanal monomer for the back, 3-hydroxy-2-butanone monomer for the leg, 3-hydroxy-2-butanone (monomer and dimer) for the heart, and acetone and isopentyl alcohol monomer for the liver. These findings could reveal references for quality assessment and development of chilled products related to different cut parts of Lueyang black chicken in the future.

Modeling spatiotemporal temperature dynamics of large-format power batteries: A multi-source information fusion approach

Uneven temperature distributions can significantly impact battery performance and cycle life. Industrial applications, in particular, where unknown disturbances and limited sensors exist, pose significant challenges for accurately estimating the battery temperature field. This work proposes a multi-source information fusion framework for capturing the spatiotemporal temperature dynamics of pouch-type Lithium-ion batteries. First, we develop a system model of the battery thermal process based on physical insights, considering unknown parameter deviations and unmodeled dynamics. Next, we use the Galerkin-spectral method to expand the spatiotemporal variable and reduce the original infinite-dimensional system to a low-order model containing the most important system modes. The unknown component of the model is then entirely stripped and integrated into a nonlinear term. To close the reality gap of the physics-based model, we subsequently develop an error compensation model that learns the dynamic behavior from sparse observations. Ultimately, our framework design yields a fusion-driven model for reliable temperature field prediction. Simulations and experimental data validate the superior performance and generalization capability of the proposed method.

Computational Frontiers in Aptamer-Based Nanomedicine for Precision Therapeutics: A Comprehensive Review.

In the rapidly evolving landscape of nanomedicine, aptamers have emerged as powerful molecular tools, demonstrating immense potential in targeted therapeutics, diagnostics, and drug delivery systems. This paper explores the computational features of aptamers in nanomedicine, highlighting their advantages over antibodies, including selectivity, low immunogenicity, and a simple production process. A comprehensive overview of the aptamer development process, specifically the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) process, sheds light on the intricate methodologies behind aptamer selection. The historical evolution of aptamers and their diverse applications in nanomedicine are discussed, emphasizing their pivotal role in targeted drug delivery, precision medicine and therapeutics. Furthermore, we explore the integration of artificial intelligence (AI), machine learning (ML), Internet of Things (IoT), Internet of Medical Things (IoMT), and nanotechnology in aptameric development, illustrating how these cutting-edge technologies are revolutionizing the selection and optimization of aptamers for tailored biomedical applications. This paper also discusses challenges in computational methods for advancing aptamers, including reliable prediction models, extensive data analysis, and multiomics data incorporation. It also addresses ethical concerns and restrictions related to AI and IoT use in aptamer research. The paper examines progress in computer simulations for nanomedicine. By elucidating the importance of aptamers, understanding their superiority over antibodies, and exploring the historical context and challenges, this review serves as a valuable resource for researchers and practitioners aiming to harness the full potential of aptamers in the rapidly evolving field of nanomedicine.

A probabilistic framework for external pitting corrosion growth modelling for buried steel pipelines considering soil properties

This paper introduces a novel framework for developing reliable probabilistic predictive corrosion growth models for buried steel pipelines using pipeline inspection data. The framework adopts a power-law function of time model formulation, accounting for nonconstant damage growth rates, and considers the correlation between defect depth and length growth models. The proposed framework explicitly incorporates local influential soil properties in the model formulation; thus, it requires no segmentation and homogenous defect growth assumption and provides defect-specific growth models. The framework is applicable regardless of the availability of matched or non-matched defect data. For corrosion initiation time estimation, two different approaches are proposed: one is to use a Poisson process to account for defect occurrence, which can also predict newly generated defects since the last inspection, and the other is to use multivariate linear regression of soil and pipe properties. The statistics of unknown model parameters are assessed using a Bayesian updating framework in which the model error can be incorporated. The proposed framework is applied using two different sets of data: one set of inline inspection (ILI) data and one set of field excavation data. A case study is conducted, where time-dependent system reliability of an in-service pipeline is assessed considering small leak and burst failure modes using the developed defect growth models. The impact of the growth model accuracy on the probability of failure is investigated, and the importance analysis is performed to identify the most influential random variables to the probability of failure.

Integrating Deep Learning with Electronic Health Records for Early Glaucoma Detection: A Multi-Dimensional Machine Learning Approach.

Recent advancements in deep learning have significantly impacted ophthalmology, especially in glaucoma, a leading cause of irreversible blindness worldwide. In this study, we developed a reliable predictive model for glaucoma detection using deep learning models based on clinical data, social and behavior risk factor, and demographic data from 1652 participants, split evenly between 826 control subjects and 826 glaucoma patients. We extracted structural data from control and glaucoma patients' electronic health records (EHR). Three distinct machine learning classifiers, the Random Forest and Gradient Boosting algorithms, as well as the Sequential model from the Keras library of TensorFlow, were employed to conduct predictive analyses across our dataset. Key performance metrics such as accuracy, F1 score, precision, recall, and the area under the receiver operating characteristics curve (AUC) were computed to both train and optimize these models. The Random Forest model achieved an accuracy of 67.5%, with a ROC AUC of 0.67, outperforming the Gradient Boosting and Sequential models, which registered accuracies of 66.3% and 64.5%, respectively. Our results highlighted key predictive factors such as intraocular pressure, family history, and body mass index, substantiating their roles in glaucoma risk assessment. This study demonstrates the potential of utilizing readily available clinical, lifestyle, and demographic data from EHRs for glaucoma detection through deep learning models. While our model, using EHR data alone, has a lower accuracy compared to those incorporating imaging data, it still offers a promising avenue for early glaucoma risk assessment in primary care settings. The observed disparities in model performance and feature significance show the importance of tailoring detection strategies to individual patient characteristics, potentially leading to more effective and personalized glaucoma screening and intervention.