Utilizing Machine Learning Research Articles

Exploring the impact of HDL and LMNA gene expression on immunotherapy outcomes in NSCLC: a comprehensive analysis using clinical & gene data.

Analyzing the impact of peripheral lipid levels on the efficacy of immune checkpoint inhibitor therapy in non-small cell lung cancer (NSCLC) patient populations and exploring whether it can serve as a biomarker for broadening precise selection of individuals benefiting from immunotherapy. We retrospectively collected clinical data from 201 cases of NSCLC patients receiving immune checkpoint inhibitor therapy. The clinical information included biochemical indicators like total cholesterol, triglycerides, high-density lipoprotein (HDL), and low-density lipoprotein (LDL). We utilized machine learning algorithms and Cox proportional hazards regression models to investigate independent predictors for both short-term and long-term efficacy of immunotherapy. Additionally, we concurrently developed a survival prediction model. Analyzing the Genes of Patients with Treatment Differences to Uncover Mechanisms. Correlation analysis revealed a significant positive association between HDL and ORR, DCR, and PFS. T-test results indicated that the high-HDL group exhibited higher DCR (81.97% vs. 45.57%) and ORR (61.48% vs. 16.46%). Kruskal-Wallis test showed that the high-HDL group had a longer median PFS (11 months vs. 6 months). Utilizing six machine learning algorithms, we constructed models to predict disease relief and stability. The model built using the random forest algorithm demonstrated superior performance, with AUC values of 0.858 and 0.802. Furthermore, both univariate and multivariate Cox analyses identified HDL and LDL as independent risk factors for predicting PFS. In patients with poor immunotherapy response, there is upregulation of BCL2L11, AKT1, and LMNA expression. HDL and LDL are independent factors influencing the survival prognosis of NSCLC patients undergoing immune checkpoint inhibitor therapy. HDL is expected to become new biomarkers for predicting the immunotherapy efficacy in patients with NSCLC. In patients with poor immunotherapy response, upregulation of the LMNA gene leads to apoptosis resistance and abnormal lipid metabolism.

Plant Leaf Disease Detection Using Xception Model

The traditional farming practices have been causing significant financial losses to farmers due to various reasons. However, the implementation of a modern, smart agricultural system utilizing machine learning techniques appears promising in safeguarding farmers and traders against these risks. This advanced system facilitates farmers in identifying common diseases through simple image recognition, employing a variety of image processing methods. Notably, the Convolutional Neural Network (CNN) algorithm stands out as an effective choice among these methods. Interestingly, among the available models, there has been limited utilization of the Xception model, and no comprehensive comparative study involving this model with different classifiers was found. To address this gap, a study was undertaken in two distinct approaches. Firstly, the Xception model demonstrated remarkable accuracy in detecting plant diseases, achieving an impressive 98.3 percent accuracy rate. In comparison, other classifiers such as logistic regression and three additional methods attained accuracy rates of 93 percent and 92 percent, respectively. Secondly, a comparative analysis was conducted on the top 12 papers out of a selection of 45 papers, each employing different methods. The Xception method once again proved to be effective in this context. Through these tests and review studies, the Xception method emerged as a reliable and superior choice. It is expected that this research will provide valuable insights for researchers and stakeholders, potentially guiding the development of new research initiatives in this field.

Thermochemistry of the Mantle Transition Zone Beneath the Western Pacific

AbstractThe Earth's mantle transition zone has significant control on material flux between upper and lower mantle, thus constraining its properties is imperative to understand dynamic processes and circulation patterns. Global seismic data sets to study the transition zone typically display highly uneven spatial distribution. Therefore, complementary geometries are essential to improve knowledge of physical structures, thermochemistry, and impact on convection. Here, we present a new automated approach utilizing machine learning to analyze large seismic data sets, and derive high‐resolution maps of transition zone discontinuity properties. Seismic measurements from ScSScS precursors are integrated with mineralogical modeling to constrain thermochemistry of the western Pacific subduction zone. Our models map recent subduction patterns through the transition zone, indicating stagnation of slabs and accumulation of basalt at its base, and interaction between stagnant slabs and plumes. These results suggest that the thermochemical properties of upper mantle discontinuities can provide high‐resolution images of mantle circulation patterns.

New-age Framework for Educational Reforms

This research describes a framework for utilizing machine learning and data visualization with publicly available education data to create educational reforms. This study aims to address the systemic issues hindering equal learning opportunities between developed and developing countries by analyzing key global education indicators. With the help of clustering and predictive analysis, the work reveals significant factors affecting education, including but not limited to the completion rates, tertiary education enrollment, and literacy rates. The framework also leverages social media and machine learning to drive educational reforms and reduce policy implementation lag by informing policymakers.

Prognostic modeling and Emerging therapeutic targets Unveiled through single-cell sequencing in esophageal squamous Cell carcinoma

ESCC presents a significant global health challenge due to its high mortality rates and varying responses to treatment. This underscores the critical need for novel diagnostic and predictive biomarkers to improve treatment outcomes.Initially, we conducted single-cell transcriptome sequencing on a total of 128,688 cells obtained from 10 patients as part of our research. Utilizing machine learning and cross-validation techniques, we developed a model incorporating 12 genes that distinguish malignant cells from non-malignant ones. In vitro, we explored the effects of IGFBP2 knockdown on the proliferation, invasion, and migration of ESCC cells. The clinical relevance of IGFBP2 was confirmed through IHC and Kaplan-Meier survival analyses. Furthermore, using bioinformatics tools such as GSVA and xCell on public databases, we discovered that high expression of IGFBP2 is associated with an immunosuppressive tumor microenvironment in ESCC, characterized by reduced CD8+ T cell infiltration. This was validated then through IHC.In summary, our study integrates single-cell sequencing and sophisticated computational techniques to highlight IGFBP2 as a promising biomarker and therapeutic target in ESCC.

The Application of Machine Learning Algorithms to Bond Strength between Steel Rebars and Concrete Using Bayesian Optimization.

The purpose of this study is to estimate the bond strength between steel rebars and concrete using machine learning (ML) algorithms with Bayesian optimization (BO). It is important to conduct beam tests to determine the bond strength since it is affected by stress fields. A machine learning approach for bond strength based on 401 beam tests with six impact factors is presented in this paper. The model is composed of three standard algorithms, including random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost), combined with the BO technique. Compared to empirical models, BO-XGB`oost was found to be the most accurate method, with values of R2, MAE, and RMSE of 0.87, 0.897 MPa, and 1.516 MPa for the test set. The development of a simplified model that contains three input variables (diameter of the rebar, yield strength of reinforcement, concrete compressive strength) has been proposed to make it more convenient to apply. According to this prediction, the Shapley additive explanation (SHAP) can help explain why the ML-based model predicts the particular outcome it does. By utilizing machine learning algorithms to predict complex interfacial mechanical behavior, it is possible to improve the accuracy of the model.

An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors

BackgroundKidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function (DGF), a common post-operative complication, may negatively impact the long-term outcomes of both the graft and the pediatric recipient. However, there is limited research on DGF in pediatric kidney transplant recipients. This study aims to develop a predictive model for the risk of DGF occurrence after pediatric kidney transplantation by integrating donor and recipient characteristics and utilizing machine learning algorithms, ultimately providing guidance for clinical decision-making.MethodsThis single-center retrospective cohort study includes all recipients under 18 years of age who underwent single-donor kidney transplantation at our hospital between 2016 and 2023, along with their corresponding donors. Demographic, clinical, and laboratory examination data were collected from both donors and recipients. Univariate logistic regression models and differential analysis were employed to identify features associated with DGF. Subsequently, a risk score for predicting DGF occurrence (DGF-RS) was constructed based on machine learning combinations. Model performance was evaluated using the receiver operating characteristic curves, decision curve analysis (DCA), and other methods.ResultsThe study included a total of 140 pediatric kidney transplant recipients, among whom 37 (26.4%) developed DGF. Univariate analysis revealed that high-density lipoprotein cholesterol (HDLC), donor after circulatory death (DCD), warm ischemia time (WIT), cold ischemia time (CIT), gender match, and donor creatinine were significantly associated with DGF (P < 0.05). Based on these six features, the random forest model (mtry = 5, 75%p) exhibited the best predictive performance among 97 machine learning models, with the area under the curve values reaching 0.983, 1, and 0.905 for the entire cohort, training set, and validation set, respectively. This model significantly outperformed single indicators. The DCA curve confirmed the clinical utility of this model.ConclusionsIn this study, we developed a machine learning-based predictive model for DGF following pediatric kidney transplantation, termed DGF-RS, which integrates both donor and recipient characteristics. The model demonstrated excellent predictive accuracy and provides essential guidance for clinical decision-making. These findings contribute to our understanding of the pathogenesis of DGF.

Machine learning-enabled attapulgite/polyimide nanofiber composite aerogels-based colorimetric sensor array for real-time monitoring of balsa fish freshness

This paper presents the development and application of attapulgite/polyimide nanofiber composite aerogels (ATP/PI NFAs) integrated with a range of acid-base indicators, fabricated using electrospinning and freeze-drying technologies. A detailed characterization of the ATP/PI NFAs revealed a 3D multi-level pore structure that enhanced the mass transfer of target gas molecules and their interaction with probe molecules. Utilizing machine learning approaches, we designed an ATP/PI NFAs-based colorimetric sensor array capable of real-time evaluation of balsa fish freshness. Color features sensitive to changes in freshness were selected using principal component analysis and random forest. Partial least squares regression and random forest regression models were established, achieving the prediction of total volatile basic nitrogen content in balsa fish. The system was validated using a national standard method to demonstrate its accuracy and practicality. The combination of advanced ATP/PI NFAs-based colorimetric sensor array with robust machine learning models paves the way for food safety monitoring.

Accelerated intelligent prediction and analysis of mechanical properties of magnesium alloys based on scaled super learner machine-learning algorithms

The use of machine learning algorithms in magnesium (Mg) alloys has evolved a scientific innovation for lightweight. The dataset was compiled by collecting data from the experiment and utilizing machine learning (ML) models to predict the mechanical properties of 348 Mg alloys. The proportion between the predicted and experimental results produced by different ML models demands more advanced regression methods to obtain better results. Utilizing Mg alloy descriptors as input variables and mechanical properties as output variables, four different ML models were employed namely (i.e.) Random Forest (RF), Extra Tree (ET), Gradient Boost (GB), and Extreme Gradient Boost (XGBoost) to resolve this difficult problem. Each single algorithm aimed to predict the mechanical properties of Mg alloy i.e. Ultimate Tensile Strength (UTS), Yield Strength (YS), and Elongation (EL). Subsequently, the data-driven intelligent prediction modeling technique called scaled Super Learner (SL) was employed to integrate the single models into the stacked model approach to enhance prediction accuracy. The results obtained using scaled Super Learner demonstrated enhanced prediction accuracy for UTS, YS, and EL. The findings further demonstrate enhanced prediction ability by outperforming other approaches as demonstrated by lower Root Mean Squared Error (RMSE) and higher R-Squared (R2) compared to previous studies. The reason for choosing Scaled Super Learner is because of its robustness and resistance to overfitting. Scaled Super Learner is also widely known for its better scalability, simplicity, and ability to handle noisy. The scaled Super Learner is an optimal approach for predicting the properties of Mg alloys. The proposed scaled Super learner serves as a tool for predicting Mg alloy properties.

Utilizing machine learning algorithms for predicting risk factors for bone metastasis from right-sided colon carcinoma after complete mesocolic excision: a 10-year retrospective multicenter study

BackgroundBone metastasis (BM) occurs when colon cancer cells disseminate from the primary tumor site to the skeletal system via the bloodstream or lymphatic system. The emergence of such bone metastases typically heralds a significantly poor prognosis for the patient. This study's primary aim is to develop a machine learning model to identify patients at elevated risk of bone metastasis among those with right-sided colon cancer undergoing complete mesocolonectomy (CME).Patients and methodsThe study cohort comprised 1,151 individuals diagnosed with right-sided colon cancer, with a subset of 73 patients presenting with bone metastases originating from the colon. We used univariate and multivariate regression analyses as well as four machine learning algorithms to screen variables for 38 characteristic variables such as patient demographic characteristics and surgical information. The study employed four distinct machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), to develop the predictive model. Additionally, the model was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), while Shapley additive explanation (SHAP) was utilized to visualize and analyze the model.ResultsThe XGBoost algorithm performed the best performance among the four prediction models. In the training set, the XGBoost algorithm had an area under curve (AUC) value of 0.973 (0.953–0.994), an accuracy of 0.925 (0.913–0.936), a sensitivity of 0.921 (0.902–0.940), and a specificity of 0.908 (0.894–0.922). In the validation set, the XGBoost algorithm had an AUC value of 0.922 (0.833–0.995), an accuracy of 0.908 (0.889–0.926), a sensitivity of 0.924 (0.873–0.975), and a specificity of 0.883 (0.810–0.956). Furthermore, the AUC value of 0.83 for the external validation set suggests that the XGBoost prediction model possesses strong extrapolation capabilities. The results of SHAP analysis identified alkaline phosphatase (ALP) levels, tumor size, invasion depth, lymph node metastasis, lung metastasis, and postoperative neutrophil-to-lymphocyte ratio (NLR) levels as significant risk factors for BM from right-sided colon cancer subsequent to CME.ConclusionThe prediction model for BM from right-sided colon cancer developed using the XGBoost machine learning algorithm in this study is both highly precise and clinically valuable.

Machine learning and multiple linear regression models can predict ascorbic acid and polyphenol contents, and antioxidant activity in strawberries.

Strawberry is a rich source of antioxidants, including ascorbic acid (ASA) and polyphenols, which have numerous health benefits. Antioxidant content and activity are often determined manually using laboratory equipment, which is destructive and time-consuming. This study constructs a prediction model for antioxidant compounds utilizing machine learning (ML) and multiple linear regression based on environmental, plant growth and agronomic fruit quality-related parameters as well as antioxidant levels. These were studied in three farms at two-week intervals during two years of cultivation. During the ML model screening, artificial neural network (ANN)-boosted models displayed a moderate coefficient of determination (R2) at 0.68-0.78 and relative root mean square error (RRMSE) at 3.8-4.8% in polyphenols and total ASA levels, as well as a high R2 of 0.96 and low RRMSE at <3.0% in antioxidant activity. Additionally, we developed variable selection models regarding the antioxidant activity, and variables two and five (environmental parameters and leaf length, respectively) with high accuracy were selected. The linear regression analysis between the actual and predicted data of antioxidants in the ANN-boosted models revealed high fitness with all parameters in almost all training, validation and test sets. Furthermore, environmental parameters are essential in developing such reliable models. We conclude that ANN-boosted, stepwise and double-Lasso regression models can predict antioxidant compounds with enhanced accuracy, and the relevant parameters can be easily acquired on-site without the need for any specific equipment. © 2024 Society of Chemical Industry.

Machine Learning Applications in Prediction Models for COVID-19: A Bibliometric Analysis

The COVID-19 pandemic has had a profound impact on global health, inspiring the widespread use of machine learning in combating the disease, particularly in prediction models. This study aimed to assess academic publications utilizing machine learning prediction models to combat COVID-19. We analyzed 2422 original articles published between 2020 and 2023 with bibliometric tools such as Histcite Pro 2.1, Bibliometrix, CiteSpace, and VOSviewer. The United States, China, and India emerged as the most prolific countries, with Stanford University producing the most publications and Huazhong University of Science and Technology receiving the most citations. The National Natural Science Foundation of China and the National Institutes of Health have made significant contributions to this field. Scientific Reports is the most frequent journal for publishing these articles. Current research focuses on deep learning, federated learning, image classification, air pollution, mental health, sentiment analysis, and drug repurposing. In conclusion, this study provides detailed insights into the key authors, countries, institutions, funding agencies, and journals in the field, as well as the most frequently used keywords.

Predicting Mental Health Disorders based on Sentiment Analysis

The increasing prevalence of mental health disorders such as anxiety, depression, and stress calls for innovative, data- driven approaches to early detection and intervention. This research investigates the use of sentiment analysis to predict mental health disorders from social media text, utilizing machine learning techniques to enhance the accuracy and interpretability of mental health classification. We developed and evaluated multiple machine learning models, including Random Forest, Extra Trees, AdaBoost, and Logistic Regression, using a curated dataset of user-generated content reflecting various mental health states. Our rigorous experimentation demonstrates the potential of sentiment analysis combined with advanced machine learning models in identifying mental health conditions. The study highlights that while Random Forest and Extra Trees excel in prediction accuracy, Logistic Regression offers a balanced trade-off between performance and model stability, making it suitable for real-world applications. These findings contribute to the growing field of AI-driven mental health assessment, offering a foundation for integrating multimodal data, model interpretability, and ethical implementation. Keywords: Mental health disorders, Sentiment analysis, Natural language processing, Machine learning

The BCPM method: decoding breast cancer with machine learning

Breast cancer prediction and diagnosis are critical for timely and effective treatment, significantly impacting patient outcomes. Machine learning algorithms have become powerful tools for improving the prediction and diagnosis of breast cancer. The Breast Cancer Prediction and Diagnosis Model (BCPM), which utilises machine learning techniques to improve the precision and efficiency of breast cancer diagnosis and prediction, is presented in this paper. BCPM collects comprehensive and high-quality data from diverse sources, including electronic medical records, clinical trials, and public datasets. Through rigorous pre-processing, the data is cleaned, inconsistencies are addressed, and missing values are handled. Feature scaling techniques are applied to normalize the data, ensuring fair comparison and equal importance among different features. Furthermore, feature-selection algorithms are utilized to identify the most relevant features that contribute to breast cancer projection and diagnosis, optimizing the model’s efficiency. The BCPM employs numerous machine learning methods, such as logistic regression, random forests, decision trees, support vector machines, and neural networks, to generate accurate models. Area under the curve (AUC), sensitivity, specificity, and accuracy are only some of the metrics used to assess a model’s performance once it has been trained on a subset of data. The BCPM holds promise in improving breast cancer prediction and diagnosis, aiding in personalized treatment planning, and ultimately taming patient results. By leveraging machine learning algorithms, the BCPM contributes to ongoing efforts in combating breast cancer and saving lives.

Optical coherence measurement-based penetration depth monitoring of stainless steel sheets in laser lap welding using long short-term memory network

The industrial production site for laser lap welding of thin stainless steel plates puts strict requests on the level and fluctuation stability of penetration depth. Hence, the penetration depth monitoring is increasingly garnering attention. This research proposes an optical coherence measurement-based approach to monitor penetration depth utilizing machine learning in laser lap welding for stainless steel sheets. After the acquisition of the keyhole depth signal by the coherent light beam, it is found that there is a significant association relationship between the reconstructed keyhole depth obtained by empirical modal decomposition and the penetration depth curve, but meanwhile a penetration depth monitoring error also exists. Accordingly, based on the cross-correlation analysis and numerical simulation, it is revealed that the formation mechanism and sources of this error are related to the “bottom melt layer thickness”, “hysteresis property” and “multiple reflections”. On this basis, a long short-term memory network is built to memorize the historical information of the reconstructed keyhole depth for predicting the penetration depth at each moment. The experimental results demonstrate that the prediction model has high accuracy and good generalization ability, and thus effective monitoring for penetration depth can be achieved.

An integrated framework utilizing machine learning to accelerate the optimization of energy-efficient urban block forms

An integrated framework utilizing machine learning to accelerate the optimization of energy-efficient urban block forms

Methods for phenotyping adult patients with acute kidney injury: a systematic review.

Acute kidney injury (AKI) is a multifaceted disease characterized by diverse clinical presentations and mechanisms. Advances in artificial intelligence have propelled the identification of AKI subphenotypes, enhancing our capacity to customize treatments and predict disease trajectories. We conducted a systematic review of the literature from 2017 to 2022, focusing on studies that utilized machine learning techniques to identify AKI subphenotypes in adult patients. Data were extracted regarding patient demographics, clustering methodologies, discriminators, and validation efforts from selected studies. The review highlights significant variability in subphenotype identification across different populations. All studies utilized clinical data such as comorbidities and laboratory variables to group patients. Two studies incorporated biomarkers of endothelial activation and inflammation into the clinical data to identify subphenotypes. The primary discriminators were comorbidities and laboratory trajectories. The association of AKI subphenotypes with mortality, renal recovery and treatment response was heterogeneous across studies. The use of diverse clustering techniques contributed to variability, complicating the application of findings across different patient populations. Identifying AKI subphenotypes enables clinicians to better understand and manage individual patient trajectories. Future research should focus on validating these phenotypes in larger, more diverse cohorts to enhance their clinical applicability and support personalized medicine in AKI management.

Utilizing machine learning to forecast mechanical characteristics of NaOH-Treated jute fiber reinforced composite materials

The prediction of mechanical properties in composite materials is crucial for optimizing their performance in various engineering applications. This study focuses on NaOH treated jute fiber reinforced glass epoxy composites, aiming to predict key mechanical characteristics like flexural strength, tensile strength, and the hardness. A comprehensive dataset of 974 samples was analyzed, encompassing a range of input parameters including fiber content, fiber orientation, matrix type, and processing conditions. Advanced statistical and machine learning techniques were employed to develop predictive models, with XGBoost showing itself to be the best model in terms of predicting each of the three mechanical properties. Feature importance analysis revealed that fiber content is the most significant factor influencing the predictions.

Utilising machine learning algorithms to predict the Marshall characteristics of asphalt pavement layers

Utilising machine learning algorithms to predict the Marshall characteristics of asphalt pavement layers

Research on the Design of Medical Imaging Assisted Diagnosis System Based on Machine Learning

The design and application of medical imaging diagnostic assistance systems have become an indispensable facet of modern medicine. Technologies based on machine learning can handle vast and intricate medical imaging data, offering precise diagnostic support and significantly enhancing the efficiency and accuracy of clinical diagnoses. This study focuses on the development and implementation of an efficient medical imaging diagnostic assistance system utilizing machine learning algorithms such as Principal Component Analysis (PCA), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP). Through the architectural design of intelligent diagnostic assistance systems, data preprocessing, and feature vectorization, combined with the application of classification algorithms, a robust diagnostic support system capable of addressing diverse medical scenarios has been constructed. Performance evaluation experiments indicate that this system demonstrates high accuracy and robustness in processing medical imaging data, holding potential for clinical implementation. The findings of this study provide new insights for the advancement of intelligent diagnostic systems in medical imaging and lay a solid foundation for the development of future clinical diagnostic tools.