Unsupervised Clustering Techniques Research Articles

Conserved pattern-based classification of human odorant receptor multigene family

Conserved protein-coding sequences are critical for maintaining protein function across species. Odorant receptors (ORs), a large poorly understood multigene family responsible for odor detection, lack comprehensive classification methods that reflect their functional diversity. In this study, we propose a new approach called conserved motif-based classification (CMC) for classifying ORs based on amino acid sequence similarities within conserved motifs. Specifically, we focused on three well-conserved motifs: MAYDRYVAIC in TM3, KAFSTCASH in TM6, and PMLNPFIY in TM7. Using an unsupervised clustering technique, we classified human ORs (hORs) into two main clusters with six sub-clusters. CMC partly reflects previously identified subfamilies, revealing altered residue positions among the sub-clusters. These altered positions interacted with specific residues within or adjacent to the transmembrane domain, suggesting functional implications. Furthermore, we found that the CMC correlated with both ligand responses and ectopic expression patterns, highlighting its relevance to OR function. This conserved motif-based classification will help in understanding the functions and features that are not understood by classification based solely on entire amino acid sequence similarity.

Assessing clustering methods using Shannon's entropy

Unsupervised clustering techniques are crucial for effectively partitioning datasets into meaningful subgroups. In this paper, we introduce a novel clustering fuzziness metric based on Shannon's entropy, which quantifies the level of uncertainty in clustering assignments. This metric is employed to develop a statistical test for evaluating clustering algorithms and to propose parametric corrections to enhance their performance. We provide empirical evidence showing that our approach significantly improves clustering quality compared to well-known algorithms such as Fuzzy K-Means (FKM) and Fanny. For instance, applying our correction method led to a notable decrease in Jensen-Shannon Divergence (JSD) values, indicating enhanced clustering accuracy. Our methodology is demonstrated through comprehensive experiments on both simulated and real-world datasets. Additionally, our approach proves effective in determining the optimal number of clusters, showing superiority over traditional methods. These results highlight the practical benefits of our metric and correction techniques for improving clustering performance.

Structural mechanism of glass transition uncovered by unsupervised machine learning

Uncovering the structural origins of the ubiquitous dynamic arrest phenomenon at the glass transition has long been a challenge due to the difficulty in identifying a rational structural representation from a disordered medium. To address this challenge, we propose a novel approach based on unsupervised learning to define a set of structural fingerprints. In this approach, complex local atomic environments, ranging from short to medium range, are captured by the discretized radial distribution function and projected onto a simple two-dimensional space using a neural network-based autoencoder. This two-dimensional space is characterized by two static structural indicators, P1 and P2, providing a comprehensive and user-friendly representation of the mysterious “glassy structure”. By employing Gaussian mixture modeling, the structural space is autonomously divided into three sections, each representing a unique cluster with similar environments. These indicators not only elucidate the glass transition but also allow for the quantitative prediction of activation barriers for local structural excitations. Furthermore, the unsupervised clustering technique can distinguish between the structural features of “hard zones” and “soft zones”, as well as recently proposed superfast “liquid-like” atoms in glass. This unsupervised machine learning approach demonstrates the utility of seemingly agnostic local structure in amorphous materials, offering insights into the long-sought structural origins of the glass transition.

Machine Learning-Driven Quantification of CO2 Plume Dynamics at Illinois Basin Decatur Project Sites Using Microseismic Data

This study utilizes machine learning to quantify CO2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO2 injection metrics, this work aims to predict the temporal evolution of subsurface CO2 saturation plumes. The findings illustrate that machine learning can predict plume dynamics, revealing vertical clustering of microseismic events over distinct time periods within certain proximities to the injection well, consistent with an invasion percolation model. The buoyant CO2 plume partially trapped within sandstone intervals periodically breaches localized barriers or baffles, which act as leaky seals and impede vertical migration until buoyancy overcomes gravity and capillary forces, leading to breakthroughs along vertical zones of weakness. Between different unsupervised clustering techniques, K-Means and DBSCAN were applied and analyzed in detail, where K-means outperformed DBSCAN in this specific study by indicating the combination of the highest Silhouette Score and the lowest Davies–Bouldin Index. The predictive capability of machine learning models in quantifying CO2 saturation plume extension is significant for real-time monitoring and management of CO2 sequestration sites. The models exhibit high accuracy, validated against physical models and injection data from the IBDP, reinforcing the viability of CO2 geological sequestration as a climate change mitigation strategy and enhancing advanced tools for safe management of these operations.

Mapping climate suitability index for rainfed cultivation of medicinal plants by developing an AI-based probabilistic framework

The Climate Suitability Index (CSI) can increase agricultural efficiency by identifying the high-potential areas for cultivation from the climate perspective. The present study develops a probabilistic framework to calculate CSI for rainfed cultivation of 12 medicinal plants from the climate perspective of precipitation and temperature. Unlike the ongoing frameworks based on expert judgments, this formulation decreases the inherent subjectivity by using two components: frequency analysis and Particle Swarm Optimization (PSO). In the first component, the precipitation and temperature layers were prepared by calculating the occurrence probability for each plant, and the obtained probabilities were spatially interpolated using geographical information system processes. In the second component, PSO quantifies CSI by classifying a study area into clusters using an unsupervised clustering technique. The formulation was implemented in the Lake Urmia basin, which was distressed by unsustainable water resources management. By identifying clusters with higher CSI values for each plant, the results provide deeper insights to optimize cultivation patterns in the basin. These insights can help managers and farmers increase yields, reduce costs, and improve profitability.

Prognostic value of anoikis-related genes revealed using multi-omics analysis and machine learning based on lower-grade glioma features and tumor immune microenvironment

BackgroundThe investigation explores the involvement of anoikis-related genes (ARGs) in lower-grade glioma (LGG), seeking to provide fresh insights into the disease's underlying mechanisms and to identify potential targets for therapy. MethodsWe applied unsupervised clustering techniques to categorize LGG patients into distinct molecular subtypes based on ARGs with prognostic significance. Additionally, various machine learning algorithms were employed to pinpoint genes most strongly correlated with patient outcomes, which were then used to develop and assess risk profiles. ResultsOur analysis identified two distinct molecular subtypes of LGG, each with significantly different prognoses. Patients in Cluster 2 had a median survival of 2.036 years, markedly shorter than the 7.994 years observed in Cluster 1 (P < 0.001). We also constructed a six-gene ARG signature that efficiently classified patients into two risk categories, showing median survival durations of 4.084 years for the high-risk group and 10.304 years for the low-risk group (P < 0.001). Significantly, the immune profiles, tumor mutation characteristics, and drug sensitivity varied greatly among these risk groups. The high-risk group was characterized by a “cold” tumor microenvironment (TME), a lower IDH1 mutation rate (61.7 % vs. 91.4 %), a higher TP53 mutation rate (53.7 % vs. 38.9 %), and greater sensitivity to targeted therapies such as QS11 and PF-562271. Furthermore, our nomogram, integrating risk scores with clinicopathological features, demonstrated strong predictive accuracy for clinical outcomes in LGG patients, with an AUC of 0.903 for the first year. The robustness of this prognostic model was further validated through internal cross-validation and across three external cohorts. ConclusionsThe evidence from our research suggests that ARGs could potentially serve as reliable indicators for evaluating immunotherapy effectiveness and forecasting clinical results in patients with LGG.

Sex differences in the acoustic structure of terrestrial alarm calls in vervet monkeys (Chlorocebus pygerythrus).

The alarm calls of vervet monkeys (Chlorocebus pygerythrus) have been the subject of considerable focus by researchers, owing primarily to the purported referential qualities of different alarm call types. With this focus on reference, acoustic variation among calls elicited by the same range of predators has typically been overlooked. Specifically, at least one type of alarm call-the terrestrial alarm-was described over 50 years ago as being acoustically distinct between males and females-a description that has largely eluded more systematic scrutiny. Here, we provide a quantitative acoustic analysis and comparison of terrestrial alarm calls produced by adult male and female vervet monkeys. We use a random forest model to determine which acoustic variables best distinguish between the calls of males and females, and use an unsupervised clustering technique to objectively determine whether alarms produced by each sex fall into discrete types. We found that the calls of males and females differed most in frequency-based parameters, with male alarms containing more energy at lower frequencies relative to females. Calls produced by males were also of longer duration, and consisted of longer individual call elements relative to female calls. While calls generally fell into clusters associated with either male or female alarms, we found that some fell into atypical clusters given the caller's sex, and that the clusters themselves showed evidence of intergradation. We discuss these results in terms of potential differences in the function of, and motivation for, calling by males and females. We emphasize the need for a more holistic approach to the classification of vocal signals that considers contextual, functional, and structural variation.

Enabling Reliable Visual Detection of Chronic Myocardial Infarction with Native T1 Cardiac MRI Using Data-Driven Native Contrast Mapping.

Purpose To investigate whether infarct-to-remote myocardial contrast can be optimized by replacing generic fitting algorithms used to obtain native T1 maps with a data-driven machine learning pixel-wise approach in chronic reperfused infarct in a canine model. Materials and Methods A controlled large animal model (24 canines, equal male and female animals) of chronic myocardial infarction with histologic evidence of heterogeneous infarct tissue composition was studied. Unsupervised clustering techniques using self-organizing maps and t-distributed stochastic neighbor embedding were used to analyze and visualize native T1-weighted pixel-intensity patterns. Deep neural network models were trained to map pixel-intensity patterns from native T1-weighted image series to corresponding pixels on late gadolinium enhancement (LGE) images, yielding visually enhanced noncontrast maps, a process referred to as data-driven native mapping (DNM). Pearson correlation coefficients and Bland-Altman analyses were used to compare findings from the DNM approach against standard T1 maps. Results Native T1-weighted images exhibited distinct pixel-intensity patterns between infarcted and remote territories. Granular pattern visualization revealed higher infarct-to-remote cluster separability with LGE labeling as compared with native T1 maps. Apparent contrast-to-noise ratio from DNM (mean, 15.01 ± 2.88 [SD]) was significantly different from native T1 maps (5.64 ± 1.58; P < .001) but similar to LGE contrast-to-noise ratio (15.51 ± 2.43; P = .40). Infarcted areas based on LGE were more strongly correlated with DNM compared with native T1 maps (R2 = 0.71 for native T1 maps vs LGE; R2 = 0.85 for DNM vs LGE; P < .001). Conclusion Native T1-weighted pixels carry information that can be extracted with the proposed DNM approach to maximize image contrast between infarct and remote territories for enhanced visualization of chronic infarct territories. Keywords: Chronic Myocardial Infarction, Cardiac MRI, Data-Driven Native Contrast Mapping Supplemental material is available for this article. © RSNA, 2024.

Promoting convergence and efficacy of open‐domain question answering via unsupervised clustering

AbstractOpen‐Domain Question Answering (ODQA) has attracted increasing interests due to its extensive applications in search engines and smart robots. In the experiments, it is observed that the convergence of the method has a huge effect on the generalizability performance. Motivated by this observation, an unsupervised clustering technique (namely, ClusSampling) is proposed to promote both the convergence and efficacy of existing ODQA methods via unsupervised clustering. Specifically, unsupervised clustering is first conducted and then negative samples are selected for higher similarity to the questions. In addition, the authors propose to use gap statistics to determine the optimal number of clusters. Experimental results show that the method achieves notable speedup during training and produces accuracy gains of 5.3% and 2.2 on two widely used benchmarks.

Bioinformatics Identification and Experimental Validation of a Prognostic Model for the Survival of Lung Squamous Cell Carcinoma Patients.

Lung squamous cell carcinoma (LUSC) kills more than four million people yearly. Creating more trustworthy tumor molecular markers for LUSC early detection, diagnosis, prognosis, and customized treatment is essential. Cuproptosis, a novel form of cell death, opened up a new field of study for searching for trustworthy tumor indicators. Our goal was to build a risk model to assess drug sensitivity, monitor immune function, and predict prognosis in LUSC patients. The 19 cuproptosis-related genes were found in the literature, and patient genomic and clinical information was collected using the Cancer Genomic Atlas (TCGA) database. The LUSC patients were grouped using unsupervised clustering techniques, and 7626 differentially expressed genes were identified. Using univariate COX analysis, LASSO regression analysis, and multivariate COX analysis, a prognostic model for LUSC patients was developed. The tumor immune escape was evaluated using the Tumor Immune Dysfunction and Exclusion (TIDE) method. The R packages 'pRRophetic,' 'ggpubr,' and 'ggplot2' were utilized to examine drug sensitivity. For modeling, a 6-cuproptosis-based gene signature was found. Patients with high-risk LUSC had significantly worse survival rates than those with low-risk conditions. The possibility of tumor immunological escape was increased in patients with higher risk scores due to more immune cell inactivation. For patients with high-risk LUSC, we discovered seven potent potential drugs (AZD6482, CHIR.99021, CMK, Embelin, FTI.277, Imatinib, and Pazopanib). In conclusion, the cuproptosis-based genes predictive risk model can be utilized to predict outcomes, track immune function, and evaluate medication sensitivity in LUSC patients.

Impact of COVID-19 pandemic on physician-scientist trainees to faculty one year into the pandemic

PurposePhysician-scientists play a crucial role in advancing biomedical sciences. Proportionally fewer physicians are actively engaged in scientific pursuits, attributed to attrition in the training and retention pipeline. This national study evaluated the ongoing and longer-term impact of the COVID-19 pandemic on stress levels, research productivity, and optimism for physician-scientists at all levels of training.MethodsA multi-institutional cross-sectional survey of medical students, graduate students, and residents/fellows/junior faculty (RFJF) was conducted from April to August 2021 to assess the impact of COVID-19 on individual stress, productivity, and optimism. Multivariate regression analyses were performed to identify associated variables and unsupervised variable clustering techniques were employed to identify highly correlated responses.ResultsA total 677 respondents completed the survey, representing different stages of physician-scientist training. Respondents report high levels of stress (medical students: 85%, graduate students: 63%, RFJF: 85%) attributed to impaired productivity concerns, concern about health of family and friends, impact on personal health and impairment in training or career development. Many cited impaired productivity (medical students: 65% graduate students: 79%, RFJF: 78%) associated with pandemic impacts on training, labs closures and loss of facility/resource access, and social isolation. Optimism levels were low (medical students: 37%, graduate students: 38% and RFJF: 39%) with females less likely to be optimistic and more likely to report concerns of long-term effects of COVID-19. Optimism about the future was correlated with not worrying about the long-term effects of COVID-19. Since the COVID-19 pandemic, all respondents reported increased prioritization of time with family/friends (67%) and personal health (62%) over career (25%) and research (24%).ConclusionsThis national survey highlights the significant and protracted impact of the COVID-19 pandemic on stress levels, productivity, and optimism among physician-scientists and trainees. These findings underscore the urgent need for tailored support, including mental health, academic, and career development assistance for this biomedical workforce.

Proposing unsupervised clustering-based earthquake records selection framework for computationally efficient nonlinear response history analysis of structures equipped with multi-stage friction pendulum bearings

The design and analysis of base-isolated structures are critical for ensuring seismic performance and safety. Nevertheless, to effectively design and analyze base-isolated structures, it is essential to consider the structure's response under various ground motions that may occur during an earthquake. The nonlinear response history analysis (NRHA) is a common method for evaluating structures' seismic response under a specific set of ground motions. However, conducting this analysis for many earthquakes can be computationally expensive and time-consuming. Therefore, it is desirable to have a method for selecting a subset of earthquake records representative of the earthquake population. Accordingly, this study introduces a novel block-based framework based on the K-means unsupervised clustering techniques for efficient earthquake records' selection NRHA of base-isolated structures. The proposed approach aims to reduce the number of records required for investigations by identifying clusters of similar seismic signals and selecting representative records from each cluster for the NRHA. Additionally, the proposed framework serves a vital role in identifying the earthquake records that can be used as a training dataset for developing artificial neural network (ANN) models, enabling rapid response estimation of base-isolated structures. This dual functionality of the proposed method provides significant value for researchers and engineers working in earthquake-prone countries. This article presents two case studies to demonstrate the applicability and effectiveness of the proposed framework. The first case study focuses on selecting a subset of earthquake records from a suit that meets the ASCE 7–22 requirements for NRHA on base-isolated structures, aiming to achieve the close mean responses between the full suit and the subset of records. The second case study highlights the development of an ANN model for response estimation of such structures using the proposed framework to select suitable earthquakes as the training dataset, hence providing an adequate database to achieve high estimation results. In general, the study results highlight the capabilities of the proposed frameworks and the benefits of the K-means unsupervised clustering approach in addressing the gap in the literature and providing a robust method for earthquake record selection in various analysis scenarios.

Optimal policies for nutrition administration to very low birth weight infants

AbstractVery low birth weight (VLBW) infants (birth weight 1500 grams) are at risk of postnatal growth restriction. Understanding how nutrition is associated with growth and how these associations vary based on infant characteristics and comorbidities is important to reduce postnatal growth restriction. We propose a three‐step analytical framework: (i) We use unsupervised Clustering techniques to identify subgroups within a cohort of VLBW infants based on infant characteristics, diagnoses, and treatments. (ii) For each cluster, we use Multilevel Modeling to explore the associations between calorie or protein intake and growth velocity (GV) for varying time windows. (iii) We build Mixed‐Integer Programming Models to achieve simple rule‐based policies that physicians can use to classify infants into one of the identified subgroups. We use electronic health records from VLBW infants at Lurie Children's Hospital in Chicago, IL, born between 2011 and 2014. We find that clustering separates infants into two clusters, with Cluster 1 having smaller infants with more comorbidities than Cluster 2. Initial clustering on only sex and birth weight provides results similar to clustering on later‐life diagnoses and treatments. Multilevel models with Clustering provide better model fit than models without clustering. For Cluster 1, there is a significant association between GV and protein but not calories. For Cluster 2, both protein and calories are individually associated with growth. We develop accurate and sparse scoring systems to help clinicians identify infants at higher risk of growth restriction and consider nutrition regimens accordingly.

Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types.

TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes: CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells (Tregs), and CD163+ tumor-associated macrophages (TAMs). In addition, many cancers have tumor-infiltrating M1 and M2 macrophages, neutrophils (Neu), CD4+ T cells (T-helper), CD8+ T cells (T-cytotoxic), eosinophils, and mast cells. A variety of clinical treatments have linked tumor immune cell infiltration (ICI) to immunotherapy receptivity and prognosis. To improve the therapeutic effectiveness of immune-modulating drugs in a wider cancer patient population, immune cells and their interactions in the TME must be better understood. This study examines the clinicopathological effects of TIICs in overcoming tumor-mediated immunosuppression to boost antitumor immune responses and improve cancer prognosis. We successfully analyzed the predictive and prognostic usefulness of TIICs alongside TMB and ICI scores to identify cancer's varied immune landscapes. Traditionally, immune cell infiltration was quantified using flow cytometry, immunohistochemistry, gene set enrichment analysis (GSEA), CIBERSORT, ESTIMATE, and other platforms that use integrated immune gene sets from previously published studies. We have also thoroughly examined traditional limitations and newly created unsupervised clustering and deconvolution techniques (SpatialVizScore and ProTICS). These methods predict patient outcomes and treatment responses better. These models may also identify individuals who may benefit more from adjuvant or neoadjuvant treatment. Overall, we think that the significant contribution of TIICs in cancer will greatly benefit postoperative follow-up, therapy, interventions, and informed choices on customized cancer medicines.

Brain Tumor Detection Using Classification Model

This study aims to develop automated methods for detecting and segmenting brain tumors in MRI scans using neural networks and conventional image processing. Accurately identifying tumor location and boundaries is crucial for diagnosis and determining the most effective treatment approach. Manual review by radiologists is time-consuming and subjective. However, recent advances in deep learning now enable more efficient analysis of large MRI datasets.&#x0D; A variety of neural network models have been explored for tumor segmentation, including recurrent networks, autoencoders and convolutional networks. RNNs are well-suited to capture contextual information across sequential MRI slices. Autoencoders perform unsupervised feature learning from the images. CNNs have shown promise due to their ability to directly learn visual patterns from pixel values. Unsupervised clustering techniques such as fuzzy c-means and k-means are also investigated to group tumor pixels based on intensity similarity without labeled data.&#x0D; Previous research combining these techniques has achieved high segmentation accuracy between 84-97% using additional image processing steps. Automated segmentation provides benefits over manual inspection such as reduced workload and more consistent delineation of tumor margins. It can precisely outline growths to aid radiologists in diagnosis and pathologists in assessing malignancy.&#x0D; The detection and segmentation of brain tumors has applications in both radiology and oncology. Radiologists could benefit from faster scan review and automated report generation. Neurosurgeons would gain insights into a tumor's size, location and infiltration to determine the most suitable surgical approach. Oncologists could utilize quantitative tumor features to select the most targeted radiation therapy or chemotherapy protocols. Overall, continued advances in deep and machine learning hold promise to improve diagnosis and management of brain cancer patients.

Using students’ cognitive, affective, and demographic characteristics to predict their understanding of computational thinking concepts: a machine learning-based approach

ABSTRACT Logistic regression models have traditionally been used to identify the factors contributing to students’ conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected cognitive, affective, and demographic data from 4,142 primary students as features to predict their understanding of computational thinking (CT) concepts. We identified and used five popularly used machine learning models. All five machine learning models outperformed the logistic regression model, with the extreme gradient boosting (XGBoost) model achieving the highest predictive accuracy. We used these features and the K-means algorithm of the unsupervised clustering technique to identify four optimal clusters of students. By comparing the representative students from each of the four clusters, selected by the t-distributed stochastic neighbour embedding algorithm, we found that each cluster had its characteristics. For example, one cluster consisted of students who outperformed students in the other three clusters in mathematics, scored highest in prior experience in programming, and used computers and the Internet most frequently. By identifying the characteristics of these clusters, pedagogical design, and resource support can be proposed to support students’ learning of CT concepts.

AutoSCAN: automatic detection of DBSCAN parameters and efficient clustering of data in overlapping density regions.

The density-based clustering method is considered a robust approach in unsupervised clustering technique due to its ability to identify outliers, form clusters of irregular shapes and automatically determine the number of clusters. These unique properties helped its pioneering algorithm, the Density-based Spatial Clustering on Applications with Noise (DBSCAN), become applicable in datasets where various number of clusters of different shapes and sizes could be detected without much interference from the user. However, the original algorithm exhibits limitations, especially towards its sensitivity on its user input parameters minPts and ɛ. Additionally, the algorithm assigned inconsistent cluster labels to data objects found in overlapping density regions of separate clusters, hence lowering its accuracy. To alleviate these specific problems and increase the clustering accuracy, we propose two methods that use the statistical data from a given dataset's k-nearest neighbor density distribution in order to determine the optimal ɛ values. Our approach removes the burden on the users, and automatically detects the clusters of a given dataset. Furthermore, a method to identify the accurate border objects of separate clusters is proposed and implemented to solve the unpredictability of the original algorithm. Finally, in our experiments, we show that our efficient re-implementation of the original algorithm to automatically cluster datasets and improve the clustering quality of adjoining cluster members provides increase in clustering accuracy and faster running times when compared to earlier approaches.

Smart science: How artificial intelligence is revolutionizing pharmaceutical medicine

Abstract Artificial intelligence (AI) is a discipline within the field of computer science that encompasses the development and utilization of machines capable of emulating human behavior, particularly regarding the astute examination and interpretation of data. AI operates through the utilization of specialized algorithms, and it includes techniques such as deep (DL), and machine learning (ML), and natural language processing (NLP). As a result, AI has found its application in the study of pharmaceutical chemistry and healthcare. The AI models employed encompass a spectrum of methodologies, including unsupervised clustering techniques applied to drugs or patients to discern potential drug compounds or appropriate patient cohorts. Additionally, supervised ML methodologies are utilized to enhance the efficacy of therapeutic drug monitoring. Further, AI-aided prediction of the clinical outcomes of clinical trials can improve efficiency by prioritizing therapeutic intervention that are likely to succeed, hence benefiting the patient. AI may also help create personalized treatments by locating potential intervention targets and assessing their efficacy. Hence, this review provides insights into recent advances in the application of AI and different tools used in the field of pharmaceutical medicine.

Ensemble Clustering to Generate Phenotypes of Kidney Transplant Donors and Recipients.

In this paper we investigate the generation of phenotypes for kidney transplant donors and recipients to assist with decision making around organ allocation. We present an ensemble clustering approach for multi-type data (numerical and categorical) using two different clustering approaches-i.e., model based and vector quantization based clustering. These clustering approaches were applied to a large, US national deceased donor kidney transplant recipient database to characterize members of each cluster (in an unsupervised fashion) and to determine whether the subsequent risk of graft failure differed for each cluster. We generated three distinct clusters of recipients, which were subsequently used to generate phenotypes. Each cluster phenotype had recipients with varying clinical features, and the risk of kidney transplant graft failure and mortality differed across clusters. Importantly, the clustering results by both approaches demonstrated a significant overlap. Utilization of two distinct clustering approaches may be a novel way to validate unsupervised clustering techniques and clustering can be used for organ allocation decision making on the basis of differential outcomes.

Comparison of geological clusters between influenza and COVID-19 in Thailand with unsupervised clustering analysis.

The coronavirus disease (COVID-19) pandemic has considerably impacted public health, including the transmission patterns of other respiratory pathogens, such as the 2009 pandemic influenza (H1N1). COVID-19 and influenza are both respiratory infections that started with a lack of vaccination-based immunity in the population. However, vaccinations have been administered over time, resulting in a transition of the status of both diseases from a pandemic to an endemic. In this study, unsupervised clustering techniques were used to identify clusters of disease trends in Thailand. The analysis incorporated three distinct surveillance datasets: the pandemic influenza outbreak, influenza in the endemic stage, and the early stages of COVID-19. The analysis demonstrated a significant difference in the distribution of provinces between Cluster -1, representing those with unique transmission patterns, and the other clusters, indicating provinces with similar transmission patterns among their members. Specifically, for Pandemic Influenza, the ratio was 61:16, while for Pandemic COVID-19, it was 65:12. In contrast, Endemic Influenza exhibited a ratio of 46:31, with a notable emergence of more clustered provinces in the southern, western, and central regions. Furthermore, a pair of provinces with highly similar spreading patterns were identified during the pandemic stages of both influenza and COVID-19. Although the similarity decreased slightly for endemic influenza, they still belonged to the same cluster. Our objective was to identify the transmission patterns of influenza and COVID-19, with the aim of providing quantitative and spatial information to aid public health management in preparing for future pandemics or transitioning into an endemic phase.