Unsupervised Clustering Algorithm Research Articles

ScVSC: Deep variational subspace clustering for single-cell transcriptome data.

Single-cell RNA sequencing (scRNA-seq) is a potent advancement for analyzing gene expression at the individual cell level, allowing for the identification of cellular heterogeneity and subpopulations. However, it suffers from technical limitations that result in sparse and heterogeneous data. Here, we propose scVSC, an unsupervised clustering algorithm built on deep representation neural networks. The method incorporates the variational inference into the subspace model, which imposes regularization constraints on the latent space and further prevents overfitting. In a series of experiments across multiple datasets, scVSC outperforms existing state-of-the-art unsupervised and semi-supervised clustering tools regarding clustering accuracy and running efficiency. Moreover, the study indicates that scVSC could visually reveal the state of trajectory differentiation, accurately identify differentially expressed genes, and further discover biologically critical pathways.

Phenotypes of maternal vascular malperfusion placental pathology and adverse pregnancy outcomes: A retrospective cohort study.

To identify which components of maternal vascular malperfusion (MVM) pathology are associated with adverse pregnancy outcomes and to investigate the morphological phenotypes of MVM placental pathology and their relationship with distinct clinical presentations of pre-eclampsia and/or fetal growth restriction (FGR). Retrospective cohort study. Tertiary care hospital in Toronto, Canada. Pregnant individuals with low circulating maternal placental growth factor (PlGF) levels (<100 pg/mL) and placental pathology analysis between March 2017 and December 2019. Association between each pathological finding and the outcomes of interest were calculated using the chi-square test. Cluster analysis and logistic regression was used to identify phenotypic clusters, and their association with adverse pregnancy outcomes. Cluster analysis was performed using the K-modes unsupervised clustering algorithm. Preterm delivery <34+0 weeks of gestation, early onset pre-eclampsia with delivery <34+0 weeks of gestation, birthweight <10th percentile (small for gestational age, SGA) and stillbirth. The diagnostic features of MVM most strongly associated with delivery <34+0 weeks of gestation were: infarction, accelerated villous maturation, distal villous hypoplasia and decidual vasculopathy. Two dominant phenotypic clusters of MVM pathology were identified. The largest cluster (n = 104) was characterised by both reduced placental mass and hypoxic ischaemic injury (infarction and accelerated villous maturation), and was associated with combined pre-eclampsia and SGA. The second dominant cluster (n = 59) was characterised by infarction and accelerated villous maturation alone, and was associated with pre-eclampsia and average birthweight for gestational age. Patients with placental MVM disease are at high risk of pre-eclampsia and FGR, and distinct pathological findings correlate with different clinical phenotypes, suggestive of distinct subtypes of MVM disease.

Forward genetics combined with unsupervised classifications identified zebrafish mutants affecting biliary system formation

Impaired formation of the biliary network can lead to congenital cholestatic liver diseases; however, the genes responsible for proper biliary system formation and maintenance have not been fully identified. Combining computational network structure analysis algorithms with a zebrafish forward genetic screen, we identified 24 new zebrafish mutants that display impaired intrahepatic biliary network formation. Complementation tests suggested these 24 mutations affect 24 different genes. We applied unsupervised clustering algorithms to unbiasedly classify the recovered mutants into three classes. Further computational analysis revealed that each of the recovered mutations in these three classes has a unique phenotype on node-subtype composition and distribution within the intrahepatic biliary network. In addition, we found most of the recovered mutations are viable. In those mutant fish, which are already good animal models to study chronic cholestatic liver diseases, the biliary network phenotypes persist into adulthood. Altogether, this study provides unique genetic and computational toolsets that advance our understanding of the molecular pathways leading to biliary system malformation and cholestatic liver diseases.

The Association between Mutational Signatures and Clinical Outcomes among Patients with Early-Onset Breast Cancer.

Early-onset breast cancer (EoBC), defined by a diagnosis <40 years of age, is associated with poor prognosis. This study investigated the mutational landscape of non-metastatic EoBC and the prognostic relevance of mutational signatures using 100 tumour samples from Alberta, Canada. The MutationalPatterns package in R/Bioconductor was used to extract de novo single-base substitution (SBS) and insertion-deletion (indel) mutational signatures and to fit COSMIC SBS and indel signatures. We assessed associations between these signatures and clinical characteristics of disease, in addition to recurrence-free (RFS) and overall survival (OS). Five SBS and two indel signatures were extracted. The SBS13-like signature had higher relative contributions in the HER2-enriched subtype. Patients with higher than median contribution tended to have better RFS after adjustment for other prognostic factors (HR = 0.29; 95% CI: 0.08-1.06). An unsupervised clustering algorithm based on absolute contribution revealed three clusters of fitted COSMIC SBS signatures, but cluster membership was not associated with clinical variables or survival outcomes. The results of this exploratory study reveal various SBS and indel signatures may be associated with clinical features of disease and prognosis. Future studies with larger samples are required to better understand the mechanistic underpinnings of disease progression and treatment response in EoBC.

Improving spatiotemporal image fusion incorporating unmixing step by considering the point spread function effect

ABSTRACT In this paper, an improving spatiotemporal image fusion (STIF) incorporating unmixing step by considering the point spread function (PSF) effect model (CPSF) is proposed to address the problem that unmixing accuracy in STIF models incorporating unmixing step is vulnerable to the PSF effect. First, an unsupervised clustering algorithm is used to cluster the fine image at previous date into a land cover map, and the coarse class fraction matrix is generated from this land cover map. Second, the fine class fraction matrix uncontaminated by the PSF effect is calculated by upsampling the coarse class fraction matrix using the Area-to-Point Kriging (ATPK) method under the PSF constraint. Third, this fine class fraction matrix is convolved with the ideal square wave filter to downsample, and then normalized to obtain the coarse class fraction matrix without the influence of the PSF effect. Finally, the CPSF-processed class fraction matrix is used to replace the original one in the following unmixing and prediction process to generate the fine resolution image at the prediction date. Experiments on two remote sensing data sets show that the proposed method can effectively improve the accuracy of spatiotemporal image fusion methods incorporating unmixing step.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice.

Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Šidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.

Unraveling the unfolded protein response signature: implications for tumor immune microenvironment heterogeneity and clinical prognosis in stomach cancer.

Stomach cancer is a leading cause of cancer-related deaths globally due to its high grade and poor response to treatment. Understanding the molecular network driving the rapid progression of stomach cancer is crucial for improving patient outcomes. This study aimed to investigate the role of unfolded protein response (UPR) related genes in stomach cancer and their potential as prognostic biomarkers. RNA expression data and clinical follow-up information were obtained from the TCGA and GEO databases. An unsupervised clustering algorithm was used to identify UPR genomic subtypes in stomach cancer. Functional enrichment analysis, immune landscape analysis, and chemotherapy benefit prediction were conducted for each subtype. A prognostic model based on UPR-related genes was developed and validated using LASSO-Cox regression, and a multivariate nomogram was created. Key gene expression analyses in pan-cancer and in vitro experiments were performed to further investigate the role of the identified genes in cancer progression. A total of 375 stomach cancer patients were included in this study. Analysis of 113 UPR-related genes revealed their close functional correlation and significant enrichment in protein modification, transport, and RNA degradation pathways. Unsupervised clustering identified two molecular subtypes with significant differences in prognosis and gene expression profiles. Immune landscape analysis showed that UPR may influence the composition of the tumor immune microenvironment. Chemotherapy sensitivity analysis indicated that patients in the C2 molecular subtype were more responsive to chemotherapy compared to those in the C1 molecular subtype. A prognostic signature consisting of seven UPR-related genes was constructed and validated, and an independent prognostic nomogram was developed. The gene IGFBP1, which had the highest weight coefficient in the prognostic signature, was found to promote the malignant phenotype of stomach cancer cells, suggesting its potential as a therapeutic target. The study developed a UPR-related gene classifier and risk signature for predicting survival in stomach cancer, identifying IGFBP1 as a key factor promoting the disease's malignancy and a potential therapeutic target. IGFBP1's role in enhancing cancer cell adaptation to endoplasmic reticulum stress suggests its importance in stomach cancer prognosis and treatment.

Gaia Focused Product Release: A catalogue of sources around quasars to search for strongly lensed quasars

Context. Strongly lensed quasars are fundamental sources for cosmology. The Gaia space mission covers the entire sky with the unprecedented resolution of 0.18″ in the optical, making it an ideal instrument to search for gravitational lenses down to the limiting magnitude of 21. Nevertheless, the previous Gaia Data Releases are known to be incomplete for small angular separations such as those expected for most lenses. Aims. We present the Data Processing and Analysis Consortium GravLens pipeline, which was built to analyse all Gaia detections around quasars and to cluster them into sources, thus producing a catalogue of secondary sources around each quasar. We analysed the resulting catalogue to produce scores that indicate source configurations that are compatible with strongly lensed quasars. Methods. GravLens uses the DBSCAN unsupervised clustering algorithm to detect sources around quasars. The resulting catalogue of multiplets is then analysed with several methods to identify potential gravitational lenses. We developed and applied an outlier scoring method, a comparison between the average BP and RP spectra of the components, and we also used an extremely randomised tree algorithm. These methods produce scores to identify the most probable configurations and to establish a list of lens candidates. Results. We analysed the environment of 3 760 032 quasars. A total of 4 760 920 sources, including the quasars, were found within 6″ of the quasar positions. This list is given in the Gaia archive. In 87% of cases, the quasar remains a single source, and in 501 385 cases neighbouring sources were detected. We propose a list of 381 lensed candidates, of which we identified 49 as the most promising ones. Beyond these candidates, the associate tables in this Focused Product Release allow the entire community to explore the unique Gaia data for strong lensing studies further.

ScTPC: a novel semisupervised deep clustering model for scRNA-seq data.

Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging. This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework. scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.

Benchmarking Unsupervised Clustering Algorithms for Atomic Force Microscopy Data on Polyhydroxyalkanoate Films.

Surface of polyhydroxyalkanoate (PHA) films of varying monomer compositions are analyzed using atomic force microscopy (AFM) and unsupervised machine learning (ML) algorithms to investigate and classify films based on global attributes such as the scan size, film thickness, and monomer type. The experiment provides benchmarked results for 12 of the most widely used clustering algorithms via a hybrid investigation approach while highlighting the impact of using the Fourier transform (FT) on high-dimensional vectorized data for classification on various pools of data. Our findings indicate that the use of a one-dimensional (1D) FT of vectorized data produces the most accurate outcome. The experiment also provides insights into case-by-case investigations of algorithm performances and the impact of various data pools. Lastly, we show an early version of our tool aimed at investigating surfaces using ML approaches and discuss the results of our current experiment to configure future improvements.

Wave energy resource classification system for the China East Adjacent Seas based on multivariate clustering

This study based on the 25-year wave hindcast database of the western Pacific and used three unsupervised learning clustering algorithms to classify the wave energy resources in the China East Adjacent Seas. Five wave energy characteristic parameters are comprehensively considered in the calculation process of the clustering algorithm. According to the analysis of the classification results, it can be seen that the Class Ⅳ is the most suitable for wave energy development in the China East Adjacent Seas, followed by the Class Ⅲ and Class Ⅴ. The Class Ⅵ is too far away from the coast to be used as the intended area for wave energy development. The Class Ⅰ is mostly located in inland seas and harbors, which are not suitable for wave energy development. By analyzing the annual average captured power, it can be seen that the optimal capture interval of the existing wave energy converters with mature technology is too large compared with the wave conditions in the China East Adjacent Seas. We should vigorously develop wave energy converters that are more suitable for the wave conditions of Class Ⅲ, Class Ⅳ and Class Ⅴ, and improve the capture efficiency of the wave energy converters.

Comparative investigation of neoadjuvant immunotherapy versus adjuvant immunotherapy in perioperative patients with cancer: a global-scale, cross-sectional, large-sample informatics study.

Neoadjuvant and adjuvant immunotherapies for cancer have evolved through a series of remarkable and critical research advances; however, addressing their similarities and differences is imperative in clinical practice. Therefore, this study aimed to examine their similarities and differences from the perspective of informatics analysis. This cross-sectional study retrospectively analyzed extensive relevant studies published between 2014 and 2023 using stringent search criteria, excluding non-peer-reviewed and non-English documents. The main outcome variables are publication volume, citation volume, connection strength, occurrence frequency, relevance percentage, and development percentage. Furthermore, an integrated comparative analysis was conducted using unsupervised hierarchical clustering, spatiotemporal analysis, regression statistics, and Walktrap algorithm analysis. This analysis included 1,373 relevant studies. Advancements in neoadjuvant and adjuvant immunotherapies have been promising over the last decade, with an annual growth rate of 25.18% vs. 6.52% and global collaboration (International Co-authorships) of 19.93% vs. 19.84%. Respectively, five dominant research clusters were identified through unsupervised hierarchical clustering based on machine learning, among which Cluster 4 (Balance of neoadjuvant immunotherapy efficacy and safety) and Cluster 2 (Adjuvant immunotherapy clinical trials) (Average Publication Year [APY]: 2021.70±0.70 vs. 2017.54±4.59) are emerging research populations. Burst and regression curve analyses uncovered domain pivotal research signatures, including microsatellite instability (R2=0.7500, P=0.0025) and biomarkers (R2=0.6505, P=0.0086) in neoadjuvant scenarios, and the tumor microenvironment (R2=0.5571, P=0.0209) in adjuvant scenarios. The Walktrap algorithm further revealed that "neoadjuvant immunotherapy, non-small cell lung cancer (NSCLC), immune checkpoint inhibitors, melanoma" and "adjuvant immunotherapy, melanoma, hepatocellular carcinoma, dendritic cells" (Relevance Percentage: 100% vs. 100%, Development Percentage: 37.5% vs. 17.1%) are extremely relevant to this field but remain underdeveloped, highlighting the need for further investigation. This study identified pivotal research signatures and provided substantial predictions for neoadjuvant and adjuvant cancer immunotherapies. In addition, comprehensive quantitative comparisons revealed a notable shift in focus within this field, with neoadjuvant immunotherapy taking precedence over adjuvant immunotherapy after 2020; such a qualitative finding facilitate proper decision-making for subsequent research and mitigate the wastage of healthcare resources.

The Clinical Relevance of the Infiltrating Immune Cell Composition in Kidney Transplant Rejection.

The link between the histology of kidney transplant rejection, especially Antibody-mediated rejection, T cell-mediated rejection and Mixed rejection, and the types of infiltrating immune cells is currently not well charted. Cost and technical complexity of single cell analysis hinder large scale studies of the relationship between cell infiltrate profiles and histological heterogeneity. In this cross-sectional study, we assessed the composition of nine intragraft immune cell types by using a validated kidney transplant-specific signature matrix for deconvolution of bulk transcriptomics in three different kidney transplant biopsy datasets (N=403, N=224, N=282). The association and the discrimination of the immune cell types with the Banff histology and the association with graft failure were assessed individually and with multivariable models. Unsupervised clustering algorithms were applied on the overall immune cells composition and compared to the Banff phenotypes. Banff-defined rejection was related to high presence of CD8+ effector T cells, Natural Killer cells, monocytes/macrophages and to a lesser extent B cells, whereas CD4+ memory T cells were lower in rejection compared to no rejection. Estimated intragraft effector memory-expressing CD45RA (TEMRA) CD8+ T cells were strongly and consistently associated with graft failure. The large heterogeneity in immune cell composition across rejection types prevented supervised and unsupervised methods to accurately recover the Banff phenotypes based solely on immune cell estimates. The lack of correlation between immune cell composition and Banff-defined rejection types was validated using multiplex immunohistochemistry. Although some specific cell types (FCGR3A+ myeloid cells, CD14+ monocytes/macrophages and NK cells), partly discriminate between rejection phenotypes, the overall estimated immune cell composition of kidney transplants is ill related to main Banff-defined rejection categories and adds to the Banff lesion scoring and evaluation of rejection severity. The estimated intragraft CD8temra cells bear strong and consistent association with graft failure and independent of Banff-grade rejection.

The expenditure composition and trade-offs in local government budgets

The fiscal behavior of local governments has gained additional attention from academia and policymakers due to decentralization reforms transferring power from central to lower levels of government. As local governments assume more responsibilities for public goods and services, understanding their expenditure composition and trade-offs becomes crucial. While existing literature has examined the factors influencing the size of local government budgets, less attention has been given to the decision-making mechanisms and trade-offs within budget allocations. This study investigates the expenditure composition of 300 Flemish municipalities, using an unsupervised clustering algorithm and a binary logistic regression framework. Our results are indicative of how local governments tend to prioritize specific policy areas in the design of their budgets, when they operate under strict institutional and fiscal constraints such as a balanced budget rule.

Optical fiber sensing and tensor AP clustering for high-speed road tunnel vehicle detection

Aiming at the problems of signal acquisition, feature extraction and vehicle recognition in highway tunnel vehicle detection, a new tunnel vehicle detection method is proposed by combining optical time-domain reflectometry distributed fiber sensing technology and tensor affine propagation clustering algorithm. Firstly, the distributed optical fiber system designed by optical time domain reflectometry was used to collect the running signals of tunnel vehicles and obtain the measurement data. Secondly, a high-order tensor sample set is constructed by using the spatial resolution of optical fiber as channel number and combining the feature number, time domain and frequency domain. Finally, tensor affine propagation clustering method and other clustering methods are used to test the accuracy. The test results show that the proposed method can better classify vehicles without destroying the original high-dimensional data structure. Meanwhile, the unsupervised clustering algorithm also reduces manual intervention in the identification process, increases the intelligence level of the whole vehicle detection model, and effectively improves the detection accuracy rate of tunnel vehicles.

Macroeconomic determinants of multidimensional risk of Islamic banks across the world

Banking system crises and bank failures can have painful impacts. Being an Islamic Bank is riskier than being a conventional bank. As IBIs risk management emphasizes risk-sharing, conventional banks transfer their risk to borrowers. In our study, we have taken 4 risk factors which are Rate of Return Risk, Operational Risk, Credit Risk and Liquidity Risk, as a proxy of all the risks faced by IBIs and developed a Composite Risk Index using these major risk indicators of Islamic banks. Further, this study has proposed indicators like Size1 for liquid assets and Size2 for fixed assets, as in Islamic banks, there is no concept of money markets. The data for the research was collected from the secondary sources of Islamic banks’ financial statements, the EIKON database and World development indicators. The data for 96 Islamic banks were analyzed. The data was analyzed through Python, R and Stata. This study is instrumental in using the unsupervised machine learning clustering algorithm known as KMeans to estimate the prevalent risk level of the bank by aggregating four-dimensional risk indicators to form a single indicator. Moreover, the study explored macroeconomic determinants i.e., Asymmetric Stock Market, Real Business Cycle (GDP), Regulatory Quality and Economic Globalization of the Composite Risk Index and developed a channel via which it affects the risk level of banks. The results showed that in a country, better regulatory quality, higher fixed assets and economic boom in the business cycle play an important role in reducing the risk level of Islamic banks. This study provides a quantified contribution of Islamic banking to social welfare, compared to focusing on the numbers of risk indicators only and comparing it with fixed boundaries developed for the case other indicators.

A network approach for multiscale catchment classification using traits

Abstract. The classification of river catchments into groups with similar biophysical characteristics is useful to understand and predict their hydrological behavior. The increasing availability of remote sensing and other large-scale geospatial datasets has enabled the use of advanced data-driven approaches to classify catchments using traits such as topography, geology, climate, land cover, land use, and human influence. Unsupervised clustering algorithms based on the Euclidean distance are commonly used for trait-based classification but are not suitable for highly dimensional data. In this study we present a new network-based method for multi-scale catchment classification, which can be applied to large datasets and used to determine the traits associated with different catchment groups. In this framework, two networks are analyzed in parallel: the first being where the nodes are traits and the second being where the nodes are catchments. In both cases, edges represent pairwise similarity, and a network cluster detection algorithm is used for the classification. The trait network is used to investigate redundancy in the trait data and to condense this information into a small number of interpretable categories. The catchments network is used to classify the catchments into clusters and to identify representative catchments for the different groups using the degree centrality metric. We apply this method to classify 9067 river catchments across the contiguous United States at both regional and continental scales using 274 non-categorical traits. At the continental scale, we identify 25 interpretable trait categories and 34 catchment clusters of sizes greater than 50. We find that catchments with similar trait categories are typically located in the same region, with different spatial patterns emerging among clusters dominated by natural and anthropogenic traits. We also find that the catchment clusters exhibit distinct hydrological behavior based on an analysis of streamflow indices. This network approach provides several advantages over traditional means of classification, including better separation of clusters, the use of alternate similarity metrics that are more suitable for highly dimensional data, and reducing redundancy in the trait information. The paired catchment–trait networks enable analysis of hydrological behavior using the dominant trait categories for each catchment cluster. The approach can be used at multiple spatial scales since the network topologies adjust automatically to reflect the trait patterns at the scale of investigation. Finally, the representative catchments identified as hub nodes in the network can be used to guide transferable observational and modeling strategies. The method is broadly applicable beyond hydrology for classification of other complex systems that utilize different types of trait datasets.

Integration analysis of cell division cycle-associated family genes revealed potential mechanisms of gliomagenesis and constructed an artificial intelligence-driven prognostic signature

Cell division cycle-associated (CDCA) gene family members are essential cell proliferation regulators and play critical roles in various cancers. However, the function of the CDCA family genes in gliomas remains unclear. This study aims to elucidate the role of CDCA family members in gliomas using in vitro and in vivo experiments and bioinformatic analyses. We included eight glioma cohorts in this study. An unsupervised clustering algorithm was used to identify novel CDCA gene family clusters. Then, we utilized multi-omics data to elucidate the prognostic disparities, biological functionalities, genomic alterations, and immune microenvironment among glioma patients. Subsequently, the scRNA-seq analysis and spatial transcriptomic sequencing analysis were carried out to explore the expression distribution of CDCA2 in glioma samples. In vivo and in vitro experiments were used to investigate the effects of CDCA2 on the viability, migration, and invasion of glioma cells. Finally, based on ten machine-learning algorithms, we constructed an artificial intelligence-driven CDCA gene family signature called the machine learning-based CDCA gene family score (MLCS). Our results suggested that patients with the higher expression levels of CDCA family genes had a worse prognosis, more activated RAS signaling pathways, and more activated immunosuppressive microenvironments. CDCA2 knockdown inhibited the proliferation, migration, and invasion of glioma cells. In addition, the MLCS had robust and favorable prognostic predictive ability and could predict the response to immunotherapy and chemotherapy drug sensitivity.

Data driven assessment of rock mass quality in red-bed hilly area: a case study of Guang’an city, SW China

The evaluation of geological suitability for urban underground space (UUS) development is an indispensable prerequisite for its optimal utilization. As the actual carrier of underground facilities, the evaluation of rock mass quality plays a crucial role in assessing geological suitability. However, it is notable that the evaluation of rock mass quality has regrettably remained somewhat marginalized within the broader framework of the geological suitability assessment in recent years. The selection of pertinent indicators for the evaluation of rock mass quality inherently presents an appreciable degree of subjectivity. Predominantly subjective evaluation methods continue to dominate the field, while the application of objective algorithms, such as unsupervised clustering, remains in its nascent stage. Furthermore, there is a lack of comprehensive investigations into distinct combinations of attributes. This limitation confines the broader applicability of the evaluation outcomes in the context of urban underground space. Within this study, we meticulously amassed rock core test data from over 40 boreholes of engineering geological significance within the urban planning ambit of Guang'An City. Utilizing the K-means unsupervised clustering algorithm and the Principal Component Analysis (PCA) algorithm. We successfully conducted an unsupervised clustering procedure with nine distinct physical and mechanical attributes. This yielded an aggregation into five discernible clusters. Building upon the derived clustering outcomes, a stratification of rock mass quality was effectuated into three distinct tiers: Level 1 (characterized by pure sandstone), Level 2 (primarily dominated by sandstone), and Level 3 (denoting fair conditions predominantly influenced by mudstone). This structured stratification facilitates a relatively objective and comprehensive evaluation of rock mass quality within the context of the red-bed hilly terrain. In the course of this analytical trajectory, we conducted a dissection of the clustering efficacy. For strongly correlated attributes, we propose a preliminary dimensionality reduction procedure prior to the clustering endeavor. Moreover, we recommend intervals of 10 m for the stratified evaluation in red bed hilly urban terrains.

Routine biomarker profile for the prediction of clinical phenotypes of adult-onset Still's disease using unsupervised clustering algorithm.

This study addresses the challenge of predicting the course of Adult-onset Still's disease (AoSD), a rare systemic autoinflammatory disorder of unknown origin. Precise prediction is crucial for effective clinical management, especially in the absence of specific laboratory indicators. We assessed the effectiveness of combining traditional biomarkers with the k-medoids unsupervised clustering algorithm in forecasting the various clinical courses of AoSD-monocyclic, polycyclic, or chronic articular. This approach represents an innovative strategy in predicting the disease's course. The analysis led to the identification of distinct patient profiles based on accessible biomarkers. Specifically, patients with elevated ferritin levels at diagnosis were more likely to experience a monocyclic disease course, while those with lower erythrocyte sedimentation rate could present with any of the clinical courses, monocyclic, polycyclic, or chronic articular, during follow-up. The study demonstrates the potential of integrating traditional biomarkers with unsupervised clustering algorithms in understanding the heterogeneity of AoSD. These findings suggest new avenues for developing personalized treatment strategies, though further validation in larger, prospective studies is necessary.