Learning Classification Models Research Articles

Nutrient based classification of Phyllospora comosa biomasses using machine learning algorithms: Towards sustainable valorisation.

Sustainable seaweed value chains necessitate accurate biomass biochemical characterisation that leads to product development, geographical authentications and quality and sustainability assurances. Underutilised yet abundantly available seaweed species require a thorough investigation of biochemical characteristics prior to their valorisation. Abundantly available Australian seaweed species lack such comprehensive investigations within the global seaweed industrial value chains. Aiming to bridge this gap, this study characterises Phyllospora comosa thallus segments (blades, stipes, and vesicles) and unsegmented samples collected from separate locations in Victoria, Australia using high throughput characterisation techniques and machine learning classification models. Carbohydrate (64-68%), ash (27-31%), potassium (31.01 - 65.01mg/g), sodium (20.36 - 30.59mg/g), calcium (15.10 - 18.40mg/g), magnesium (7.71 - 11.81mg/g) and iodine (1.57 - 2.74mg/g) were the most abundant nutrients of the P. comosa biomasses, on a dry weight basis. Variations between segments showed that stipes were rich in carbohydrate, blades in glutamic acid, calcium, magnesium, and iodine and vesicles in potassium, suggesting differing valorisation paths. The "rpart" classification separated the collection sites based on cadmium: Bancoora < 84.9x10-6 mg/g (dw) ≤ Port Fairy with a 88% accuracy and segments, initially based on glutamic acid : blades ≥ 10.61mg/g (dw) or protein 45.25mg/g (dw) > stipes and vesicles and then by potassium : vesicles ≥ 44.88mg/g (dw) > stipes with a 100% accuracy. These highly accurate characterisation and classification methods, when applied to larger sample sizes will assist in the diversification and expansions of authentic and sustainable Australian seaweed value chains.

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.

Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationships of multiple brain networks on a millisecond scale. This study aimed to explore the pathological changes of whole-brain functional networks in patients with obsessive-compulsive disorders (OCD) through microstate analysis and further to explore its potential value as an auxiliary diagnostic index. Forty-eight OCD patients (33 with more than moderate anxiety symptoms, 15 with mild anxiety symptoms) and 52 healthy controls (HCs) were recruited. Brain activities during eyes-closed period were collected using 64-channel electroencephalography. The differences in microstate features between OCD patients and HCs were compared, and the relationship between the microstate features and clinical symptoms were explored. Key microstate features were selected for machine learning modeling to achieve targeted classifications. The probability of transition from microstate B to C was significantly lower in OCD patients compared to HCs, and the obsessive thoughts factor scores were significantly correlated with the duration of microstate A, the occurrence of microstate B, and the transition probability from microstate C to B. The occurrence rate of microstate C was significantly negatively correlated with the Hamilton rating scale for anxiety (HAMA) scores. The AUC (Area Under the Receiver Operating Characteristic Curve) of the machine learning model in the test set classification between the above two groups and between OCD patients with more than moderate/mild anxiety symptoms could achieve 70.43% and 77.13%, respectively. EEG microstate characteristics were altered in OCD patients, and these changes were closely associated with obsessive thoughts and anxiety symptoms. Besides, the machine learning classification model based on microstate features has limited ability to identify OCD, and further optimization on this classification approach is still needed in the future.

Data‐Driven Analysis of Ni‐Catalyzed Semihydrogenations of Alkynes

The semihydrogenation of alkynes to alkenes has historically been an essential technique in organic chemistry. In this context, researchers often employ transition metal complexes to achieve this conversion. Given the pronounced polarization of results, often yielding either very high or very low values, it remains challenging to discern the factors influencing reactivity and selectivity in many cases. In this work, we combine different sub‐disciplines of digital chemistry with experimental outcomes to rationalize the results of a model Ni‐catalyzed semihydrogenation that leads to E‐alkenes. First, we analyze the main factors behind successful reactions using a machine learning classification model. The descriptors are computed directly from the SMILES strings of the reacting alkynes using an automated protocol that relies on structural features, molecular mechanics, and semi‐empirical techniques. This workflow requires minimal human intervention and provides a fast yet useful approach. Next, we couple the same descriptors with activation barriers calculated with density functional theory, generating a regression model that explains reactivity based on the properties of the alkyne substrates. Overall, this study demonstrates the potential of using a combination of digital chemistry techniques to uncover reaction trends in Ni‐catalyzed semihydrogenations of alkynes, an area where human intuition proves limited in application.

Deterministic and Stochastic Machine Learning Classification Models: A Comparative Study Applied to Companies’ Capital Structures

Corporate financing decisions, particularly the choice between equity and debt, significantly impact a company’s financial health and value. This study predicts binary corporate debt levels (high or low) using supervised machine learning (ML) models and firms’ characteristics as predictive variables. Key features include companies’ size, tangibility, profitability, liquidity, growth opportunities, risk, and industry. Deterministic models, represented by logistic regression and multilevel logistic regression, and stochastic approaches that incorporate a certain degree of randomness or probability, including decision trees, random forests, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks, were evaluated using usual metrics. The results indicate that decision trees, random forest, and XGBoost excelled in the training phase but showed higher overfitting when evaluated in the test sample. Deterministic models, in contrast, were less prone to overfitting. Notably, all models delivered statistically similar results in the test sample, emphasizing the need to balance performance, simplicity, and interpretability. These findings provide actionable insights for managers to benchmark their company’s debt level and improve financing strategies. Furthermore, this study contributes to ML applications in corporate finance by comparing deterministic and stochastic models in predicting capital structure, offering a robust tool to enhance managerial decision-making and optimize financial strategies.

Detecting autism in children through drawing characteristics using the visual-motor integration test.

This study introduces a novel classification method to distinguish children with autism from typically developing children. We recruited 50 school-age children in Taiwan, including 44 boys and 6 girls aged 6 to 12 years, and asked them to draw patterns from a visual-motor integration test to collect data and train deep learning classification models. Ensemble learning was adopted to significantly improve the classification accuracy to 0.934. Moreover, we identified five patterns that most effectively differentiate the drawing performance between children with and without ASD. From these five patterns we found that children with ASD had difficulty producing patterns that include circles and spatial relationships. These results align with previous findings in the field of visual-motor perceptions of individuals with autism. Our results offer a potential cross-cultural tool to detect autism, which can further promote early detection and intervention of autism.

Predicting Drug Resistance in Mycobacterium tuberculosis: A Machine Learning Approach to Genomic Mutation Analysis

Background/Objectives: Tuberculosis (TB) is one of the leading causes of death by infectious disease in the world, caused by the bacterium Mycobacterium tuberculosis (M. tuberculosis). First- and second-line drugs are used to treat active tuberculosis. However, drug resistance presents a critical challenge in the global fight against tuberculosis. Materials and Methods: Drug resistance diagnosis is typically performed through drug susceptibility testing, either via culture-based methods or molecular rapid tests. In recent years, studies have utilized whole-genome sequencing of M. tuberculosis isolates combined with machine learning techniques to predict drug resistance. In this study, we evaluated four machine learning classification models: Extreme Gradient Boosting Classifier (XGBC), Logistic Gradient Boosting Classifier (LGBC), Gradient Boosting Classifier (GBC), and an Artificial Neural Network (ANN). These models were trained using a Variant Call Format (VCF) file preprocessed by the CRyPTIC consortium. We employed three datasets: the original dataset, a dataset reduced through principal component analysis, and a dataset that prioritized the most important features identified by the XGBC model. Results: The four models were utilized on the principal component analysis dataset, while the XGBC model was additionally implemented with an arbitrarily reduced dataset focusing on the most significant mutations identified during model training. All models were trained and tested across these datasets, and their performances were compared. The XGBC model trained on the original dataset outperformed the others, achieving sensitivity values of 0.97, 0.90, and 0.94, specificity values of 0.97, 0.99, and 0.96, and F1-scores of 0.93, 0.94, and 0.92 for ethambutol, isoniazid, and rifampicin, respectively. Discussion: This study highlights the effectiveness of a binary representation of mutations (indicating their presence or absence) as a robust approach for training XGBC models that accurately classify resistance and susceptibility to ethambutol, isoniazid, and rifampicin in M. tuberculosis. Conclusion: The XGBC model trained on the original dataset demonstrated superior performance compared to the other models, indicating its potential for improving drug resistance prediction in TB. This approach could be further explored for clinical applications in the fight against drug-resistant tuberculosis.

A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.

To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators. Tabular data with missing laboratory indicators were collected from 393 patients with ovarian cancer and 1951 control patients. The missing ovarian cancer laboratory indicator features were projected to the latent space to obtain a classification model using the representational learning classification model based on discriminative learning and mutual information coupled with feature projection significance score consistency and missing location estimation. The proposed constraint term was ablated experimentally to assess the feasibility and validity of the constraint term by accuracy, area under the ROC curve (AUC), sensitivity, and specificity. Cross-validation methods and accuracy, AUC, sensitivity and specificity were also used to evaluate the discriminative performance of this classification model in comparison with other interpolation methods for processing of the missing data. The results of the ablation experiments showed good compatibility among the constraints, and each constraint had good robustness. The cross-validation experiment showed that for identification of ovarian cancer with missing laboratory indicators, the AUC, accuracy, sensitivity and specificity of the proposed multi-constraints representation-based learning classification model was 0.915, 0.888, 0.774, and 0.910, respectively, and its AUC and sensitivity were superior to those of other interpolation methods. The proposed model has excellent discriminatory ability with better performance than other missing data interpolation methods for identification of ovarian cancer with missing laboratory indicators.

Integrating C-H Information to Improve Machine Learning Classification Models for Microplastic Identification from Raman Spectra.

Research has shown microplastic particles to be pervasive pollutants in the natural environment, but labor-intensive sample preparation, data acquisition, and analysis protocols continue to be necessary to navigate their diverse chemistry. Machine learning (ML) classification models have shown promise for identifying microplastics from their Raman spectra, but all attempts to date have focused on the lower energy "fingerprint" region of the spectrum. We explore strategies to improve ML classification models based on the k-nearest-neighbor algorithm by including other regions of the Raman spectra. The information content inherent in C-H bonds, which occur in the higher frequency region of 2500-3600 cm-1, is found to be particularly powerful in improving classification model performance. Variations in the relative intensity of peaks arising from C-H vibrations improve identification capabilities for plastics that the fingerprint region alone struggles with, such as resolving acrylonitrile butadiene styrene from polystyrene and identifying poly(vinyl chloride), polyurethane, and polyoxymethylene. Testing of strategies to both acquire and analyze data across the two regions is explored for their efficacy and their compatibility with real-world sampling restrictions. We find that localized normalization of spectra, independently acquired in the two regions, provides the most direct and effective route to improving the ML classification performance.

Enhancing supply chain resilience through supervised machine learning: supplier performance analysis and risk profiling for a multi-class classification problem

PurposeThis paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature.Design/methodology/approachThis research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations.FindingsThe study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service.Research limitations/implicationsThe findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties.Practical implicationsThe practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly.Social implicationsThe study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains.Originality/valueUnlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.

Fast and accurate deep learning scans for signatures of natural selection in genomes using FASTER-NN

Deep learning classification models based on Convolutional Neural Networks (CNNs) are increasingly used in population genetic inference for detecting signatures of natural selection. Prevailing detection methods treat the design of the classifier as a discrete phase, assuming that high classification accuracy is the sole prerequisite for precise detection. This frequently steers method development toward classification-driven optimizations that can inadvertently impede detection. We present FASTER-NN, a CNN classifier designed specifically for the precise detection of natural selection. It has higher sensitivity than state-of-the-art CNN classifiers while only processing allele frequencies and genomic positions through dilated convolutions to maximize data reuse. As a result, execution time is invariant to the sample size and the chromosome length, creating a highly suitable solution for large-scale, whole-genome scans. Furthermore, FASTER-NN can accurately identify selective sweeps in recombination hotspots, which is a highly challenging detection problem with very limited theoretical treatment to date.

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.

Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diagnosis and treatment rates remain low due to its diverse and often overlapping clinical manifestations. In this context, electroencephalography (EEG) has gained attention as a potential objective tool for diagnosing depression. This study aimed to evaluate the effectiveness of EEG in identifying MDD by analyzing 140 EEG recordings from patients diagnosed with depression and healthy volunteers. Using various machine learning (ML) classification models, we achieved up to 80% accuracy in distinguishing individuals with MDD from healthy controls. Despite its promise, this approach has limitations. The variability in the clinical and biological presentations of depression, as well as patient-specific confounding factors, must be carefully considered when integrating ML technologies into clinical practice. Nevertheless, our findings suggest that an EEG-based ML model holds potential as a diagnostic aid for MDD, paving the way for further refinement and clinical application.

Prediction of urinary tract infection using machine learning methods: a study for finding the most-informative variables

BackgroundUrinary tract infection (UTI) is a frequent health-threatening condition. Early reliable diagnosis of UTI helps to prevent misuse or overuse of antibiotics and hence prevent antibiotic resistance. The gold standard for UTI diagnosis is urine culture which is a time-consuming and also an error prone method. In this regard, complementary methods are demanded. In the recent decade, machine learning strategies that employ mathematical models on a dataset to extract the most informative hidden information are the center of interest for prediction and diagnosis purposes.MethodIn this study, machine learning approaches were used for finding the important variables for a reliable prediction of UTI. Several types of machines including classical and deep learning models were used for this purpose.ResultsEighteen selected features from urine test, blood test, and demographic data were found as the most informative features. Factors extracted from urine such as WBC, nitrite, leukocyte, clarity, color, blood, bilirubin, urobilinogen, and factors extracted from blood test like mean platelet volume, lymphocyte, glucose, red blood cell distribution width, and potassium, and demographic data such as age, gender and previous use of antibiotics were the determinative factors for UTI prediction. An ensemble combination of XGBoost, decision tree, and light gradient boosting machines with a voting scheme obtained the highest accuracy for UTI prediction (AUC: 88.53 (0.25), accuracy: 85.64 (0.20)%), according to the selected features. Furthermore, the results showed the importance of gender and age for UTI prediction. ConclusionThis study highlighted the potential of machine learning strategies for UTI prediction.

Prediction of early postoperative recurrence of hepatocellular carcinoma by habitat analysis based on different sequence of contrast-enhanced CT.

To develop a habitat imaging method for preoperative prediction of early postoperative recurrence of hepatocellular carcinoma. A retrospective cohort study was conducted to collect data on 344 patients who underwent liver resection for HCC. The internal subregion of the tumor was objectively delineated and the clinical features were also analyzed to construct clinical models. Radiomics feature extraction was performed on tumor subregions of arterial and portal venous phase images. Machine learning classification models were constructed as a fusion model combining the three different models, and the models were assessed. A comprehensive retrospective analysis was conducted on a cohort of 344 patients who underwent hepatic cancer resection at one of the two centers. it was found that the combined SVM model yielded superior results after comparing various metrics, such as the AUC, accuracy, sensitivity, specificity, and DCA. Habitat analysis of sequential CT images can delineate distinct subregions within a tumor, offering valuable insights for early prediction of postoperative HCC recurrence.

Filter Based Sentiment Feature Selection Using Back Propagation Deep Learning

Aim: The proposed Filter Based Sentiment Feature Selection (FBSFS) model focuses on to improve the performance of Sentiment Learning (SL) by selecting the most relevant sentiment features from text reviews using feature selection methods at document level. Method: Sentiment Learning is applied at the document level for classifying text reviews into two categories either positive or negative. The key sentiment features adjectives (ADJ), adverbs (ADV), and verbs (VRB) which are essential for sentiment analysis, are extracted from text document using the WordNet dictionary. Feature selection is performed by applying four different algorithms: Information Gain, Correlation, Gini Index, and Chi-Square. These algorithms help identify the most significant features that contribute to sentiment classification. The selected features are then fed into a Back Propagation Deep Learning (BPDL) classification model for sentiment analysis. Result: The experimental findings show that the proposed model achieved higher accuracy of 91.15% using Correlation feature selection. This accuracy signifies the effectiveness of the proposed model in classifying text reviews, outperforming other methods in terms of sentiment feature selection and classification. Conclusion: The proposed model enhances the performance of sentiment learning by selecting the most relevant sentiment features, particularly those extracted from adjectives, adverbs, and verbs, and combining them with BPDL. The FBSFS model as a robust tool for sentiment classification.

Construction of an Interpretable Model of the Risk of Post-Traumatic Brain Infarction Based on Machine Learning Algorithms: A Retrospective Study.

Post-traumatic cerebral infarction (PTCI) is a severe complication resulting from traumatic brain injury (TBI), which can lead to permanent neurological damage or death. The investigation of the factors associated with PTCI and the establishment of predictive models are crucial for clinical practice. We made a retrospective analysis of clinical data from 1484 TBI patients admitted to the Neurosurgery Department of a provincial hospital from January 2018 to December 2023. Predictive factors were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) and multivariable logistic regression analysis. Several machine learning (ML) classification models were developed and compared. The interpretations of the ML models' predictions were provided by SHAP values. Key predictors included age, bilateral brain contusions, platelet count, uric acid, glucose, traumatic subarachnoid hemorrhage, and surgical treatment. The logistic regression (LR) model outperformed other ML algorithms, demonstrating superior performance in the test set with an AUC of 0.821, accuracy of 0.845, Matthews correlation coefficient (MCC) of 0.264, area under the receiver operating characteristic curve (AUROC) of 0.711, precision of 0.56, and specificity of 0.971. It had stable performance in the ten-fold cross-validation. ML algorithms, integrating demographic and clinical factors, accurately predicted the risk of PTCI occurrence. Interpretations using the SHAP method offer guidance for personalized treatment of different patients, filling gaps between complex clinical data and actionable insights.

Performance and Analysis of FCN, U-Net, and SegNet in Remote Sensing Image Segmentation Based on the LoveDA Dataset

Remote sensing image segmentation is a vital method in image analysis that significantly contributes to the extraction of surface information and aids in land use planning. This study utilizes the LoveDA dataset to investigate the segmentation performance of three classic deep learning models: Fully Convolutional Networks(FCN), U-Net, and SegNet, in both urban and rural scenarios. By partitioning the Urban-Rural dataset of LoveDA for training and testing, it was determined that SegNet excels in detail restoration and boundary handling, while U-Net demonstrates robust adaptability across various scenarios. In contrast, FCN, with its simpler architecture, shows lower segmentation accuracy in certain contexts. This paper offers a comprehensive comparison of the strengths and weaknesses of different models in remote sensing image segmentation and proposes enhancements in model structure and data preprocessing optimization. The findings provide valuable insights for improving the performance of semantic segmentation models and are of significant importance for the precise analysis and practical applications of remote sensing images.

Lung Cancer Prediction from Smoking Cause by Machine Learning Classification Models

The incidence of lung cancer varies in males and females, which occurs due to the abnormal and uncontrolled growth of cells in the lungs. It has a greater predilection in males as compared to females. Smoking is the most important risk factor for lung cancer. It causes serious breathing issues and also affects other organs. It increases the mortality rate both in young adults as well as in the older age group. Therefore, there is improvement in medical technologies to facilitate specialized diagnosis and treatment, but the mortality has not been controlled to a satisfactory extent. It is important to take preventive measures and precautions at the initial stages. Machine learning brings various advancements to the medical sector due to which various diseases can be detected at an early stage. In this paper, we presented different machine learning classifier techniques used for the classification of the present lung cancer data in the UCI machine learning repository as benign and malignant. The dataset is divided into cancerous and non-cancerous by converting the input data into binary form and using the classifier technique in theWeka tool. This specifically includes classifiers used: Logistic Regression, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Decision Trees, and Na¨ıve Bayes. In addition, we study the effect of data preprocessing methods on our prediction accuracy, such as data normalization and feature selection. The study seeks to help develop various reliable resources for lung cancer identification, which are critical for diagnosing and treating patients in a timely manner and improving their outcomes.

Machine Learning--based Discovery of Novel Oxide and Halide Perovskites for Energy Storage

In this study, the novel oxide and halide perovskite compounds in the form of single and double perovskite are explored by using machine learning, density functional theory databases, and experimental databases. The important features that define the formability and stability of oxide perovskite and halide perovskite compounds are extracted. Using these datasets as training datasets, very precise machine learning classification models are prepared using random forest classifiers for formability and stability. Subsequently, these results are cross-validated to screen the potential novel compounds. Further, the hierarchical clustering technique is used to understand the effect of multi-collinearity among selected features and also to analyze their effect on perovskite formability and stability. Our machine learning model predicts 591 novel perovskite compounds which are formable as well as stable. Further, the charge capacity of these novel compounds is also analyzed for battery applications.

Identification of molecular and cellular infection response biomarkers associated with anthrax infection through comparative analysis of gene expression data.

Bacillus anthracis, a gram-positive bacillus capable of forming spores, causes anthrax in mammals, including humans, and is recognized as a potential biological weapon agent. The diagnosis of anthrax is challenging due to variable symptoms resulting from exposure and infection severity. Despite the availability of a licensed vaccines, their limited long-term efficacy underscores the inadequacy of current human anthrax vaccines, highlighting the urgent need for next-generation alternatives. Our study aimed to identify molecular biomarkers and essential biological pathways for the early detection and accurate diagnosis of human anthrax infection. Using a comparative analysis of Bacillus anthracis gene expression data from the Gene Expression Omnibus (GEO) database, this cost-effective approach enables the identification of shared differentially expressed genes (DEGs) across separate microarray datasets without additional hybridization. Three microarray datasets (GSE34407, GSE14390, and GSE12131) of B. anthracis-infected human cell lines were analyzed via the GEO2R tool to identify shared DEGs. We identified 241 common DEGs (70 upregulated and 171 downregulated) from cell lines treated similarly to lethal toxins. Additionally, 10 common DEGs (5 upregulated and 5 downregulated) were identified across different treatments (lethal toxins and spores) and cell lines. Network meta-analysis identified JUN and GATAD2A as the top hub genes for overexpression, and NEDD4L and GULP1 for underexpression. Furthermore, prognostic analysis and SNP detection of the two identified upregulated hub genes were carried out in conjunction with machine learning classification models, with SVM yielding the best classification accuracy of 87.5%. Our comparative analysis of Bacillus anthracis infection revealed striking similarities in gene expression 241 profiles across diverse datasets, despite variations in treatments and cell lines. These findings underscore how anthrax infection activates shared genes across different cell types, emphasizing this approach in the discovery of novel gene markers. These markers offer insights into pathogenesis and may lead to more effective therapeutic strategies. By identifying these genetic indicators, we can advance the development of precise immunotherapies, potentially enhancing vaccine efficacy and treatment outcomes.

Fluid volume status detection model for patients with heart failure based on machine learning methods.

Fluid volume abnormalities are a major cause of exacerbations in heart failure patients. However, there is few efficient, rapid, or cost-effective clinical approach for determining volume status, resulting in inadequate or unsatisfactory treatment. The aim was to develop an early fluid volume detection model for heart failure patients utilizing a machine learning stratification. The training set data collected by Tianjin Chest Hospital on heart failure patients from December 2016 to December 2021, included 2056 samples and 97 medical characteristics. The minimum Redundancy Maximum Relevance(mRMR) feature selection method was utilized to filter features that were strongly related to the patient's fluid volume status. Four machine learning classification models were used to predict patients' fluid volume status, and their effectiveness was measured using the receiver operating characteristic (ROC) area under the curve (AUC), calibration curve, accuracy, precision, recall, F1 score, specificity, and sensitivity. Data from 186 heart failure patients collected between January 2022 and July 2022 were employed as an external validation set to investigate the effects of model training. SHapley Additive exPlanations (SHAP) were used to interpret the ML models. Thirty features were selected for model development, and the area under the ROC curve AUC (95% CI) for the four machine learning models in the testing set was 0.75 (0.73-0.77), 0.77 (0.74-0.79), 0.70 (0.67-0.73), and 0.76 (0.73-0.78), and the AUC (95% CI) in the external validation set was 0.74 (0.71-0.76), 0.70 (0.67-0.73), 0.64 (0.59-0.68), and 0.67 (0.63-0.71). Logistic regression models were globally interpreted using SHAP-based summary plots. Machine learning methods are effective in detecting fluid volume status in heart failure patients and can assist physicians with assisted diagnosis, thus helping clinicians to tailor precise management.