Clustering Method Research Articles

Research on Automatic Alignment for Corn Harvesting Based on Euclidean Clustering and K-Means Clustering

Aiming to meet the growing need for automated harvesting, an automatic alignment method based on Euclidean clustering and K-means clustering is proposed to address issues of driver fatigue and inaccurate driving in manually operated corn harvesters. Initially, the corn field environment is scanned using LiDAR to obtain point cloud data, which are then subjected to pass-through filtering and statistical filtering to remove noise and non-corn contour points. Subsequently, Euclidean clustering and K-means clustering methods are applied to the filtered point cloud data. To validate the impact of Euclidean clustering on subsequent clustering, two separate treatments of the obtained point cloud data were conducted during experimental validation: the first used the K-means clustering algorithm directly, while the second involved performing Euclidean clustering followed by K-means clustering. The results demonstrate that the combined method of Euclidean clustering and K-means clustering achieved a success rate of 81.5%, representing a 26.5% improvement over traditional K-means clustering. Additionally, the Rand index increased by 0.575, while accuracy improved by 57% and recall increased by 61%.

Fine construction of gene coexpression network analysis using GTOM and RECODE detected a critical module of neuroblastoma stages 4 and 4S.

Stage 4 neuroblastoma (NBL), a solid tumor of childhood, has a poor prognosis. Despite intensive molecular genetic studies, no targetable gene abnormalities have been identified. Stage 4S NBL has a characteristic of spontaneous regression, and elucidation of the mechanistic differences between stages 4 and 4S may improve treatment. Conventional NBL studies have mainly focused on the detection of abnormalities in individual genes and have rarely examined abnormalities in gene networks. While the gene coexpression network is expected to contribute to the detection of network abnormalities, the fragility of the network due to data noise and the extraction of arbitrary topological structures for the high-dimensional network are issues. The present paper concerns the classification method of stages 4 and 4S NBL patients using highly accurate gene coexpression network analysis based on RNA-sequencing data of transcription factors (TFs). In particular, after applying a noise reduction method RECODE, generalized topological overlapping measure (GTOM), which weighs the connections of nodes in the network structure, succeeded in extracting a cluster of TFs that showed high classification performance for stages 4 and 4S. In addition, we investigated how these clusters correspond to clinical information and to TFs which control the normal adrenal tissue and NBL characters. A clustering method is presented for finding intermediate-scale clusters of TFs that give considerable separation performance for distinguishing between stages 4 and 4S. It is suggested that this method is useful as a way to extract factors that contribute to the separation of groups from multiple pieces of information such as gene expression levels.

STI or DUI modes of innovation: Evidence from comprehensive evaluation of four cities in China

The Science and Technology Innovation Center holds a pivotal position in the national science and technology innovation system, and a scientific evaluation of the “Sci-tech Innovation Center” will guide its construction direction. This study found the advantages and disadvantages of the four cities through comparison; Hence improvement suggestions were proposed for the weaknesses of the four cities. There are two main paths for the government to drive technology innovation: STI (Science and Technology Innovation) mode and DUI (Doing, Using, Interacting) mode. With the aid of the evaluation index system of the Sci-tech Innovation Center, this article uses fuzzy sets, rough sets and fuzzy dynamic clustering methods to comprehensively evaluate the effects of driving technology innovation in the four cities of Beijing, Shanghai, Shenzhen and Guangzhou. The results found that Shenzhen has a significant effect in DUI, and Beijing has a significant effect in STI. The choice of path is related to the abundance of innovation resources.

Nash Bargaining-Based Coordinated Frequency-Constrained Dispatch for Distribution Networks and Microgrids

As the penetration of distributed renewable energy continues to increase in distribution networks, the traditional scheduling model that the inertia and primary frequency support of distribution networks are completely dependent on the transmission grid will place enormous regulatory pressure on the transmission grid and hinder the active regulation capabilities of distribution networks. To address this issue, this paper proposes a coordinated optimization method for distribution networks and microgrid clusters considering frequency constraints. First, the confidence interval of disturbances was determined based on historical forecast deviation data. On this basis, a second-order cone programming model for distribution networks with embedded frequency security constraints was established. Then, microgrids performed economic dispatch considering the reserves requirement to provide inertia and primary frequency support, and a stochastic optimization model with conditional value-at-risk was built to address uncertainties. Finally, a cooperative game between the distribution network and microgrid clusters was established, determining the reserve capacity provided by each microgrid and the corresponding prices through Nash bargaining. The model was further transformed into two sub-problems, which were solved in a distributed manner using the ADMM algorithm. The effectiveness of the proposed method in enhancing the operational security and economic efficiency of the distribution networks is validated through simulation analysis.

Quantum Chemistry Study on Cl-Initiated Reactions of 2-Chloropropane and 2-Methylpropanoyl Halogen (Cl, Br, F): Mechanism, Kinetics, and Atmospheric Implications.

Halogenated volatile organic compounds (HVOCs) pose significant bioaccumulative and toxicological risks, necessitating effective strategies for their removal. Here, we show, through a computational study employing density functional theory and coupled cluster methods, the detailed mechanism and kinetic properties of Cl-initiated degradation reactions of 2-chloropropane (2-CP, (CH3)2CHCl) and 2-methylpropanoyl halide ((CH3)2CHCOX, X = Cl, Br, F). The reaction rate constants of all the channels were calculated by the canonical variational transition state theory (CVT) with the correction of the small curvature tunneling effect (SCT) at 200-1000 K. The subsequent transformation pathways of the major radical products of (CH3)2CHCl and (CH3)2CHCOCl in the presence of O2, NO, and HO2 radical were investigated. The results elucidate the reaction pathways and rate constants, which are in excellent agreement with the experimental data at 296 K. We further explore the atmospheric implications of these reactions by assessing the atmospheric lifetime (τ) and ozone depletion potential (ODP). Additionally, we delve into the aquatic toxicity and bioaccumulation potential of the reactants and their transformation products. This study not only advances our knowledge of the atmospheric fate of halogenated hydrocarbons but also underscores the importance of considering the environmental and toxicological impacts in the development of HVOC mitigation strategies.

Abstract 4141460: Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements

Introduction: Neonatal hypothermia (< 36.5 o C) after CPB is considered benign despite lack of evidence on its prognostic significance. Question: Are group-based trajectory modeling (GBTM), k-means, and self-organizing map (SOM) clustering approaches clinically useful for evaluating their associations with important outcomes? Aims: Identify distinct postoperative temperature trajectories in neonates after CPB using novel machine learning (ML) clustering methods, corroborate findings, and evaluate prognostic value on outcomes. Methods: Secondary cohort analysis of prospectively collected data. Accessed data from a single pediatric referral center's CardioAccess registry consistent of postoperative neonates with congenital heart defects (CHD). 48-hourly postoperative temperature measurements were extracted from patient records. GBTM, SOM, and k-means clustering identified cluster membership and model fit was optimized for 3 clusters based on existing knowledge. The primary outcome was a complication composite of severe bleeding, illness severity, cardiac arrest, arrhythmia, delayed first successful extubation, prolonged ICU LOS, very poor weight gain, and 30-day mortality. 3 data driven techniques were compared and then associated with outcomes using adjusted multivariable binary logistic regression (LR). Results: 450 neonates ≥ 34 weeks gestation undergoing CPB between 2015 and 2019 met inclusion criteria. GBTM, SOM, and k-means clustering identified temperature group assignment for 3 clusters: 1) persistent hypothermia (n=38, 9% vs. n=49, 11% vs. n=40, 9%), 2) resolving hypothermia (n=233, 51% vs. n=227, 50% vs. n=147, 33%), and 3) normothermia (n=179, 40% vs. n=174, 39% vs. n=263, 58%). Concordance of techniques showed strong agreement between GBTM and SOM (kappa= 0.92). The kappa test comparing GBTM and k-means assignment was 0.41, indicating a weak to moderate agreement. After adjustment in the multivariable binary LR, persistently hypothermic neonates compared to normothermic neonates were associated with higher odds of the complication composite outcome in the GBTM (OR: 2.8 [1.0-7.3], p 0.041) and SOM (OR: 2.3 [1.0-5.4], p 0.045) models, but not the k-means model (OR: 1.4 [0.7-3.1], p 0.38). Conclusion: Exploring concordance between different unsupervised ML techniques, shows that neonatal temperature after CPB follows distinct postoperative trajectories, and those exhibiting a persistent hypothermia trend are at higher risk of adverse outcomes.

M-CLUSTER: multistage clustering for unsupervised train wheel condition monitoring

Wheel out-of-roundness (OOR) is a common wheel defect that raises maintenance costs and increases the risk of failure or damage to track components. This paper proposes a novel multistage clustering framework (M-CLUSTER) for unsupervised condition monitoring of train wheels. The framework initially extracts time-domain features from raw acceleration responses collected by rail-mounted sensors. Sensitive features are then selected through an unsupervised feature selection algorithm called local learning-based clustering (LLC). Next, a detector model is trained using density-based spatial clustering of applications with noise (DBSCAN), a data clustering method effective for clusters with similar density. Since this algorithm does not originally involve separate training and testing phases, a new two-step mechanism is introduced: (1) training on a healthy dataset and (2) testing on an unlabelled dataset. Finally, the severity of train wheel defects is classified by K-means, with cluster validity indices (CVI) automatically determining the number of severity clusters (classes). The framework’s efficiency is demonstrated through the detection of defective wheels using the Alfa Pendular passenger model. Results indicate that M-CLUSTER accurately identifies train wheel flats and polygonal wear without labelled data, achieving 98% accuracy by selecting 10 features from the set.

Analysis of behavioral flow resolves latent phenotypes.

The accurate detection and quantification of rodent behavior forms a cornerstone of basic biomedical research. Current data-driven approaches, which segment free exploratory behavior into clusters, suffer from low statistical power due to multiple testing, exhibit poor transferability across experiments and fail to exploit the rich behavioral profiles of individual animals. Here we introduce a pipeline to capture each animal's behavioral flow, yielding a single metric based on all observed transitions between clusters. By stabilizing these clusters through machine learning, we ensure data transferability, while dimensionality reduction techniques facilitate detailed analysis of individual animals. We provide a large dataset of 771 behavior recordings of freely moving mice-including stress exposures, pharmacological and brain circuit interventions-to identify hidden treatment effects, reveal subtle variations on the level of individual animals and detect brain processes underlying specific interventions. Our pipeline, compatible with popular clustering methods, substantially enhances statistical power and enables predictions of an animal's future behavior.

An "ultimate" coupled cluster method based entirely on T2.

Electronic structure methods built around double-electron excitations have a rich history in quantum chemistry. However, it seems to be the case that such methods are only suitable in particular situations and are not naturally equipped to simultaneously handle the variety of electron correlations that might be present in chemical systems. To this end, the current work seeks a computationally efficient, low-rank, "ultimate" coupled cluster method based exclusively on T2 and its products that can effectively emulate more "complete" methods that explicitly consider higher-rank, T2m, operators. We introduce a hierarchy of methods designed to systematically account for higher, even order cluster operators, such as T4, T6, …, T2m, by invoking tenets of the factorization theorem of many-body perturbation theory (MBPT) and expectation-value coupled cluster theory. It is shown that each member within this methodological hierarchy is defined such that both the wavefunction and energy are correct through some order in MBPT and can be extended up to arbitrarily high orders in T2. The efficacy of such approximations are determined by studying the potential energy surface of several closed and open-shell molecules. We find that the proposed hierarchy of augmented T2 methods essentially reduces to standard CCD for problems where dynamic electron correlations dominate but offer improvements in situations where non-dynamic and static correlations become relevant. A notable highlight of this work is that the cheapest methods in this hierarchy-which are correct through fifth-order in MBPT-consistently emulate the behavior of the O(N10) CCDQ method, yet only require a O(N6) algorithm by virtue of factorized intermediates.

The profound influence of the North Atlantic Ocean on Northeast Asia: A comprehensive multi‐model study

AbstractWe assess the effects of the North Atlantic Sea surface temperature multidecadal variability on Northeast Asia using a set of sensitivity experiments (with a total of 530 ensemble members). We show that a warming of the North Atlantic Ocean leads to a strong and robust increase in temperature over Northeast Asia, which is replicated by a large majority of ensemble members. We show that the effect of the North Atlantic on Northeast Asia is model and season‐dependent. We focus on two seasons, for which response to the North Atlantic Ocean is the most robust (autumn) and the less robust (spring) as indicated by the number of models that simulate a statistically significant change in surface air temperature. We use a clustering method to identify the sources of differences between models in simulating the effects of warming in the North Atlantic. We find that the primary mechanism linking the North Atlantic to Northeast Asia is a perturbation of the circumglobal teleconnection pattern (i.e., of the upper tropospheric atmospheric circulation), which allows modulation of the near‐surface atmospheric circulation and an increase in temperature over East Asia. A second mechanism is related to the influence of the North Atlantic on the Pacific Ocean and the resulting effects on atmospheric circulation over Northeast Asia.

Implementation of K-Means Clustering in Mapping Teacher Distribution Using Geographic Information System

The placement of teachers in Indonesia has not been evenly distributed across several regions due to inaccurate recruitment and placement processes. The quality of education, particularly in rural areas, is negatively impacted by this uneven distribution. Teachers play a crucial role in enhancing education, making it essential to address this issue. This study seeks to equilibrate the allocation of teachers in Langsa City using the K-Means Clustering method based on the number of teachers, students, and study groups at the Madrasah Ibtidaiyah, Madrasah Tsanawiyah, and Madrasah Aliyah levels. The clustering results are then mapped using the Quantum Geographic Information System. The study identifies 20 schools with a shortage of teachers, 7 schools with sufficient teachers, and 3 schools with a surplus. The utilization of the K-Means Clustering method demonstrated a high accuracy rate of 92.8%. The implications of these findings suggest that educational authorities can use the clustering results to strategically address teacher shortages by reallocating teaching resources more effectively, thus potentially improving educational outcomes in underserved areas. Moreover, the GIS mapping offers a practical tool for ongoing monitoring and decision-making regarding teacher distribution.

Optimization of bus stop layout considering multiple factors including passenger flow direction.

Bus stop layout typically requires consideration of urban population distribution, traffic conditions, and passenger flow demand to establish an efficient foundation for the bus system's operation. Based on the above key factors, this paper introduces a strategic method to optimize the bus stop layout from a macro perspective in order to save passengers' travel time and improve the attractiveness of the bus system. This approach accounts for the matching degree between the Origin-Destination (OD) direction of passengers and their walking direction heading to bus stops. Initially, we take into account factors such as the population and area of traffic districts, and urban road conditions. Utilizing the hypernetwork multidimensional data clustering method along with GIS technology, we construct an alternative set of bus stops based on the hypernetwork framework. This set serves as a reference for the positioning of newly built and moved bus stops. Subsequently, we develop a two-stage model for bus stop layout decision-making. The first stage focuses on determining the bus stop layout at the traffic district level, taking into account multi-factors including the passenger flow matching degree. The second stage is designed to mitigate the negative impact of bus stop optimization on the overall service level of the urban bus system. A case study conducted in XT city demonstrates the effectiveness of our approach. Post-optimization, there is a 15.83% increase in the alignment between passenger flow direction and bus stop layout. Additionally, the average travel time for passengers is reduced by 7.55 minutes.

Implementation of the Web-Based K-Means Clustering Algorithm on Hypertension Levels in the Elderly at the Bungah District Health Center

This research was carried out at the community health center in Bungah sub - district with the aim that so that patients receive appropriate treatment, a web-based system is needed to group hypertension data into several classes (clusters). The method used in data grouping is the K-Means Clustering method . In this study, 100 hypertension data were grouped into 4 clusters . The hypertension data used consists of two attributes, namely systole and diastole, the working mechanism is to normalize the data first, then the system groups the data into groups that have the same characteristics. The system will display the results of the clustering process which consists of four (4) clusters , namely cluster 1 Isolated Systolic Hypertension, cluster 2 Grade 1 (mild hypertension), cluster 3 Grade 2 (moderate hypertension), and cluster 4 Grade 3 (high hypertension). The data used for clustering is 100 data from blood pressure checks of patients at the Community Health Center using a blood pressure monitor. The results of the last iteration of 100 hypertension data in the system were used as parameters for calculating the level of effectiveness, by comparing the data from the clustering test results to the data from diagnosis a which produced an effectiveness value of 80%.

Novel insights of disulfidptosis-mediated immune microenvironment regulation in atherosclerosis based on bioinformatics analyses

Atherosclerosis (AS) is the leading cause of coronary heart disease, which is the primary cause of death worldwide. Recent studies have identified disulfidptosis as a new type of cell death that may be involved in onset and development of many diseases. However, the role of disulfidptosis in AS is not clear. In this study, bioinformatics analysis and experiments in vivo and in vitro were performed to evaluate the potential relationship between disulfidptosis and AS. AS-related sequencing data were obtained from the Gene Expression Omnibus (GEO). Bioinformatics techniques were used to evaluate differentially expressed genes (DEGs) associated with disulfidptosis-related AS. Hub genes were screened using least absolute shrinkage and selection operator (LASSO) and random forests (RF) methods. In addition, we established a foam cell model in vitro and an AS mouse model in vivo to verify the expressions of hub genes. In addition, we constructed a diagnostic nomogram with hub genes to predict progression of AS. Finally, the consensus clustering method was used to establish two different subtypes, and associations between subtypes and immunity were explored. As the results, 9 disulfidptosis-related AS DEGs were identified from GSE28829 and GSE43292 datasets. Evaluation of DEGs using LASSO and RF methods resulted in identification of 4 hub genes (CAPZB, DSTN, MYL6, PDLIM1), which were analyzed for diagnostic value using ROC curve analysis and verified in vitro and in vivo. Furthermore, a nomogram including hub genes was established that accurately predicted the occurrence of AS. The consensus clustering algorithm was used to separate patients with early atherosclerotic plaques and patients with advanced atherosclerotic plaques into two disulfidptosis subtypes. Cluster B displayed higher levels of infiltrating immune cells, which indicated that patients in cluster B may have a positive immune response for progression of AS. In summary, disulfidptosis-related genes including CAPZB, DSTN, MYL6, and PDLIM1 may be diagnostic markers and therapeutic targets for AS. In addition, these genes are closely related to immune cells, which may inform immunotherapy for AS.

The critical role of c and φ in ensuring stability: A study on rockfill dams

Abstract Slope stability analysis is an important part of rockfill dam design, and the uncertainty of rock and soil physical and mechanical parameters has a significant impact on slope stability. In this article, based on physical and mechanical parameters c and φ, simplified Bishop’s method and mean clustering method are adopted to study the influence of parameter cohesion c and internal friction Angle φ on slope safety factor k of composite geomembrane rockfill dam, and the key and non-critical areas of the dam are preliminarily differentiated according to the different influences of the changes in different sections of the dam body on slope safety factor c and φ. The research results show that whether c and φ change ±5 and ±10% simultaneously with single parameter or double parameter, it shows that cohesion force c has little influence on slope stability, while internal friction Angle φ is the most sensitive factor in slope stability calculation, and its numerical accuracy has a great influence on the calculation result of safety factor. In addition, the influence of c and φ in key areas on the stability of the dam body is more significant. Therefore, in the construction of a composite geofilm rockfill dam, a relatively accurate φ value is required when selecting parameters, especially in key areas. This study not only has a certain guiding significance for the property requirements of the selected soil, engineering safety, and engineering optimization design, but also puts forward some new methods and ideas for optimizing the design scheme and improving the safety of the dam.

Evaluation Method of Water Injection Development Effects in Fractured Vuggy Carbonate Reservoirs: Case Study of FI7 Strike-Slip Fault Zone

As the main method of secondary development, water injection has been widely used in fractured vuggy carbonate reservoirs. By using an evaluation index system combined with an integrated approach to objectively evaluate and support targeted and operational adjustments of water injection development effects in fractured vuggy carbonate reservoirs, a comprehensive evaluation method is established in this study. The CRITIC method is used as the main approach, and the analytic hierarchy process, the entropy weight method, and the coefficient of variation method are used as sub-methods. Additionally, the clustering centres are divided using the clustering method to reduce the error caused by the irrational distribution of the relevant evaluation index data. The method is used to evaluate and analyse the effects of water injection development, using the FI7 fault zone of the Halahatang oilfield as an example. The application results show that the evaluation method is feasible and effective for cases with small data volume, fewer computational resources, and less time. This study has a certain reference significance for the evaluation of the effect of water injection development in similar fractured vuggy reservoirs.

Screening of biomarkers in acute radiation enteritis based on microbiome and clustering methods

BackgroundRadiation enteritis (RE) is a common complication of radiotherapy for abdominal and pelvic tumors, adversely affecting treatment outcomes and patients’ quality of life. Gut microbiome alterations may contribute to RE development, but the underlying pathogenic factors are not fully understood. This study aimed to characterize the intestinal microbial changes associated with RE and severe acute radiation enteritis (SARE) and to identify predictive biomarkers.MethodsWe enrolled 50 cervical cancer patients undergoing radiotherapy and 15 healthy women (controls). Stool samples were collected at the baseline and during weeks 2, 4, and 6 of radiotherapy, and then analyzed using 16 S rDNA sequencing and bioinformatics.ResultsAlthough the Bacteroidetes/Firmicutes (B/F) ratio was higher in patients with RE or SARE, it alone could not predict these conditions. Three enterotypes were identified based on dominant genera: Blautia (enterotype 1), Escherichia-Shigella (enterotype 2), and Faecalibacterium (enterotype 3). A decrease in Blautia and an increase in Escherichia-Shigella and Faecalibacterium were correlated with RE and SARE. Univariate logistic regression revealed that the Faecalibacterium enterotype at the baseline was associated with a 4.4-fold higher risk of developing SARE (odds ratio 5.400; P = 0.017). The Escherichia-Shigella enterotype was also linked to increased SARE incidence.ConclusionThese findings suggest that while single bacterial genera or the B/F ratio are insufficient predictors, enterotype classification may serve as a potential biomarker for predicting SARE in patients undergoing radiotherapy.

Representing and Quantifying Conformational Changes of Kinases and Phosphatases Using the TSR-Based Algorithm

Protein kinases and phosphatases are key signaling proteins and are important drug targets. An explosion in the number of publicly available 3D structures of proteins has been seen in recent years. Three-dimensional structures of kinase and phosphatase have not been systematically investigated. This is due to the difficulty of designing structure-based descriptors that are capable of quantifying conformational changes. We have developed a triangular spatial relationship (TSR)-based algorithm that enables a unique representation of a protein’s 3D structure using a vector of integers (keys). The main objective of this study is to provide structural insight into conformational changes. We also aim to link TSR-based structural descriptors to their functions. The 3D structures of 2527 kinases and 505 phosphatases are studied. This study results in several major findings as follows: (i) The clustering method yields functionally coherent clusters of kinase and phosphatase families and their superfamilies. (ii) Specific TSR keys are identified as structural signatures for different types of kinases and phosphatases. (iii) TSR keys can identify different conformations of the well-known DFG motif of kinases. (iv) A significant number of phosphatases have their own distinct DFG motifs. The TSR keys from kinases and phosphatases agree with each other. TSR keys are successfully used to represent and quantify conformational changes of CDK2 upon the binding of cyclin or phosphorylation. TSR keys are effective when used as features for unsupervised machine learning and for key searches. If discriminative TSR keys are identified, they can be mapped back to atomic details within the amino acids involved. In conclusion, this study presents an advanced computational methodology with significant advantages in not only representing and quantifying conformational changes of protein structures but also having the capability of directly linking protein structures to their functions.

Iris Recognition System Using Advanced Segmentation Techniques and Fuzzy Clustering Methods for Robotic Control

The idea of developing a robot controlled by iris movement to assist physically disabled individuals is, indeed, innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact with their environment. Disability of movement has a huge impact on the lives of physically disabled people. Therefore, there is need to develop a robot that can be controlled using iris movement. The main idea of this work revolves around iris recognition from an eye image, specifically identifying the centroid of the iris. The centroid’s position is then utilized to issue commands to control the robot. This innovative approach leverages iris movement as a means of communication and control, offering a potential breakthrough in assisting individuals with physical disabilities. The proposed method aims to improve the precision and effectiveness of iris recognition by incorporating advanced segmentation techniques and fuzzy clustering methods. Fast gradient filters using a fuzzy inference system (FIS) are employed to separate the iris from its surroundings. Then, the bald eagle search (BES) algorithm is employed to locate and isolate the iris region. Subsequently, the fuzzy KNN algorithm is applied for the matching process. This combined methodology aims to improve the overall performance of iris recognition systems by leveraging advanced segmentation, search, and classification techniques. The results of the proposed model are validated using the true success rate (TSR) and compared to those of other existing models. These results highlight the effectiveness of the proposed method for the 400 tested images representing 40 people.

M6A-related genes and their role in Parkinson's disease: Insights from machine learning and consensus clustering.

Parkinson disease (PD) is a chronic neurological disorder primarily characterized by a deficiency of dopamine in the brain. In recent years, numerous studies have highlighted the substantial influence of RNA N6-methyladenosine (m6A) regulators on various biological processes. Nevertheless, the specific contribution of m6A-related genes to the development and progression of PD remains uncertain. In this study, we performed a differential analysis of the GSE8397 dataset in the Gene Expression Omnibus database and selected important m6A-related genes. Candidate m6A-related genes were then screened using a random forest model to predict the risk of PD. A nomogram model was built based on the candidate m6A-related genes. By employing a consensus clustering method, PD was divided into different m6A clusters based on the selected significant m6A-related genes. Finally, we performed immune cell infiltration analysis to explore the immune infiltration between different clusters. We performed a differential analysis of the GSE8397 dataset in the Gene Expression Omnibus database and selected 11 important m6A-related genes. Four candidate m6A-related genes (YTH Domain Containing 2, heterogeneous nuclear ribonucleoprotein C, leucine-rich pentatricopeptide repeat motif containing protein and insulin-like growth factor binding protein-3) were then screened using a random forest model to predict the risk of PD. A nomogram model was built based on the 4 candidate m6A-related genes. The decision curve analysis indicated that patients can benefit from the nomogram model. By employing a consensus clustering method, PD was divided into 2 m6A clusters (cluster A and cluster B) based on the selected significant m6A-related genes. The immune cell infiltration analysis revealed that cluster A and cluster B exhibit distinct immune phenotypes. In conclusion, m6A-related genes play a significant role in the development of PD and our study on m6A clustering may potentially guide personalized treatment strategies for PD in the future.