Reliable Prediction Model Research Articles

Early stage cost prediction model for Indian building construction projects using artificial neural networks

Purpose This paper aims to enhance early-stage cost estimation in construction projects, a critical factor in project feasibility, funding, resource allocation and scheduling. Traditional cost estimation approaches suffer from limitations such as the absence of structured methodologies, assumptions of linear cost relationships, prolonged processes and expert judgment variations. To address these challenges, this study proposes a reliable cost prediction model based on artificial neural networks (ANNs) for building construction projects in India. Design/methodology/approach To develop cost prediction model, this study collected data from 377 building construction projects in India, encompassing 17 essential cost parameters. The methodology involves data preprocessing, constructing features and fine-tuning ANN hyperparameters meticulously to achieve optimal performance. Findings The research showcases effectiveness of cost prediction model, evident in significantly reduced mean square error values. ANN-based prediction model excels in handling nonlinear cost dependencies and diverse project complexities, making it a valuable tool for early-stage cost estimation. Research limitations/implications ANN-based cost prediction model is primarily designed for predicting costs associated with structural works of building projects. Practical implications The proposed solution offers stakeholders a robust data-driven decision-making tool during initial phases of construction projects. This can lead to more successful and economically viable outcomes. Originality/value This research examines the drawbacks of traditional cost estimation methods by presenting a data-driven approach leveraging machine learning. It significantly improves precision of early cost forecasts in construction projects while offering practical value to industry.

Genetic concordance in melanoma: insights from primary tumors and their matched distant metastases.

Melanoma metastasis poses a significant challenge due to its aggressive nature and increasing incidence. Confirming the clonal relationship between the primary melanoma and its metastasis is essential to developing reliable prediction models. Here, we compared the genetic profile of primary melanoma and matched metastasis to assess their genetic clonal relationship. Using a targeted sequencing panel encompassing 330 amplicons, we targeted hotspot regions in 41 cancer genes and 154 single nucleotide polymorphisms. The clonal relation between primary and matched metastasis tumors was evaluated by comparing the mutational status and the copy number variations profile in 15 patients with primarily thin melanomas and distant metastases, or with a long latency between the primary melanoma and distant metastasis. Our findings revealed that only about 50% of the analyzed matched primaries and metastases were clonally or likely clonally related, while the remaining sets were either not clonally related or difficult to determine with certainty the clonal relatedness. The findings of our study illustrate the intricate clonal relationships between primary melanoma and metastasis and raise doubts if the metastatic potential is overestimated in the primary tumors. Further investigation with larger cohorts is needed to better understand this complexity of melanoma metastasis and clonality phenomenon, which should be carefully considered when using primary tumor molecular profiles for prognostic model building or therapeutic guidance in metastatic cases.

<b>Combinatorial Design of Imidazole/Triazole Linked Aryl-Substituted Derivatives as CYP17 Inhibitors Exploring Hansch and Molecular Docking Approach</b>

Prostate cancer is an age-related most common cancer characterized by uncontrolled cell growth that develops in the walnut-sized prostate gland in men that potentially forms tumors and metastases to other parts of the body that are untreated. It is widely recognized that 80% of the prostate cancers are androgen-dependent. Thus, inhibition of CYP17 hydroxylase and lyase will stop the androgen synthesis and testosterone over production and ultimately help in treating prostate cancer. Heterocyclic scaffolds (especially imidazole and triazole) have great affinity and potency against the CYP17 hydroxylase and lyase. In this study, we developed a statistically robust and reliable predictive model using Hansch and topological parameters with a series of phenyl alkyl imidazole-based 17α-hydroxylase/17-20-lyase inhibitors. The promising scaffolds were optimized based on the mechanistic interpretations of substitutions, fragments, and branching, and a total of 3645 novel compounds were designed and screened over topological models to identify drug-like potential inhibitors. Molecular docking analysis depicted binding patterns, interaction energies, and a possible mechanism of inhibition of potential molecules. Finally, six potential hydroxylase and nine lyase inhibitors were reported based on the docking score and binding free energy. The novel 369 and 318 are the most promising designed candidates for prostate cancer treatment, while the compound 625 is a potential selective lyase inhibitor with 375-fold activity over the hydroxylase. The ADMET study states the compounds are drug-like and non-toxic. The findings of this study will definitely help the experimental researchers with a prior basis for the potential of novel, promising CYP-17 inhibitors of this class. Received: 23 August 2024 | Revised: 19 December 2024 | Accepted: 15 January 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support this work are available upon reasonable request to the corresponding author. Author Contribution Statement Balaji Wamanrao Matore: Data curation, Writing – original draft, Visualization. Anjali Murmu: Writing – review & editing. Jagadish Singh: Formal analysis, Investigation. Partha Pratim Roy: Conceptualization, Methodology, Software, Validation, Resources, Supervision, Project administration.

Mortality Risk Prediction in Patients With Antimelanoma Differentiation-Associated, Gene 5 Antibody-Positive, Dermatomyositis-Associated Interstitial Lung Disease: Algorithm Development and Validation.

Patients with antimelanoma differentiation-associated gene 5 antibody-positive dermatomyositis-associated interstitial lung disease (anti-MDA5+DM-ILD) are susceptible to rapidly progressive interstitial lung disease (RP-ILD) and have a high risk of mortality. There is an urgent need for a reliable prediction model, accessible via an easy-to-use web-based tool, to evaluate the risk of death. This study aimed to develop and validate a risk prediction model of 3-month mortality using machine learning (ML) in a large multicenter cohort of patients with anti-MDA5+DM-ILD in China. In total, 609 consecutive patients with anti-MDA5+DM-ILD were retrospectively enrolled from 6 hospitals across China. Patient demographics and laboratory and clinical parameters were collected on admission. The primary endpoint was 3-month mortality due to all causes. Six ML algorithms (Extreme Gradient Boosting [XGBoost], logistic regression (LR), Light Gradient Boosting Machine [LightGBM], random forest [RF], support vector machine [SVM], and k-nearest neighbor [KNN]) were applied to construct and evaluate the model. After applying inclusion and exclusion criteria, 509 (83.6%) of the 609 patients were included in our study, divided into a training cohort (n=203, 39.9%), an internal validation cohort (n=51, 10%), and 2 external validation cohorts (n=92, 18.1%, and n=163, 32%). ML identified 8 important variables as critical for model construction: RP-ILD, erythrocyte sedimentation rate (ESR), serum albumin (ALB) level, age, C-reactive protein (CRP) level, aspartate aminotransferase (AST) level, lactate dehydrogenase (LDH) level, and the neutrophil-to-lymphocyte ratio (NLR). LR was chosen as the best algorithm for model construction, and the model demonstrated excellent performance, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.866, a sensitivity of 84.8%, and a specificity of 84.4% on the validation data set and an AUC of 0.90, a sensitivity of 85.0%, and a specificity of 83.9% on the training data set. Calibration curves and decision curve analysis (DCA) confirmed the model's accuracy and clinical applicability. Moreover, the model showed strong predictive performance in the external validation cohorts (cohort 1: AUC=0.836, 95% CI 0.754-0.916; cohort 2: AUC=0.915, 95% CI 0.871-0.959), indicating good generalizability. This model was integrated into a web-based tool to predict the 3-month mortality for patients with anti-MDA5+DM-ILD. We successfully developed a robust clinical prediction model and an accompanying web tool to estimate the 3-month mortality risk for patients with anti-MDA5+DM-ILD.

A Clinical Investigation of Hip Implant Migration and Wear

Abstract The prediction of survival rate probability for hip implants, based on clinical data acquired before and after surgery, incorporating patient-specific parameters, represents a pivotal advancement in enabling more precise risk assessment for potential complications, such as aseptic loosening and implant wear-related inflammation, on an individualized basis. This critical step marks a substantial progression toward the realization of digitized and personalized medicine. The objective of this study was to establish prediction aiding correlations between implant wear and migration data, derived from X-ray imaging of 149 patients diagnosed with hip arthritis, and the performance of hip implants. The patients underwent cementless hip replacement surgery, receiving implants consisting of ultra-high-molecular-weight polyethylene (UHMWPE) paired with titanium-aluminum-vanadium (Ti6Al4V) wedges. Over the course of a median follow-up period of 4 years, X-ray assessments were conducted to monitor the migration of the femoral head and acetabular components using Ein Bild Röntgen Analyse (EBRA). Clinical findings revealed a linear relationship between average migration and wear. Notably, it was observed that increased cup migration corresponded proportionally to greater wear values. Furthermore, in-depth analysis revealed significant distinctions based on gender and age. Specifically, the established relationship can confidently serve as a reliable predictive model for the behavior of hip implants in female subjects and individuals aged 50–60 years.

Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model

As the global energy demand continues to produce, photovoltaic (PV) solar energy has emerged as a key Renewable Energy Source (RES) due to its sustainability and potential to reduce dependence on fossil fuels. However, accurate forecasting of Solar Energy (SE) output remains a significant challenge due to the inherent variability and intermittency of solar irradiance (SI), which is affected by factors such as weather conditions, geographic location, and seasonal patterns. Reliable prediction models are crucial for optimizing energy management, ensuring grid stability, and minimizing operational costs. To address these challenges, this research introduces an innovative method that integrates Robust Seasonal-Trend Decomposition (RSTL) with an Adaptive Seagull Optimisation Algorithm (ASOA)-optimized Long Short-Term Memory (LSTM) neural network. Using RSTL to differentiate between time series data into development, seasonal in nature, and residual factors, this methodology addresses SI’s unpredictable nature and intermittent operation and provides the basis for accurate predictions. ASOA improves LSTM features by constantly finding and exploiting resources and adopting motivation from seagulls’ collecting and migration behaviours. Parameter standardization employing ASOA, the RSTL decomposition approach, and the conceptual model of LSTM networks are all presented in this research work. The proposed method has been contrasted with conventional methods by applying a testing environment incorporating essential Meteorological Factors (MF) and historical SE datasets. The study of performance measurements (RMSE, MAE, and R2) demonstrates significant improvements in the accuracy of predictions. The research results highlight significant implications regarding subsequent studies and real-world uses in SE prediction, accentuating the positive impacts of incorporating accurate data decomposition and adaptive optimized performance.

Improved DevOps Lifecycle by Integrating a Novel Tool V-Git Lab

Aims: We propose a tool that can automatically generate datasets for software defect prediction from GitHub repositories Background: DevOps is a software development approach that emphasizes collaboration, communication, and automation in order to improve the speed and quality of software delivery. Objective: This study aims to demonstrate the effectiveness of the tool, and in order to do so, a series of experiments were conducted on several popular GitHub repositories and compared the performance of our generated datasets with existing datasets Method: The tool works by analyzing the commit history of a given repository and extracting relevant features that can be used to predict defects. These features include code complexity metrics, code churn, and the number of developers involved in a particular code change. Result: Our results show that the datasets generated by our tool are comparable in quality to existing datasets and can be used to train effective software defect prediction models. Conclusion: Overall, the proposed tool provides a convenient and effective way to generate highquality datasets for software defect prediction, which can significantly improve the accuracy and reliability of prediction models.

An Interpretable XGBoost-SHAP Machine Learning Model for Reliable Prediction of Mechanical Properties in Waste Foundry Sand-Based Eco-Friendly Concrete

An Interpretable XGBoost-SHAP Machine Learning Model for Reliable Prediction of Mechanical Properties in Waste Foundry Sand-Based Eco-Friendly Concrete

Signal Preprocessing in Instrument-Based Electronic Noses Leads to Parsimonious Predictive Models: Application to Olive Oil Quality Control

Gas sensor-based electronic noses (e-noses) have gained considerable attention over the past thirty years, leading to the publication of numerous research studies focused on both the development of these instruments and their various applications. Nonetheless, the limited specificity of gas sensors, along with the common requirement for chemical identification, has led to the adaptation and incorporation of analytical chemistry instruments into the e-nose framework. Although instrument-based e-noses exhibit greater specificity to gasses than traditional ones, they still produce data that require correction in order to build reliable predictive models. In this work, we introduce the use of a multivariate signal processing workflow for datasets from a multi-capillary column ion mobility spectrometer-based e-nose. Adhering to the electronic nose philosophy, these workflows prioritized untargeted approaches, avoiding dependence on traditional peak integration techniques. A comprehensive validation process demonstrates that the application of this preprocessing strategy not only mitigates overfitting but also produces parsimonious models, where classification accuracy is maintained with simpler, more interpretable structures. This reduction in model complexity offers significant advantages, providing more efficient and robust models without compromising predictive performance. This strategy was successfully tested on an olive oil dataset, showcasing its capability to improve model parsimony and generalization performance.

P0216 PANoptosis-related genes can predict the progression of ulcerative colitis and colitis-associated colorectal cancer

Abstract Background Ulcerative colitis (UC) is a complex inflammatory bowel disease that significantly increases the risk of developing colitis-associated colorectal cancer (CAC). The etiology of UC remains unclear, and the mechanisms underlying its progression to CAC require further exploration. Recent studies have identified a novel form of cell death called pan-apoptosis, which is closely linked to inflammatory diseases. However, its specific role in UC and CAC progression is not yet fully understood. This study aims to develop a reliable predictive model based on pan-apoptosis using high-throughput RNA sequencing and validate it, along with an analysis of cellular characteristics in UC patients through single-cell sequencing data. This research offers new insights for the prognosis and treatment of UC patients. Methods This study collected single-cell and high-throughput sequencing data from UC patients and defined a pan-apoptosis gene set. The dataset was analyzed for immune infiltration, differential expression, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Additionally, weighted gene co-expression network analysis (WGCNA) was used to identify key genes associated with UC, which were then utilized to construct a predictive model. Finally, single-cell data analysis was conducted on UC patients, followed by in vitro validation of the expression of key genes. Results By analyzing differentially expressed genes in UC samples and using the pan-apoptosis gene set, WGCNA identified key genes associated with UC, including CASP1, LCN2, STAT3, and ZBP1. These genes were used to construct a pan-apoptosis-related predictive model, establishing a pan-apoptosis-related score (PAR-Score). The results showed that PAR-Score has strong classification ability and is positively correlated with immune cell infiltration and the inflammatory microenvironment in UC patients. Notably, CAC patients had significantly higher PAR-Scores. Single-cell sequencing data confirmed that monocyte activity in UC patients is linked to pan-apoptosis, while intercellular communication among immune cells was notably reduced. The expression levels of key genes in the predictive model were validated in vivo. Drug predictions targeting these genes suggested that Leridistim, interferon-γ, and other compounds could serve as potential therapeutic agents for UC or CAC. Conclusion The predictive model established based on pan-apoptosis exhibits good predictive performance, providing new perspectives for assessing the condition and progression of UC patients, and opening up new avenues for the treatment of UC and CAC in the future. References Nature. 2007;448(7152):427-434. doi:10.1038/nature060052. Jess T, Rungoe C, Peyrin L. Risk of Colorectal Cancer in Patients With Ulcerative Colitis: A Meta-analysis of Population-Based Cohort Studies. 2012;10(6). 3. Shin J, Baek GH, Cha B, et al. Complementary Therapeutic Effect of Fecal Microbiota Transplantation in Ulcerative Colitis after the Response to Anti-Tumor Necrosis Factor Alpha Agent Was Lost: A Case Report. Published online 2024. doi:10.3390/biomedicines12040800 Front Cell Infect Microbiol. 2019;9:406. doi:10.3389/fcimb.2019.00406 J Immunol. 2022;209(9):1625-1633. doi:10.4049/jimmunol.2200508 World J Gastroenterol. 2014;20(44):16389. doi:10.3748/wjg.v20.i44.16389

Prediction of Macroeconomic Growth Using Backpropagation Algorithms: A Review

This study aims to evaluate the effectiveness of the backpropagation algorithm in predicting macroeconomic growth through a Systematic Literature Review (SLR) approach. The review analyzes literature sourced from reputable indexes such as Scopus, DOAJ, and Google Scholar, focusing on publications from the last decade (2013–2024). It examines various studies on the application of the backpropagation algorithm, including parameter settings and model selection that influence prediction accuracy. The findings indicate that careful parameter configuration, such as the number of neurons, hidden layers, and learning rates, along with appropriate model selection, significantly enhance the performance of the backpropagation algorithm in macroeconomic prediction. This study highlights that combining optimal techniques and accurate parameter configurations substantially improves prediction accuracy and efficiency. It provides valuable insights and practical guidance for researchers and practitioners in designing more reliable and effective macroeconomic prediction models.

A hybrid data assimilation method based on real-time Ensemble Kalman filtering and KNN for COVID-19 prediction

This study introduces a hybrid data assimilation method that significantly improves the predictive accuracy of the time-dependent Susceptible-Exposed-Asymptomatic-Infected-Quarantined-Removed (SEAIQR) model for epidemic forecasting. The approach integrates real-time Ensemble Kalman Filtering (EnKF) with the K-Nearest Neighbors (KNN) algorithm, combining dynamic real-time adjustments with pattern recognition techniques tailored to the specific dynamics of epidemics. This hybrid methodology overcomes the limitations of single-model predictions in the face of increasingly complex transmission pathways in modern society. Numerical experiments conducted using COVID-19 case data from Xi’an, Shaanxi Province, China (December 9, 2021, to January 8, 2022) demonstrate a marked improvement in forecasting accuracy relative to traditional models and other data assimilation approaches. These findings underscore the potential of the proposed method to enhance the accuracy and reliability of predictive models, providing valuable insights for future epidemic forecasting and disease control strategies.

A Comprehensive Review of Wind Power Prediction Based on Machine Learning: Models, Applications, and Challenges

Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review of machine learning techniques applied to wind power prediction, emphasizing their advantages over traditional physical and statistical models. Machine learning methods, especially deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ensemble learning techniques like XGBoost, excel in addressing the nonlinearity and complexity of wind power data. The review also explores critical aspects such as data preprocessing, feature selection strategies, and model optimization techniques, which significantly enhance prediction accuracy and robustness. Challenges such as data acquisition difficulties, complex terrain influences, and sensor quality issues are examined in depth, with proposed solutions discussed. Additionally, the paper highlights future research directions, including the potential of multi-model fusion, emerging deep learning technologies like Transformers, and the integration of smart sensors and IoT technologies to develop intelligent, automated, and reliable prediction systems. By addressing existing challenges and leveraging advanced machine learning techniques, this work provides valuable insights into the current state of wind power prediction research and offers strategic guidance for enhancing the applicability and reliability of prediction models in practical scenarios.

Precision Prediction of Alzheimer's Disease: Integrating Mitochondrial Energy Metabolism and Immunological Insights.

Alzheimer's disease (AD), a prevalent neurodegenerative disorder, is characterized by mitochondrial dysfunction and immune dysregulation. This study is aimed at developing a risk prediction model for AD by integrating multi-omics data and exploring the interplay between mitochondrial energy metabolism-related genes (MEMRGs) and immune cell dynamics. We integrated four GEO datasets (GSE132903, GSE29378, GSE33000, GSE5281) for differential gene expression analysis, functional enrichment, and weighted gene co-expression network analysis (WGCNA). We identified two key gene modules (turquoise and magenta) significantly correlated with AD. Subsequently, we constructed a risk prediction model incorporating five MEMRGs (MRPL15, RBP4, ABCA1, MPV17, and MRPL37) and clinical factors using LASSO regression. The model demonstrated robust predictive performance (AUC > 0.815) in both internal and external validation (GSE44770) cohorts. Downregulation of MRPL15, RBP4, MPV17, and MRPL37 in AD brain regions (validated using AlzData and qRT-PCR) suggests impaired mitochondrial function. Conversely, ABCA1 upregulation may represent a compensatory response. Furthermore, significant differences in immune cell proportions, particularly gamma delta T cells (p = 0.002) and activated CD4 memory T cells (p = 0.027), were found between AD and non-demented samples. We observed significant correlations between MEMRG expression and specific immune cell fractions, indicating a potential link between mitochondrial dysfunction and immune dysregulation in AD. Our study provides a reliable risk prediction model for AD and highlights the crucial roles of MEMRGs and immune responses in disease pathogenesis, offering potential targets for therapeutic interventions.

Establishment of a Prognostic Necroptosis-Related lncRNA Signature in Ovarian Cancer.

Ovarian Cancer (OC) was known for its high mortality rate among gynecological malignancies, often resulting in a poor prognosis. This study sought to identify prognostic necroptosis-related long non-coding RNAs (lncRNAs) (NRlncRNAs) with prognostic potential and to construct a reliable risk prediction model for OC patients. The transcriptome and clinic data were sourced from TCGA and GTEx databases. Initially, NRlncRNAs were discovered by assessing gene correlations and evaluating differences in gene expression. Subsequently, Cox regression and LASSO methods were employed to develop the NRlncRNAs risk model, which was further validated through survival analysis, ROC curves, Cox regression, and nomograms across both the test and entire datasets. Multivariate Cox analysis revealed that the risk score based on 14 NRlncRNAs can independently predict the prognosis of OC. The low-risk group demonstrated significantly higher immune cell infiltration scores and lower tumor immune dysfunction, exclusion, and TIDE scores, as well as an increased number of neoantigens and higher TMB. Notably, the low-risk group also exhibited an elevated HRD score. The model's predictive accuracy was further substantiated through ROC analysis, showing superior performance compared to many existing models.Finally, the expression levels of 14 NRlncRNAs were confirmed using the qRT-PCR in two OC cell lines. These findings suggested that the NRlncRNAs risk model could serve as a more precise indicator for forecasting immune response and outcomes of targeted treatments in OC.

Predictive model of neurodevelopmental outcome in neonatal hypoxic ischemic encephalopathy.

To build an early, prognostic model for adverse outcome in infants with hypoxic ischemic encephalopathy (HIE) receiving therapeutic hypothermia (TH) based on brain magnetic resonance images (MRI), electrophysiological tests and clinical assessments were performed during the first 5days of life. Retrospective study of 182 neonates with HIE and managed with TH. The predominant pattern of HIE brain injury on MRI performed following cooling was scored by neuroradiologists. The electroencephalogram (EEG) background and evoked potential (EP) response, were analyzed. Area under the curve (AUC) of these tools for adverse outcome including death and/or moderate disabilities using the Bayley-III at 36months were calculated. A stepwise model approach was used to reach the final most efficient predictive model. Of 182 neonates, 99 were male (54.4%), with median gestational age of 39weeks (IQR 38-40) and median weight of 3.3kg (IQR 2.9-3.7). On admission, 47 (26%), 104 (57%) and 31(17%) neonates presented with mild, moderate and severe encephalopathy respectively. In multivariate analysis of 129 infants who received all prognostic modalities, the predictive value of a model of EEG plus MRI, AUC=84%) is equivalent to models of EEG plus MRI with added EP and clinical assessment at discharge (AUC=84 and 85% respectively). In the era of cooling for neonatal HIE, the combination of EEG background and MRI during the first few days of life, provide an objective and highly reliable model for prediction of death and long-term disabilities.

Combinatorial Design of Imidazole/Triazole Linked Aryl-Substituted Derivatives as CYP17 Inhibitors Exploring Hansch and Molecular Docking Approach

Prostate cancer is an age-related most common cancer characterized by uncontrolled cell growth that develops in the walnut-sized prostate gland in men that potentially forms tumors and metastases to other parts of the body that are untreated. It is widely recognized that 80% of the prostate cancers are androgen-dependent. Thus, inhibition of CYP17 hydroxylase and lyase will stop the androgen synthesis and testosterone over production and ultimately help in treating prostate cancer. Heterocyclic scaffolds (especially imidazole and triazole) have great affinity and potency against the CYP17 hydroxylase and lyase. In this study, we developed a statistically robust and reliable predictive model using Hansch and topological parameters with a series of phenyl alkyl imidazole-based 17α-hydroxylase/17-20-lyase inhibitors. The promising scaffolds were optimized based on the mechanistic interpretations of substitutions, fragments, and branching, and a total of 3645 novel compounds were designed and screened over topological models to identify drug-like potential inhibitors. Molecular docking analysis depicted binding patterns, interaction energies, and a possible mechanism of inhibition of potential molecules. Finally, six potential hydroxylase and nine lyase inhibitors were reported based on the docking score and binding free energy. The novel 369 and 318 are the most promising designed candidates for prostate cancer treatment, while the compound 625 is a potential selective lyase inhibitor with 375-fold activity over the hydroxylase. The ADMET study states the compounds are drug-like and non-toxic. The findings of this study will definitely help the experimental researchers with a prior basis for the potential of novel, promising CYP-17 inhibitors of this class.

Leveraging Explainable AI and Multimodal Data for Stress Level Prediction in Mental Health Diagnostics

The increasing prevalence of mental health issues, particularly stress, has necessitated the development of data-driven, interpretable machine learning models for early detection and intervention. This study leverages multimodal data, including activity levels, perceived stress scores (PSS), and event counts, to predict stress levels among individuals. A series of models, including Logistic Regression, Random Forest, Gradient Boosting, and Neural Networks, were evaluated for their predictive performance. Results demonstrated that ensemble models, particularly Random Forest and Gradient Boosting, performed significantly better compared to Logistic Regression. Random Forest achieved an accuracy of 73%, while Gradient Boosting delivered a balanced precision-recall tradeoff with an accuracy of 72%. Gradient Boosting outperformed in identifying high-stress instances, achieving a recall of 70%, making it the most reliable model for stress prediction. Explainable AI (XAI), which refers to the ability of machine learning models to provide transparent and understandable explanations for their predictions, was employed in this study using SHAP (SHapley Additive exPlanations). SHAP values revealed that Perceived Stress Score (PSS) had the most significant impact on predictions, followed by event count and activity inference. Higher PSS scores strongly correlated with high-stress predictions, while increased event counts and activity levels were associated with lower stress. These findings underscore the importance of incorporating behavioral patterns in stress diagnostics and highlight the utility of explainable models in improving transparency, trust, and usability for clinical decision-making. This research establishes a robust foundation for deploying interpretable machine learning systems to support mental health diagnostics and enhance clinician decision support.

A review on computational models for predicting protein solubility.

Protein solubility is a critical factor in the production of recombinant proteins, which are widely used in various industries, including pharmaceuticals, diagnostics, and biotechnology. Predicting protein solubility remains a challenging task due to the complexity of protein structures and the multitude of factors influencing solubility. Recent advances in computational methods, particularly those based on machine learning, have provided powerful tools for predicting protein solubility, thereby reducing the need for extensive experimental trials. This review provides an overview of current computational approaches to predict protein solubility. We discuss the datasets, features, and algorithms employed in these models. The review aims to bridge the gap between computational predictions and experimental validations, fostering the development of more accurate and reliable solubility prediction models that can significantly enhance recombinant protein production.

Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.

Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on various machine learning approaches for predicting heart disease, but they could not able to achieve remarkable accuracy. In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. Our approach involved feature selection techniques to identify the most relevant predictors, aimed at refining the models to enhance both performance and interpretability. The models were trained, incorporating processes such as grid search hyperparameter tuning, and cross-validation to minimize overfitting. Additionally, we have developed a novel voting system with feature selection techniques to advance heart disease classification. Furthermore, we have evaluated the models using key performance metrics including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (ROC AUC). Among the models, XGBoost demonstrated exceptional performance, achieving 99% accuracy, precision, F1-Score, 98% recall, and 100% ROC AUC. This study offers a promising approach to early heart disease diagnosis and preventive healthcare.