Random Forest Machine Learning Model Research Articles

Quantifying crowd engagement with machine learning

Inferring a crowd’s engagement at a sporting event using only acoustic signals is difficult for humans to do precisely, but it may be a great job for machine learning. This presentation will summarize the results of using acoustic data from crowds at four types of college sporting events to train a random forest machine-learning model to identify behaviors such as cheering, chanting, and distracting the opposing team. The model uses spectral data and other features derived from acoustic measurements such as spectral slope, spectral kurtosis, and zero cross rate to quantify the level of crowd engagement. This presentation will then discuss an ongoing investigation of how the model performs at predicting crowd engagement from acoustic data collected with a new data-collection system. It will discuss specific challenges and opportunities associated with incorporating new data into an existing machine-learning pipeline.

Satellite imagery as a management tool for monitoring water clarity across freshwater ponds on Cape Cod, Massachusetts

Water clarity serves as both an indicator and a regulator of biological function in aquatic systems. Large-scale, consistent water clarity monitoring is needed for informed decision-making. Inland freshwater ponds and lakes across Cape Cod, a 100-km peninsula in Massachusetts, are of particular interest for water clarity monitoring. Secchi disk depth (SDD), a common measure of water clarity, has been measured intermittently for over 200 Cape Cod ponds since 2001. Field-measured SDD data were used to estimate SDD from satellite data, leveraging the NASA/USGS Landsat Program and Copernicus Sentinel-2 mission, spanning 1984 to 2022. Random forest machine learning models were generated to estimate SDD from satellite reflectance data and maximum pond depth. Spearman rank correlations (rs) were “strong” for Landsat 5 and 7 (rs = 0.78 and 0.79), and “very strong” for Landsat 8, 9, and Sentinel-2 (rs = 0.83, 0.86, and 0.80). Mean absolute error also indicated strong predictive capacity, ranging from 0.65 to 1.05 m, while average bias ranged from −0.20 to 0.06 m. Long- and recent short-term changes in satellite-estimated SDD were assessed for 193 ponds, selected based on surface area and the availability of maximum pond depth data. Long-term changes between 1984 and 2022 established a retrospective baseline using the Mann-Kendall test for trend and Theil-Sen slope. Generally, long-term water clarity improved across the Cape; 149 ponds indicated increasing water clarity, and 8 indicated deteriorating water clarity. Recent short-term changes between 2021 and 2022 identified ponds that may benefit from targeted management efforts using the Mann-Whitney U test. Between 2021 and 2022, 96 ponds indicated deteriorations in water clarity, and no ponds improved in water clarity. While the 193 ponds analyzed here constitute only one quarter of Cape Cod ponds, they represent 85% of its freshwater surface area, providing the most spatially and temporally comprehensive assessment of Cape Cod ponds to date. Efforts are focused on Cape Cod, but can be applied to other areas given the availability of local field data. This study defines a framework for monitoring and assessing change in satellite-estimated SDD, which is important for both local and regional management and resource prioritization.

Construction of An Oral Bioavailability Prediction Model Based on Machine Learning for Evaluating Molecular Modifications

ObjectiveThis study aims to explore the impact of ADME on the Oral Bioavailability (OB) of drugs and to construct a machine learning model for OB prediction. The model is then applied to predict the OB of modified berberine and atenolol molecules to obtain structures with higher OB. MethodsInitially, a drug OB database was established, and corresponding ADME characteristics were obtained. The relationship between ADME and OB was analyzed using machine learning, with Morgan fingerprints serving as molecular descriptors. Compounds from the database were input into Random Forest, XGBoost, CatBoost, and LightGBM machine learning models to train the OB 7prediction model and evaluate its performance. Subsequently, berberine and atenolol were modified using Chemdraw software with ten different substituents for mono-substitution, and chlorine atoms for a full range of double substitutions. The modified molecular structures were converted into the same format as the training set for OB prediction. The predicted OB values of the modified structures of berberine and atenolol were compared. ResultsAn OB database of 386 drugs was obtained. It was found that smaller molecular weight and a higher number of rotatable bonds (ten or less) could potentially lead to higher OB. The four machine learning models were evaluated using MSE, R2 score, MAE, and MFE as metrics, with Random Forest performing the best. The models' predictions for the test set were particularly accurate when OB ranged from 30% to 90%. After mono-substitution and double substitution of berberine and atenolol, the OB of both drugs was significantly improved. ConclusionsThis study found that some ADME properties of molecules do not have an absolute impact on OB. The database played a decisive role in the process of the machine learning OB prediction model, and the performance of the model was evaluated based on predictions within a range of strong generalization ability. In most cases, mono-substitution and double substitution were beneficial for enhancing the OB of berberine and atenolol. In summary, this study successfully constructed a machine learning regression prediction model that can accurately predict drug OB, which can guide drug design to achieve higher OB to some extent.

Machine Learning to Predict Length of Stay Following Revision Hip Arthroplasty

AbstractPredicting length of stay (LOS) for revision total hip arthroplasty (rTHA) remains challenging due to the complexity of cases and increased complication rates compared with primary total hip arthroplasty. Artificial intelligence may play a role in predicting LOS after rTHA. This study aims to develop a machine learning model to predict LOS in rTHA patients based on preoperative and intraoperative factors. The American College of Surgeons National Surgical Quality Improvement Program database was used to identify patients undergoing a rTHA between 2012 and 2018. The data were used to train both random forest machine and logistic regression machine learning models to predict short LOS (≤ 1 day) or long LOS (≥ 2 days). Statistical analysis was performed to analyze differences between short and long LOS groups. Logistic regression analysis was used to calculate odds ratios associated with long LOS. A total of 4,228 patients were identified in this analysis with a mean postoperative LOS of 3.69 days. Preoperative features associated with a short LOS included male sex, noninfectious revision, spinal anesthesia, later year of operation, younger age, smoking history, and lower body mass index. Area under the receiver operating characteristic curve was calculated to measure model performance for the random forest model and logistic regression model as 0.76 and 0.78, respectively. The machine learning model presented here was able to reasonably predict LOS for revision hip arthroplasty showing promise for use as patient selection tool to identify short LOS patients.

Predicting hosts and cross-species transmission of Streptococcus agalactiae by interpretable machine learning

BackgroundStreptococcus agalactiae, commonly known as Group B Streptococcus (GBS), exhibits a broad host range, manifesting as both a beneficial commensal and an opportunistic pathogen across various species. In humans, it poses significant risks, causing neonatal sepsis and meningitis, along with severe infections in adults. Additionally, it impacts livestock by inducing mastitis in bovines and contributing to epidemic mortality in fish populations. Despite its wide host spectrum, the mechanisms enabling GBS to adapt to specific hosts remain inadequately elucidated. Therefore, the development of a rapid and accurate method differentiates GBS strains associated with particular animal hosts based on genome-wide information holds immense potential. Such a tool would not only bolster the identification and containment efforts during GBS outbreaks but also deepen our comprehension of the bacteria's host adaptations spanning humans, livestock, and other natural animal reservoirs. Methods and resultsHere, we developed three machine learning models—random forest (RF), logistic regression (LR), and support vector machine (SVM) based on genome-wide mutation data. These models enabled precise prediction of the host origin of GBS, accurately distinguishing between human, bovine, fish, and pig hosts. Moreover, we conducted an interpretable machine learning using SHapley Additive exPlanations (SHAP) and variant annotation to uncover the most influential genomic features and associated genes for each host. Additionally, by meticulously examining misclassified samples, we gained valuable insights into the dynamics of host transmission and the potential for zoonotic infections. ConclusionsOur study underscores the effectiveness of random forest (RF) and logistic regression (LR) models based on mutation data for accurately predicting GBS host origins. Additionally, we identify the key features associated with each GBS host, thereby enhancing our understanding of the bacteria's host-specific adaptations.

DNA Methylation Profiling Enables Accurate Classification of Nonductal Primary Pancreatic Neoplasms

Background and aimsCytological and histopathological diagnosis of non-ductal pancreatic neoplasms can be challenging in daily clinical practice while it is crucial for therapy and prognosis. The cancer methylome is successfully used as a diagnostic tool in other cancer entities. Here, we investigate if methylation profiling can improve the diagnostic work-up of pancreatic neoplasms. MethodsDNA methylation data were obtained for 301 primary tumors spanning six primary pancreatic neoplasms and 20 normal pancreas controls. Neural Network, Random Forest, and XGBoost machine learning models were trained to distinguish between tumor types. Methylation data of 29 non-pancreatic neoplasms (n = 3708) were used to develop an algorithm capable of detecting neoplasms of non-pancreatic origin. ResultsAfter benchmarking three state-of-the-art machine learning models, the Random Forest model emerged as the best classifier with 96.9% accuracy. All classifications received a probability score reflecting the confidence of the prediction. Increasing the score threshold, improved the Random Forest classifier performance up to 100% with 87% of samples with scores surpassing the cutoff. Using a logistic regression model, detection of non-pancreatic neoplasms achieved an area under the curve (AUC) of > 0.99. Analysis of biopsy specimens showed concordant classification with their paired resection sample. ConclusionPancreatic neoplasms can be classified with high accuracy based on DNA methylation signatures. Additionally, non-pancreatic neoplasms are identified with near perfect precision. In summary, methylation profiling can serve as a valuable adjunct in the diagnosis of pancreatic neoplasms with minimal risk for misdiagnosis, even in the pre-operative setting.

HRpredict: Introducing a Web-Based Application for Heart Rate Prediction and Lifestyle Recommendations

Background: Monitoring heart health requires early detection of deviations in HR, which makes it easier to detect and address heart irregularities at an early stage. Health remote systems when combined with artificial intelligence (AI) can assist in better health outcomes through early detection of heart problems. Aims: Our main goal is to create a website application (Web-App) for web browser access, aiming to utilize a Random Forest (RF) machine learning (ML) model trained to predict the average heart rate (HR) over 10 days for different periods, and to enable lifestyle and activity recommendations. Methods: The Web-App is created using Laravel, an open-source Personal Home Page (PHP) web framework that follows the model-view-controller (MVC) architectural pattern. Results: This research resulted in a web-based ML model that can be used to predict future heart rates over a 10-day period which are utilized to establish average HR values, considering baseline and three distinct periods: morning, noon, and evening across the 10-day duration. Through this Web-App lifestyle, habit, activity, and 10-day reassessment recommendations are also provided. Conclusion: The Web-App was designed to be accessed and used through a web browser, to provide lifestyle recommendations based on predicted HR readings. To determine the impact of users adhering to recommendations, further research is required.

Multi-Objective Stochastic Paint Optimizer for Solving Dynamic Economic Emission Dispatch with Transmission Loss Prediction Using Random Forest Machine Learning Model

Dynamic economic emission dispatch problems are complex optimization tasks in power systems that aim to simultaneously minimize both fuel costs and pollutant emissions while satisfying various system constraints. Traditional methods often involve solving intricate nonlinear load flow equations or employing approximate loss formulas to account for transmission losses. These methods can be computationally expensive and may not accurately represent the actual transmission losses, affecting the overall optimization results. To address these limitations, this study proposes a novel approach that integrates transmission loss prediction into the dynamic economic emission dispatch (DEED) problem. A Random Forest machine learning model was offline-trained to predict transmission losses accurately, eliminating the need for repeated calculations during each iteration of the optimization process. This significantly reduced the computational burden of the algorithm and improved its efficiency. The proposed method utilizes a powerful multi-objective stochastic paint optimizer to solve the highly constrained and complex dynamic economic emission dispatch problem integrated with random forest-based loss prediction. A fuzzy membership-based approach was employed to determine the best compromise Pareto-optimal solution. The proposed algorithm integrated with loss prediction was validated on widely used five and ten-unit power systems with B-loss coefficients. The results obtained using the proposed algorithm were compared with seventeen algorithms available in the literature, demonstrating that the multi-objective stochastic paint optimizer (MOSPO) outperforms most existing algorithms. Notably, for the Institute of Electrical and Electronics Engineers (IEEE) thirty bus system, the proposed algorithm achieves yearly fuel cost savings of USD 37,339.5 and USD 3423.7 compared to the existing group search optimizer algorithm with multiple producers (GSOMP) and multi-objective multi-verse optimization (MOMVO) algorithms.

A supervised machine learning model for identifying predictive factors for recommending head and neck cancer surgery.

New patient referrals are often processed by practice coordinators with little-to-no medical background. Treatment delays due to incorrect referral processing, however, have detrimental consequences. Identifying variables that are associated with a higher likelihood of surgical oncological resection may improve patient referral processing and expedite the time to treatment. The study objective is to develop a supervised machine learning (ML) platform that identifies relevant variables associated with head and neck surgical resection. A retrospective cohort study was conducted on 64 222 patient datapoints from the SEER database. The random forest ML model correctly classified patients who were offered head and neck surgery with an 81% accuracy rate. The sensitivity and specificity rates were 86% and 71%. The positive and negative predictive values were 85% and 73%. ML modeling accurately predicts head and neck cancer surgery recommendations based on patient and cancer information from a large population-based dataset. ML adjuncts for referral processing may decrease the time to treatment for patients with cancer.

Machine learning-based optimal distributed generation and electric vehicle load management

The load profile of radial distribution networks (RDNs) is significantly impacted when plug-in electric vehicles (PEVs) are connected to them in large numbers. The disturbances in the load profile may lead to increased power losses in distribution lines and deterioration of the network voltage profile. The provision of distributed generation at strategic locations in the distribution network can help to compensate for the impact on the electrical network due to PEV loads. This paper proposes the use of machine learning (ML)-based models for determining the optimal location of distributed generators (DGs) in an RDN. The proposed method considered time-varying load in addition to PEV load. The suggested method determines optimal placement of DGs based on the power loss reduction index and voltage deviation index reduction index. Four distinct types of ML models were used in the proposed approach using synthesised data on the Institute of Electrical and Electronics Engineers' 33-bus RDN. The performance of the ML models was evaluated with respect to mean squared error and mean absolute percentage error and, for the ML models considered, the random forest ML model gave the best performance.

Digital assessment of speech in Huntington disease.

Speech changes are an early symptom of Huntington disease (HD) and may occur prior to other motor and cognitive symptoms. Assessment of HD commonly uses clinician-rated outcome measures, which can be limited by observer variability and episodic administration. Speech symptoms are well suited for evaluation by digital measures which can enable sensitive, frequent, passive, and remote administration. We collected audio recordings using an external microphone of 36 (18 HD, 7 prodromal HD, and 11 control) participants completing passage reading, counting forward, and counting backwards speech tasks. Motor and cognitive assessments were also administered. Features including pausing, pitch, and accuracy were automatically extracted from recordings using the BioDigit Speech software and compared between the three groups. Speech features were also analyzed by the Unified Huntington Disease Rating Scale (UHDRS) dysarthria score. Random forest machine learning models were implemented to predict clinical status and clinical scores from speech features. Significant differences in pausing, intelligibility, and accuracy features were observed between HD, prodromal HD, and control groups for the passage reading task (e.g., p < 0.001 with Cohen'd = -2 between HD and control groups for pause ratio). A few parameters were significantly different between the HD and control groups for the counting forward and backwards speech tasks. A random forest classifier predicted clinical status from speech tasks with a balanced accuracy of 73% and an AUC of 0.92. Random forest regressors predicted clinical outcomes from speech features with mean absolute error ranging from 2.43-9.64 for UHDRS total functional capacity, motor and dysarthria scores, and explained variance ranging from 14 to 65%. Montreal Cognitive Assessment scores were predicted with mean absolute error of 2.3 and explained variance of 30%. Speech data have the potential to be a valuable digital measure of HD progression, and can also enable remote, frequent disease assessment in prodromal HD and HD. Clinical status and disease severity were predicted from extracted speech features using random forest machine learning models. Speech measurements could be leveraged as sensitive marker of clinical onset and disease progression in future clinical trials.

Decoding Diagnostic Precision: Evaluating Error Implications with Random Forest and Support Vector Regression Machine Learning Models

Decoding Diagnostic Precision: Evaluating Error Implications with Random Forest and Support Vector Regression Machine Learning Models

Diversity, composition, metabolic characteristics, and assembly process of the microbial community in sewer system at the early stage.

Sewer systems play vital roles in wastewater treatment facilities, and the microbial communities contribute significantly to the transformation of domestic wastewater. Therefore, this study conducted a 180-day experiment on a sewer system and utilized the high-throughput sequencing technology to characterize the microbial communities. Additionally, community assembly analysis was performed to understand the early-stage dynamics within the sewer system. The results demonstrated that the overall diversity of microbial communities exhibited fluctuations as the system progressed. The dominant phyla observed were Chloroflexi, Bacteroidetes, Firmicutes, and Proteobacteria, accounting for over 85.4% of the total relative abundances. At the genus level, bacteria associated with fermentation displayed a high relative abundance, particularly during days 75 to 180. A random-forest machine-learning model identified a group of microbes that confirmed the substantial contribution of fermentation. During the process of fermentation, microorganisms predominantly utilized propionate formation as the main pathway for acidogenesis, followed by acetate and butyrate formation. In terms of nitrogen and sulfur cycles, dissimilatory nitrate reduction and assimilatory sulfate reduction played significant roles. Furthermore, stochastic ecological processes had a dominant effect during the experiment. Dispersal limitation primarily governed the assembly process almost the entire experimental period, indicating the strong adaptability and metabolic plasticity of microorganisms in response to environmental variations. This experiment provides valuable insights into the metabolic mechanisms and microbial assembly associated with sewer systems.

Can adverse childhood experiences predict chronic health conditions? Development of trauma-informed, explainable machine learning models.

Decades of research have established the association between adverse childhood experiences (ACEs) and adult onset of chronic diseases, influenced by health behaviors and social determinants of health (SDoH). Machine Learning (ML) is a powerful tool for computing these complex associations and accurately predicting chronic health conditions. Using the 2021 Behavioral Risk Factor Surveillance Survey, we developed several ML models-random forest, logistic regression, support vector machine, Naïve Bayes, and K-Nearest Neighbor-over data from a sample of 52,268 respondents. We predicted 13 chronic health conditions based on ACE history, health behaviors, SDoH, and demographics. We further assessed each variable's importance in outcome prediction for model interpretability. We evaluated model performance via the Area Under the Curve (AUC) score. With the inclusion of data on ACEs, our models outperformed or demonstrated similar accuracies to existing models in the literature that used SDoH to predict health outcomes. The most accurate models predicted diabetes, pulmonary diseases, and heart attacks. The random forest model was the most effective for diabetes (AUC = 0.784) and heart attacks (AUC = 0.732), and the logistic regression model most accurately predicted pulmonary diseases (AUC = 0.753). The strongest predictors across models were age, ever monitored blood sugar or blood pressure, count of the monitoring behaviors for blood sugar or blood pressure, BMI, time of last cholesterol check, employment status, income, count of vaccines received, health insurance status, and total ACEs. A cumulative measure of ACEs was a stronger predictor than individual ACEs. Our models can provide an interpretable, trauma-informed framework to identify and intervene with at-risk individuals early to prevent chronic health conditions and address their inequalities in the U.S.

Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks

Social interactions are essential for well-being. Therefore, researchers increasingly attempt to capture an individual's social context to predict well-being, including mood. Different tools are used to measure various aspects of the social context. Digital phenotyping is a commonly used technology to assess a person's social behavior objectively. The experience sampling method (ESM) can capture the subjective perception of specific interactions. Lastly, egocentric networks are often used to measure specific relationship characteristics. These different methods capture different aspects of the social context over different time scales that are related to well-being, and combining them may be necessary to improve the prediction of well-being. Yet, they have rarely been combined in previous research. To address this gap, our study investigates the predictive accuracy of mood based on the social context. We collected intensive within-person data from multiple passive and self-report sources over a 28–day period in a student sample (Participants: N = 11, ESM measures: N = 1313). We trained individualized random forest machine learning models, using different predictors included in each model summarized over different time scales. Our findings revealed that even when combining social interactions data using different methods, predictive accuracy of mood remained low. The average coefficient of determination over all participants was 0.06 for positive and negative affect and ranged from − 0.08 to 0.3, indicating a large amount of variance across people. Furthermore, the optimal set of predictors varied across participants; however, predicting mood using all predictors generally yielded the best predictions. While combining different predictors improved predictive accuracy of mood for most participants, our study highlights the need for further work using larger and more diverse samples to enhance the clinical utility of these predictive modeling approaches.

Using machine learning to identify indicators of rare earth element enrichment in sedimentary strata with applications for metal prospectivity

Rare earth elements (REE), classified as critical minerals which are crucial for clean energy technologies, face soaring demand. While economic deposits are found in limited geologic environments including carbonatites and ion-adsorption clays, unconventional, secondary sources such as those from sedimentary basins could hold potential to meet this increased demand. Coal and its associated combustion by-products, phosphorites, oil sands tailings, and formation waters have all garnered interest for REE recovery, yet they remain significantly underexplored. Accordingly, new tools for data analysis and optimization such as machine learning can assist in mineral prospectivity, with these tools being subject to rapid proliferation in the Earth sciences.This work leverages compositional data analysis principles and machine learning to probe geochemical relationships and predict REE abundances in sedimentary lithologies using unsupervised (correlation, principal component, and cluster analysis) and supervised (regression, support vector machine, random forest, and boosting) machine learning models. These three unsupervised models display similar results, with REE typically being associated with incompatible elements (e.g., Th, Nb, and Hf). Gradient boosting, Adaboost, and Random Forest had the highest performance for predicting REE concentrations, with Th and P commonly being the most important predictor variables. Identifying geochemical indicators of REE enrichment that may be used to assist in discovering potentially exploitable REE resources based on existing data, as well as increasing the understanding of metal behaviour in sedimentary systems, is a step forward in understanding novel secondary and unconventional REE sources. Although REE concentrations from these sources are generally lower than primary ore deposits, the amount of available feedstock, potentially simpler, cheaper, and less environmentally taxing extraction processes, and the added benefit of remediating waste streams and contributing to the circular economy make these sources alluring.

The rising human footprint in the Tibetan Plateau threatens the effectiveness of ecological restoration on vegetation growth

Ecological restoration projects in the Tibetan Plateau aimed to reverse ecosystem degradation and safeguard the fragile alpine ecological environment. However, it is still being determined if the vegetation restoration is successful on a large scale or reaches the expected magnitude, restricting our ability to adapt practices to maximise the benefit. With multiple vegetation indices (VIs: NDVI, LAI, and GPP) from satellite observations and random forest machine-learning models, we performed an attribution study on vegetation growth trends caused by climate change and human activities. Then, we further explored the relationship between vegetation growth and ecological projects and human footprint without the influence of climate. The results showed that climatic change was a relatively strong driver of vegetation growth. The positive contributions of ecological restoration occurred only in half of the plateau due to the increased human footprint. Vegetation enhancement resulting from ecological restoration occurred in 13.1%–23.1% of the plateau. Among these values, ecological restoration counteracted the negative climate effects in 4.7%–8.3% of the plateau (about half of the negative climate effect area). In forest and grassland protection areas, the ecological restoration was more successful. The study provides a better understanding of the role of ecological projects in vegetation restoration and potential threats to its effectiveness. This is essential to improve future restoration projects.

Identification of immune microenvironment subtypes and clinical risk biomarkers for osteoarthritis based on a machine learning model.

Osteoarthritis (OA) is a degenerative disease with a high incidence worldwide. Most affected patients do not exhibit obvious discomfort symptoms or imaging findings until OA progresses, leading to irreversible destruction of articular cartilage and bone. Therefore, developing new diagnostic biomarkers that can reflect articular cartilage injury is crucial for the early diagnosis of OA. This study aims to explore biomarkers related to the immune microenvironment of OA, providing a new research direction for the early diagnosis and identification of risk factors for OA. We screened and downloaded relevant data from the Gene Expression Omnibus (GEO) database, and the immune microenvironment-related genes (Imr-DEGs) were identified using the ImmPort data set by combining weighted coexpression analysis (WGCNA). Functional enrichment of GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to explore the correlation of Imr-DEGs. A random forest machine learning model was constructed to analyze the characteristic genes of OA, and the diagnostic significance was determined by the Receiver Operating Characteristic Curve (ROC) curve, with external datasets used to verify the diagnostic ability. Different immune subtypes of OA were identified by unsupervised clustering, and the function of these subtypes was analyzed by gene set enrichment analysis (GSVA). The Drug-Gene Interaction Database was used to explore the relationship between characteristic genes and drugs. Single sample gene set enrichment analysis (ssGSEA) revealed that 16 of 28 immune cell subsets in the dataset significantly differed between OA and normal groups. There were 26 Imr-DEGs identified by WGCNA, showing that functional enrichment was related to immune response. Using the random forest machine learning model algorithm, nine characteristic genes were obtained: BLNK (AUC = 0.809), CCL18 (AUC = 0.692), CD74 (AUC = 0.794), CSF1R (AUC = 0.835), RAC2 (AUC = 0.792), INSR (AUC = 0.765), IL11 (AUC = 0.662), IL18 (AUC = 0.699), and TLR7 (AUC = 0.807). A nomogram was constructed to predict the occurrence and development of OA, and the calibration curve confirmed the accuracy of these 9 genes in OA diagnosis. This study identified characteristic genes related to the immune microenvironment in OA, providing new insight into the risk factors of OA.

Using machine learning for the optimisation of operations and management in electric systems and networks

This research employs the Random Forest Machine Learning model to predict electricity consumption and detect anomalies in electrical networks. Addressing the energy sector’s challenges, such as supply reliability and renewable energy integration, this model processes historical electricity consumption data, weather conditions, and network events to efficiently forecast demand and identify anomalies. Data cleansing and normalisation preceded the training phase, where the model was fine-tuned using historical data to balance forecast accuracy and overfitting avoidance. The dataset was divided into training (80%) and testing (20%) sets for performance evaluation. Through cross-validation, optimal model hyperparameters were determined. The findings highlight the model’s efficacy in accurately predicting daily electricity consumption in a small, homogenous town. The model achieved a Mean Absolute Error (MAE) of 198.73 MWh and a coefficient of determination (R²) of 0.9387. Temperature, humidity, and wind speed were identified as key influencing factors on consumption levels. Conclusively, the Random Forest model presents a valuable tool for energy management, offering precise consumption forecasting and anomaly detection capabilities. Future work will address computational demands and enhance model integration with other Machine Learning methods for improved performance. This contribution is significant for efficient energy system planning and operation.

Metabolic Heterogeneity of Tumor Cells and its Impact on Colon Cancer Metastasis: Insights from Single-Cell and Bulk Transcriptome Analyses.

Background: Metabolic reprogramming plays a crucial role in the development of colorectal cancer (CRC), influencing tumor heterogeneity, the tumor microenvironment, and metastasis. While the interaction between metabolism and CRC is critical for developing personalized treatments, gaps remain in understanding how tumor cell metabolism affects prognosis. Our study introduces novel insights by integrating single-cell and bulk transcriptome analyses to explore the metabolic landscape within CRC cells and its mechanisms influencing disease progression. This approach allows us to uncover metabolic heterogeneity and identify specific metabolic genes impacting metastasis, which have not been thoroughly examined in previous studies. Methods: We sourced microarray and single-cell RNA sequencing datasets from the Gene Expression Omnibus (GEO) and bulk sequencing data for CRC from The Cancer Genome Atlas (TCGA). We employed Gene Set Variation Analysis (GSVA) to assess metabolic pathway activity, consensus clustering to identify CRC-specific transcriptome subtypes in bulkseq, and rigorous quality controls, including the exclusion of cells with high mitochondrial gene expression in scRNA seq. Advanced analyses such as AUCcell, infercnvCNV, Non-negative Matrix Factorization (NMF), and CytoTRACE were utilized to dissect the cellular landscape and evaluate pathway activities and tumor cell stemness. The hdWGCNA algorithm helped identify prognosis-related hub genes, integrating these findings using a random forest machine learning model. Results: Kaplan-Meier survival curves identified 21 significant metabolic pathways linked to prognosis, with consensus clustering defining three CRC subtypes (C3, C2, C1) based on metabolic activity, which correlated with distinct clinical outcomes. The metabolic activity of the 13 cell subpopulations, particularly the epithelial cell subpopulation with active metabolic levels, was evaluated using AUCcell in scRNA seq. To further analyze tumor cells using infercnv, NMF disaggregated these cells into 10 cellular subpopulations. Among these, the C2 subpopulation exhibited higher stemness and tended to have a poorer prognosis compared to C6 and C0. Conversely, the C8, C3, and C1 subpopulations demonstrated a higher level of the five metabolic pathways, and the C3 and C8 subpopulations tended to have a more favorable prognosis. hdWGCNA identified 20 modules, from which we selected modules primarily expressed in high metabolic tumor subgroups and highly correlated with clinical information, including blue and cyan. By applying variable downscaling of RF to a total of 50 hub genes, seven gene signatures were obtained. Furthermore, molecules that were validated to be protective in GEO were screened alongside related molecules, resulting in the identification of prognostically relevant molecules such as UQCRFS1 and GRSF1. Additionally, the expression of GRSF1 was examined in colon cancer cell lines using qPCR and phenotypically verified by in vitro experiments. Conclusion: Our findings emphasize that high activity in specific metabolic pathways, including pyruvate metabolism and the tricarboxylic acid cycle, correlates with improved colon cancer outcomes, presenting new avenues for metabolic-based therapies. The identification of hub genes like GRSF1 and UQCRFS1 and their link to favorable metabolic profiles offers novel insights into tumor neovascularization and metastasis, with significant clinical implications for targeting metabolic pathways in CRC therapy.