Biclustering data analysis: a comprehensive survey.

Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

Exploring the Genetic Heterogeneity of Alzheimer's Disease: Evidence for Genetic Subtypes.

Background: Alzheimer's disease (AD) exhibits considerable phenotypic heterogeneity, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins. Objective: We investigated genetic heterogeneity in AD risk through a multi-step analysis. Methods: We performed principal component analysis (PCA) on AD-associated variants in the UK Biobank (AD cases = 2,739, controls = 5,478) to assess structured genetic heterogeneity. Subsequently, a biclustering algorithm searched for distinct disease-specific genetic signatures among subsets of cases. Replication tests were conducted using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (AD cases = 500, controls = 470). We categorized a separate set of ADNI individuals with mild cognitive impairment (MCI; n = 399) into genetic subtypes and examined cognitive, amyloid, and tau trajectories. Results: PCA revealed three distinct clusters ("constellations") driven primarily by different correlation patterns in a region of strong LD surrounding the MAPT locus. Constellations contained a mixture of cases and controls, reflecting disease-relevant but not disease-specific structure. We found two disease-specific biclusters among AD cases. Pathway analysis linked bicluster-associated variants to neuron morphogenesis and outgrowth. Disease-relevant and disease-specific structure replicated in ADNI, and bicluster 2 exhibited increased cerebrospinal fluid p-tau and cognitive decline over time. Conclusions: This study unveils a hierarchical structure of AD genetic risk. Disease-relevant constellations may represent haplotype structure that does not increase risk directly but may alter the relative importance of other genetic risk factors. Biclusters may represent distinct AD genetic subtypes. This structure is replicable and relates to differential pathological accumulation and cognitive decline over time.

SpaDE: Semantic Locality Preserving Biclustering for Neuroimaging Data.

The most discriminative and revealing patterns in the neuroimaging population are often confined to smaller subdivisions of the samples and features. Especially in neuropsychiatric conditions, symptoms are expressed within micro subgroups of individuals and may only underly a subset of neurological mechanisms. As such, running a whole-population analysis yields suboptimal outcomes leading to reduced specificity and interpretability. Biclustering is a potential solution since subject heterogeneity makes one-dimensional clustering less effective in this realm. Yet, high dimensional sparse input space and semantically incoherent grouping of attributes make post hoc analysis challenging. Therefore, we propose a deep neural network called semantic locality preserving auto decoder (SpaDE), for unsupervised feature learning and biclustering. SpaDE produces coherent subgroups of subjects and neural features preserving semantic locality and enhancing neurobiological interpretability. Also, it regularizes for sparsity to improve representation learning. We employ SpaDE on human brain connectome collected from schizophrenia (SZ) and healthy control (HC) subjects. The model outperforms several state-of-the-art biclustering methods. Our method extracts modular neural communities showing significant (HC/SZ) group differences in distinct brain networks including visual, sensorimotor, and subcortical. Moreover, these bi-clustered connectivity substructures exhibit substantial relations with various cognitive measures such as attention, working memory, and visual learning.

Biclustering for Epi-Transcriptomic Co-functional Analysis.

Dynamic and reversible N6-methyladenosine (m6A) modifications are associated with many essential cellular functions as well as physiological and pathological phenomena. In-depth study of m6A co-functional patterns in epi-transcriptomic data may help to understand its complex regulatory mechanisms. In this chapter, we describe several biclustering mining algorithms for epi-transcriptomic data to discover potential co-functional patterns. The concepts and computational methods discussed in this chapter will be particularly useful for researchers working in related fields. We also aim to introduce new deep learning techniques into the field of co-functional analysis of epi-transcriptomic data.

A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data.

We introduce a Bayesian approach for biclustering that accounts for the prior functional dependence between genes using hidden Markov models (HMMs). We utilize biological knowledge gathered from gene ontologies and the hidden Markov structure to capture the potential coexpression of neighboring genes. Our interpretable model-based clustering characterized each cluster of samples by three groups of features: overexpressed, underexpressed, and irrelevant features. The proposed methods have been implemented in an R package and are used to analyze both the simulated data and The Cancer Genome Atlas kidney cancer data.

Biclustering analysis on tree-shaped time-series single cell gene expression data of Caenorhabditis elegans.

BACKGROUND: In recent years, gene clustering analysis has become a widely used tool for studying gene functions, efficiently categorizing genes with similar expression patterns to aid in identifying gene functions. Caenorhabditis elegans is commonly used in embryonic research due to its consistent cell lineage from fertilized egg to adulthood. Biologists use 4D confocal imaging to observe gene expression dynamics at the single-cell level. However, on one hand, the observed tree-shaped time-series datasets have characteristics such as non-pairwise data points between different individuals. On the other hand, the influence of cell type heterogeneity should also be considered during clustering, aiming to obtain more biologically significant clustering results. RESULTS: A biclustering model is proposed for tree-shaped single-cell gene expression data of Caenorhabditis elegans. Detailedly, a tree-shaped piecewise polynomial function is first employed to fit non-pairwise gene expression time series data. Then, four factors are considered in the objective function, including Pearson correlation coefficients capturing gene correlations, p-values from the Kolmogorov-Smirnov test measuring the similarity between cells, as well as gene expression size and bicluster overlapping size. After that, Genetic Algorithm is utilized to optimize the function. CONCLUSION: The results on the small-scale dataset analysis validate the feasibility and effectiveness of our model and are superior to existing classical biclustering models. Besides, gene enrichment analysis is employed to assess the results on the complete real dataset analysis, confirming that the discovered biclustering results hold significant biological relevance.

EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma.

The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC.

Bibliometric analysis and visualization of quorum sensing research over the last two decade.

Background: Quorum sensing (QS) research stands as a pivotal and multifaceted domain within microbiology, holding profound implications across various scientific disciplines. This bibliometric analysis seeks to offer an extensive overview of QS research, covering the period from 2004 to 2023. It aims to elucidate the hotspots, trends, and the evolving dynamics within this research domain. Methods: We conducted an exhaustive review of the literature, employing meticulous data curation from the Science Citation Index Extension (SCI-E) within the Web of Science (WOS) database. Subsequently, our survey delves into evolving publication trends, the constellation of influential authors and institutions, key journals shaping the discourse, global collaborative networks, and thematic hotspots that define the QS research field. Results: The findings demonstrate a consistent and growing interest in QS research throughout the years, encompassing a substantial dataset of 4,849 analyzed articles. Journals such as Frontiers in Microbiology have emerged as significant contributor to the QS literature, highlighting the increasing recognition of QS's importance across various research fields. Influential research in the realm of QS often centers on microbial communication, biofilm formation, and the development of QS inhibitors. Notably, leading countries engaged in QS research include the United States, China, and India. Moreover, the analysis identifies research focal points spanning diverse domains, including pharmacological properties, genetics and metabolic pathways, as well as physiological and signal transduction mechanisms, reaffirming the multidisciplinary character of QS research. Conclusion: This bibliometric exploration provides a panoramic overview of the current state of QS research. The data portrays a consistent trend of expansion and advancement within this domain, signaling numerous prospects for forthcoming research and development. Scholars and stakeholders engaged in the QS field can harness these findings to navigate the evolving terrain with precision and speed, thereby enhancing our comprehension and utilization of QS in various scientific and clinical domains.

Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

Biclustering of Log Data: Insights from a Computer-Based Complex Problem Solving Assessment.

Computer-based assessments provide the opportunity to collect a new source of behavioral data related to the problem-solving process, known as log file data. To understand the behavioral patterns that can be uncovered from these process data, many studies have employed clustering methods. In contrast to one-mode clustering algorithms, this study utilized biclustering methods, enabling simultaneous classification of test takers and features extracted from log files. By applying the biclustering algorithms to the "Ticket" task in the PISA 2012 CPS assessment, we evaluated the potential of biclustering algorithms in identifying and interpreting homogeneous biclusters from the process data. Compared with one-mode clustering algorithms, the biclustering methods could uncover clusters of individuals who are homogeneous on a subset of feature variables, holding promise for gaining fine-grained insights into students' problem-solving behavior patterns. Empirical results revealed that specific subsets of features played a crucial role in identifying biclusters. Additionally, the study explored the utilization of biclustering on both the action sequence data and timing data, and the inclusion of time-based features enhanced the understanding of students' action sequences and scores in the context of the analysis.

MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping.

Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.

G-bic: generating synthetic benchmarks for biclustering.

BACKGROUND: Biclustering is increasingly used in biomedical data analysis, recommendation tasks, and text mining domains, with hundreds of biclustering algorithms proposed. When assessing the performance of these algorithms, more than real datasets are required as they do not offer a solid ground truth. Synthetic data surpass this limitation by producing reference solutions to be compared with the found patterns. However, generating synthetic datasets is challenging since the generated data must ensure reproducibility, pattern representativity, and real data resemblance. RESULTS: We propose G-Bic, a dataset generator conceived to produce synthetic benchmarks for the normative assessment of biclustering algorithms. Beyond expanding on aspects of pattern coherence, data quality, and positioning properties, it further handles specificities related to mixed-type datasets and time-series data.G-Bic has the flexibility to replicate real data regularities from diverse domains. We provide the default configurations to generate reproducible benchmarks to evaluate and compare diverse aspects of biclustering algorithms. Additionally, we discuss empirical strategies to simulate the properties of real data. CONCLUSION: G-Bic is a parametrizable generator for biclustering analysis, offering a solid means to assess biclustering solutions according to internal and external metrics robustly.

Robust knowledge-guided biclustering for multi-omics data.

Biclustering is a useful method for simultaneously grouping samples and features and has been applied across various biomedical data types. However, most existing biclustering methods lack the ability to integratively analyze multi-modal data such as multi-omics data such as genome, transcriptome and epigenome. Moreover, the potential of leveraging biological knowledge represented by graphs, which has been demonstrated to be beneficial in various statistical tasks such as variable selection and prediction, remains largely untapped in the context of biclustering. To address both, we propose a novel Bayesian biclustering method called Bayesian graph-guided biclustering (BGB). Specifically, we introduce a new hierarchical sparsity-inducing prior to effectively incorporate biological graph information and establish a unified framework to model multi-view data. We develop an efficient Markov chain Monte Carlo algorithm to conduct posterior sampling and inference. Extensive simulations and real data analysis show that BGB outperforms other popular biclustering methods. Notably, BGB is robust in terms of utilizing biological knowledge and has the capability to reveal biologically meaningful information from heterogeneous multi-modal data.

Bibliometric analysis and visualization of endocrine therapy for breast cancer research in the last two decade.

Background: Breast cancer endocrine therapy research has become a crucial domain in oncology since hormone receptor-positive breast cancers have been increasingly recognized, and targeted therapeutic interventions have been advancing over the past few years. This bibliometric analysis attempts to shed light on the trends, dynamics, and knowledge hotspots that have shaped the landscape of breast cancer endocrine therapy research between 2003 and 2022. Methods: In this study, we comprehensively reviewed the scientific literature spanning the above-mentioned period, which included publications accessible through the database of the Web of Science (WOS) and the National Center for Biotechnology Information (NCBI). Next, a systematic and data-driven analysis supported by sophisticated software tools was conducted, such that the core themes, prolific authors, influential journals, prominent countries, and critical citation patterns in the relevant research field can be clarified. Results: A continuous and substantial expansion of breast cancer endocrine therapy research was revealed over the evaluated period. A total of 1,317 scholarly articles were examined. The results of the analysis suggested that research on endocrine therapy for breast cancer has laid a solid basis for the treatment of hormone receptor-positive breast cancer. From a geographical perspective, the US, the UK, and China emerged as the most active contributors, illustrating the global impact of this study. Furthermore, our analysis delineated prominent research topics that have dominated the discourse in the past two decades, including drug therapy, therapeutic efficacy, molecular biomarkers, and hormonal receptor interactions. Conclusion: This comprehensive bibliometric analysis provides a panoramic view of the ever-evolving landscape of breast cancer endocrine therapy research. The findings highlight the trajectory of past developments while signifying an avenue of vast opportunities for future investigations and therapeutic advancements. As the field continues to burgeon, this analysis will provide valuable guidance for to researchers toward pertinent knowledge hotspots and emerging trends, which can expedite the discoveries in the realm of breast cancer endocrine therapy.

RUBic: rapid unsupervised biclustering.

Biclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took [Formula: see text] s to extract 494,872 biclusters. In the human PPI database of size [Formula: see text], our method generates 1840 biclusters in [Formula: see text] s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes   101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEGG-enriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at ( https://github.com/CMATERJU-BIOINFO/RUBic ) for academic use only.

Research hotspot and trend of chronic wounds: A bibliometric analysis from 2013 to 2022.

Chronic wounds have been confirmed as a vital health problem facing people in the global population aging process. While significant progress has been achieved in the study of chronic wounds, the treatment effect should be further improved. The number of publications regarding chronic wounds has been rising rapidly. In this study, bibliometric analysis was conducted to explore the hotspots and trends in the research on chronic wounds. All relevant studies on chronic wounds between 2013 and 2022 were collected from the PubMed database of the Web of Science (WOS) and the National Center for Biotechnology Information (NCBI). The data were processed and visualised using a series of software. On that basis, more insights can be gained into hotspots and trends of this research field. Wound Repair and Regeneration has the highest academic achievement in the field of chronic wound research. The United States has been confirmed as the most productive country, and the University of California System ranks high among other institutions. Augustin, M. is the author of the most published study, and Frykberg, RG et al. published the most cited study. Furthermore, the hotspots of wound research over the last decade were identified (e.g., bandages, infection and biofilms, pathophysiology and therapy). This study will help researchers gain insights into chronic wound research's hotspots and trends accurately and quickly. Moreover, the exploration of bacterial biofilm and the pathophysiological mechanism of the chronic wound will lay a solid foundation and clear direction for treating chronic wounds.

On the challenges of predicting treatment response in Hodgkin's Lymphoma using transcriptomic data.

BACKGROUND: Despite the advancements in multiagent chemotherapy in the past years, up to 10% of Hodgkin's Lymphoma (HL) cases are refractory to treatment and, after remission, patients experience an elevated risk of death from all causes. These complications are dependent on the treatment and therefore an increase in the prognostic accuracy of HL can help improve these outcomes and control treatment-related toxicity. Due to the low incidence of this cancer, there is a lack of works comprehensively assessing the predictability of treatment response, especially by resorting to machine learning (ML) advances and high-throughput technologies. METHODS: We present a methodology for predicting treatment response after two courses of Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) chemotherapy, through the analysis of gene expression profiles using state-of-the-art ML algorithms. We work with expression levels of tumor samples of Classical Hodgkin's Lymphoma patients, obtained through the NanoString's nCounter platform. The presented approach combines dimensionality reduction procedures and hyperparameter optimization of various elected classifiers to retrieve reference predictability levels of refractory response to ABVD treatment using the regulatory profile of diagnostic tumor samples. In addition, we propose a data transformation procedure to map the original data space into a more discriminative one using biclustering, where features correspond to discriminative putative regulatory modules. RESULTS: Through an ensemble of feature selection procedures, we identify a set of 14 genes highly representative of the result of an fuorodeoxyglucose Positron Emission Tomography (FDG-PET) after two courses of ABVD chemotherapy. The proposed methodology further presents an increased performance against reference levels, with the proposed space transformation yielding improvements in the majority of the tested predictive models (e.g. Decision Trees show an improvement of 20pp in both precision and recall). CONCLUSIONS: Taken together, the results reveal improvements for predicting treatment response in HL disease by resorting to sophisticated statistical and ML principles. This work further consolidates the current hypothesis on the structural difficulty of this prognostic task, showing that there is still a considerable gap to be bridged for these technologies to reach the necessary maturity for clinical practice.

Bibliometric analysis and visualization of transdermal drug delivery research in the last decade: global research trends and hotspots.

Background: Transdermal delivery has become a crucial field in pharmaceutical research. There has been a proliferation of innovative methods for transdermal drug delivery. In recent years, the number of publications regarding transdermal drug delivery has been rising rapidly. To investigate the current research trends and hotspots in transdermal drug delivery, a comprehensive bibliometric analysis was performed. Methods: An extensive literature review was conducted to gather information on transdermal drug delivery that had been published between 2003 and 2022. The articles were obtained from the Web of Science (WOS) and the National Center for Biotechnology Information (NCBI) databases. Subsequently, the collected data underwent analysis and visualization using a variety of software tools. This approach enables a deeper exploration of the hotspots and emerging trends within this particular research domain. Results: The results showed that the number of articles published on transdermal delivery has increased steadily over the years, with a total of 2,555 articles being analyzed. The most frequently cited articles were related to the optimization of drug delivery and the use of nanotechnology in transdermal drug delivery. The most active countries in the field of transdermal delivery research were the China, United States, and India. Furthermore, the hotspots over the past 2 decades were identified (e.g., drug therapy, drug delivery, and pharmaceutical preparations and drug design). The shift in research focus reflects an increasing emphasis on drug delivery and control release, rather than simply absorption and penetration, and suggests a growing interest in engineering approaches to transdermal drug delivery. Conclusion: This study provided a comprehensive overview of transdermal delivery research. The research indicated that transdermal delivery would be a rapidly evolving field with many opportunities for future research and development. Moreover, this bibliometric analysis will help researchers gain insights into transdermal drug delivery research's hotspots and trends accurately and quickly.

Exploring the genetic heterogeneity of Alzheimer's disease: Evidence for genetic subtypes.

Background: Alzheimer's disease (AD) exhibits heterogeneity in cognitive impairment, atrophy, and pathological accumulation, suggesting the potential existence of subtypes. AD is under substantial genetic influence, thus identifying systematic variation in genetic risk may provide insights into disease origins. Objective: We investigated genetic heterogeneity in AD risk through a multi-step analysis. Methods: We performed principal component analysis (PCA) on AD-associated variants in the UK Biobank (AD cases=2,739, controls=5,478) to assess the presence of structured genetic heterogeneity. Subsequently, a biclustering algorithm searched for distinct disease-specific genetic signatures among subsets of cases. Replication tests were conducted using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (AD cases=500, controls=470). We categorized a separate set of ADNI individuals with mild cognitive impairment (MCI; n=399) into genetic subtypes and examined cognitive, amyloid, and tau trajectories. Results: PCA revealed three distinct clusters ("constellations") within AD-associated variants containing a mixture of cases and controls, reflecting disease-relevant structure. We found two disease-specific biclusters among AD cases. Pathway analysis linked bicluster-associated variants to neuron morphogenesis and outgrowth, including genes related to cellular components and development-modulating factors. Both disease-relevant and disease-specific structure replicated in ADNI. Individuals with genetic signatures resembling bicluster 2 exhibited increased CSF p-tau and cognitive decline over time. Conclusions: This study unveils a hierarchical structure of AD genetic risk. Disease-relevant constellations may represent differential biological vulnerability that is itself not sufficient to increase risk. Biclusters may represent distinct AD genetic subtypes. This structure replicates in an independent dataset and relates to differential pathological accumulation and cognitive decline over time.

Research progress and hot spot analysis related to oxidative stress and osteoarthritis: a bibliometric analysis.

BACKGROUND: Osteoarthritis, a common degenerative osteochondral disease, has a close relationship between its mechanism of occurrence and oxidative stress. However, there are relatively few relevant studies in this field, and a more mature research system has not yet been formed. METHODS: By searching the Web of Science (WOS) database, we obtained 1 412 publications in the field of osteoarthritis and oxidative stress. The search results were then analyzed bibliometrically using Citespace and VOSviewer, including a study of publication trends in the field, analysis of core authors, analysis of countries and institutions with high contributions, analysis of core journals, and to identify research trends and hot spots in the field, we performed keyword clustering. RESULTS: We collected 1 412 publications on the field of osteoarthritis and oxidative stress from 1998-2022. By analyzing the publication trends in the field, we noted an exponential increase in the number of publications per year since 2014. We then identified the core authors in the field (Blanco, Francisco J., Loeser, Richard F., Vaamonde-garcia, et.al) as well as the countries (China, USA, Italy et.al) and institutions (Xi An Jiao Tong Univ, Wenzhou Med Univ, Zhejiang Univ et.al). The OSTEOARTHRITIS AND CARTILAGE and INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES contain a large number of research papers in this field, and through keyword co-occurrence analysis, we counted 3 227 keywords appearing in the field of osteoarthritis and oxidative stress. These keywords were clustered into 9 groups, representing 9 different research hotspots. CONCLUSIONS: Research in the field of osteoarthritis and oxidative stress has been developing since 1998 and is now maturing, but there is an urgent need to strengthen international academic exchanges and discuss the future focus of research development in the field of osteoarthritis and oxidative stress.