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Polyspectral mean based time series clustering of Indian stock market

Discover Data 2025年第1期3卷 1-18页

作者： Ghosh, Dhrubajyoti Department of Biostatistics and Bioinformatics Duke University Durham USA

In this study, we employ k-means clustering algorithm of polyspectral means to analyze 49 stocks in the Indian stock market. We have used spectral and bispectral information obtained from the data, by using spectral and bispectral means with different weight functions that will give us varying insights into the temporal patterns of the stocks. In particular, the higher order polyspectral means can provide significantly more information than what we can gather from power spectra, and can also unveil nonlinear trends in a time series. Through rigorous analysis, we identify five distinctive clusters, uncovering nuanced market structures. Notably, one cluster emerges as that of a conglomerate powerhouse, featuring ADANI, BIRLA, TATA, and unexpectedly, government-owned bank SBI. Another cluster spotlights the IT sector with WIPRO and TCS, while a third combines private banks, government entities, and RELIANCE. The final cluster comprises publicly traded companies with dispersed ownership. Such clustering of stocks sheds light on intricate financial relationships within the stock market, providing valuable insights for investors and analysts navigating the dynamic landscape of the Indian stock market.

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FUNCTIONAL CLUSTERING FOR LONGITUDINAL ASSOCIATIONS BETWEEN SOCIAL DETERMINANTS OF HEALTH AND STROKE MORTALITY IN THE U.S.

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Annals of Applied Statistics 2025年第1期19卷 798-820页

作者： Luo, Fangzhi Tan, Jianbin Zhang, Donglan Huang, Hui Shen, Ye Department of Epidemiology and Biostatistics College of Public Health University of Georgia United States Department of Biostatistics and Bioinformatics School of Medicine Duke University United States Department of Foundations of Medicine NYU Grossman Long Island School of Medicine United States Center for Applied Statistics and School of Statistics Renmin University of China China

Understanding the longitudinally changing associations between Social Determinants of Health (SDOH) and stroke mortality is essential for effective stroke management. Previous studies have uncovered significant regional disparities in the associations between SDOH and stroke mortality. However, existing studies have not utilized longitudinal associations to develop datadriven methods for regional division in stroke control. To fill this gap, we propose a novel clustering method to analyze SDOH-stroke mortality associations across U.S. counties. To enhance the interpretability of the clustering outcomes, we introduce a novel regularized expectation-maximization algorithm equipped with sparsity-and smoothness-pursued penalties, aiming at simultaneous clustering and variable selection in longitudinal associations. As a result, we can identify key SDOH that contributes to longitudinal changes in stroke mortality. This facilitates the clustering of U.S. counties based on the associations between these SDOH and stroke mortality. The effectiveness of our proposed method is demonstrated through extensive numerical studies. By applying our method to longitudinal data on SDOH and stroke mortality at the county level, we identify 18 important SDOH for stroke mortality and divide the U.S. counties into two clusters based on these selected SDOH. Our findings unveil complex regional heterogeneity in the longitudinal associations between SDOH and stroke mortality, providing valuable insights into region-specific SDOH adjustments for mitigating stroke mortality. © Institute of Mathematical Statistics, 2025.

关键词： functional regression model Model-based clustering regularized expectation-maximization algorithm stroke mortality variable selection

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The emerging role of mesenchymal stem cell-derived extracellular vesicles to ameliorate hippocampal NLRP3 inflammation induced by binge-like ethanol treatment in adolescence

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Neural Regeneration Research 2025年第4期20卷 1153-1163页

作者： Susana Mellado María JoséMorillo-Bargues Carla Perpiñá-Clérigues Francisco García-García Victoria Moreno-Manzano Consuelo Guerri María Pascual Department of Physiology School of Medicine and DentistryUniversity of ValenciaValenciaSpain Bioinformatics and Biostatistics Unit Príncipe Felipe Research CenterValenciaSpain Neuronal and Tissue Regeneration Laboratory Príncipe Felipe Research CenterValenciaSpain

Our previous studies have reported that activation of the NLRP3(NOD-,LRR-and pyrin domain-containing protein 3)-inflammasome complex in ethanol-treated astrocytes and chronic alcohol-fed mice could be associated with neuroinflammation and brain *** stem cell-derived extracellular vesicles(MSC-EVs)have been shown to restore the neuroinflammatory response,along with myelin and synaptic structural alterations in the prefrontal cortex,and alleviate cognitive and memory dysfunctions induced by binge-like ethanol treatment in adolescent *** the therapeutic role of the molecules contained in mesenchymal stem cell-derived extracellular vesicles,the present study analyzed whether the administration of mesenchymal stem cell-derived extracellular vesicles isolated from adipose tissue,which inhibited the activation of the NLRP3 inflammasome,was capable of reducing hippocampal neuroinflammation in adolescent mice treated with binge *** demonstrated that the administration of mesenchymal stem cell-derived extracellular vesicles ameliorated the activation of the hippocampal NLRP3 inflammasome complex and other NLRs inflammasomes(e.g.,pyrin domain-containing 1,caspase recruitment domain-containing 4,and absent in melanoma 2,as well as the alterations in inflammatory genes(interleukin-1β,interleukin-18,inducible nitric oxide synthase,nuclear factor-kappa B,monocyte chemoattractant protein-1,and C–X3–C motif chemokine ligand 1)and miRNAs(miR-21a-5p,miR-146a-5p,and miR-141-5p)induced by binge-like ethanol treatment in adolescent *** analysis further revealed the involvement of miR-21a-5p and miR-146a-5p with inflammatory target genes and NOD-like receptor signaling *** together,these findings provide novel evidence of the therapeutic potential of MSC-derived EVs to ameliorate the hippocampal neuroinflammatory response associated with NLRP3 inflammasome activation induced by binge drinking in adolescence.

关键词： adolescence binge-like ethanol treatment extracellular vesicles hippocampus mesenchymal stem cells neuroinflammation NOD-,LRR-and pyrin domain-containing protein 3(NLRP3)

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Screening and function analysis of key genes and signaling pathways in Alzheimer’s disease via next generation sequencing data analysis and bioinformatics approaches

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Genome Instability & Disease 2025年第1期6卷 42-59页

作者： Vastrad, Basavaraj Vastrad, Chanabasayya Department of Pharmaceutical Chemistry KLE College of Pharmacy Gadag India Biostatistics and Bioinformatics Dharwad India

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by extracellular senile plaques. The pathogenesis of AD remains unclear. This study aims to explore the molecular mechanism of AD through bioinformatics and next generation sequencing (NGS) data analysis. NGS dataset GSE125583 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the “limma” R bioconductor package. Gene Ontology (GO) and REACTOME enrichment analysis were conducted. Analysis and visualization of protein–protein interaction network (PPI) and modules were carried out with HIPPIE and Cytoscape. Subsequently, miRNA-hub gene regulatory network and TF-hub gene regulatory network were built to predict the underlying microRNAs (miRNAs) and transcription factors (TFs) associated with hub genes. Finally, receiver operating characteristic (ROC) curve to assess the diagnostic efficacy of hub genes, which have been validated with training and validation datasets. Totally 956 DEGs (479 up regulated DEGs and 477 down regulated DEGs) were identified from GSE125583. GO and REACTOME pathway enrichment analysis indicated that DEGS were mainly enriched in regulation of biological quality, cell junction, channel activity, cell communication, plasma membrane, signaling receptor activity, neuronal system and GPCR ligand binding. 10 hub genes were selected, including PAK1, ELAVL2, NSF, HTR2C, TERT, UBD, MKI67, HSPB1, PYHIN1 and TES. MiRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed successfully. Hsa-mir-4517, hsa-mir-3652, EGR1 and ZNF354C were predicted as possible key miRNAs and TFs for progression of AD. In conclusion, the results in this study provided reliable key genes and signaling pathways for AD, which will be useful for AD molecular mechanisms, diagnosis, prognosis and candidate targeted treatment.

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Fast Algorithm for Calculating Probability of Chess Winning Streaks

arXiv

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arXiv 2025年

作者： Diao, Guoqing Department of Biostatistics and Bioinformatics George Washington University United States

Motivated by the controversy in the chess community, where Hikaru Nakamura, a renowned grandmaster, has posted multiple impressive winning streaks over the years on the online platform ***, we derive the probabilities of various types of streaks in online chess and/or other sports. Specifically, given the winning/drawing/losing probabilities of individual games, we derive the probabilities of"pure" winning streaks, non-losing streaks, and"in-between" streaks involving at most one draw over the course of games played in a period. The performance of the developed algorithms is examined through numerical studies. © 2025, CC BY-NC-ND.

关键词： Monte Carlo methods

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In Reply to Li

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International Journal of Radiation Oncology Biology Physics 2025年第2期122卷 513-514页

作者： Eschrich, Steven A. Torres-Roca, Javier F. Department of Bioinformatics Biostatistics and Radiation Oncology Moffitt Cancer Center TampaFlorida United States

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Development of a natural language processing algorithm to extract social determinants of health from clinician notes

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American Journal of Transplantation 2025年第6期25卷 1306-1318.0页

作者： Zaribafzadeh, Hamed Henson, Jacqueline B. Chan, Norine W. Rogers, Ursula Webster, Wendy Schappe, Tyler Li, Fan Matsouaka, Roland A. Kirk, Allan D. Henao, Ricardo McElroy, Lisa M. Department of Surgery Duke University Durham NC United States Department of Medicine Duke University Durham NC United States Department of Biostatistics and Bioinformatics Duke University Durham NC United States Department of Statistical Science Duke University Durham NC United States

Disparities in access to the organ transplant waitlist are well-documented, but research into modifiable factors has been limited due to a lack of access to organized prewaitlisting data. This study aimed to develop a natural language processing (NLP) algorithm to extract social determinants of health (SDOH) from free-text notes and quantify the association of SDOH with access to the transplant waitlist. We collected 261 802 clinician notes from 11 111 adults referred for kidney or liver transplants between 2016 and 2022 at the Duke University Health System. An SDOH ontology and a rule-based NLP algorithm were created to extract and organize terms. Education, transportation, and age were the most frequent terms identified. Negative sentiment and refer were the most negatively associated features with listing in both kidney and liver transplant patients. Income and employment for the kidney, and judgment and positive sentiment for liver were the most positively associated features with the listing. This study suggests that the integration of NLP tools into the transplant clinical workflow could help improve collection and organization of SDOH and inform center-level efforts at resource allocation, potentially improving access to the transplant waitlist and posttransplant outcomes. © 2025 American Society of Transplantation & American Society of Transplant Surgeons

关键词： natural language processing social determinants of health

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Deciphering COVID-19 Narratives: A Comparative Study of ML Models (RF, MNB, GB, LR, SVM) and DL Models (CNN, Bi-LSTM) for News Article Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第14期49卷 127-142页

作者： Das, Kana Md. Asadullah, Murad Hossain Tanni, Annita Siddeka Islam, Shahidul Islam, Masudul Sumy, Mst Sharmin Akter Department of Statistics Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj8100 Bangladesh Bangladesh Rice Research Institute Gazipur1701 Bangladesh Statistics Discipline Khulna University Khulna Bangladesh Department of Bioinformatics and Biostatistics University of Louisville 485 E. Gray St LouisvilleKY40202 United States

The COVID-19 pandemic has provided an unprecedented amount of information in news outlets, which include scientific, health-related, political, economic, and social narratives. This study compares the effectiveness of machine learning and deep learning algorithms for classifying text data, with a certain emphasis on how well the former handle COVID-19 news narratives. The study dataset contains news articles regarding COVID-19. To achieve the primary purpose of this research is to classify COVID-19 related news, we integrate multiple datasets. The analysis reveals machine learning models exhibit superior performance in text data classification. In particular, the Random Forest model reaches a 98% accuracy rate. In contrast, with regards to deep learning models, the Bidirectional Long Short-Term Memory model with FastText integration turns out to be the best option due to its exceptional accuracy. Exploratory data techniques such as topic modeling and word cloud approaches are incorporated to uncover hidden patterns in the data. Pre-trained (e.g., deep learning) and non-pre-trained ML models are implemented highlighting the versatility of ML in text classification tasks. The specific purpose to compare to the deep learning and machine learning algorithm to classification of the new article. Notably, a predictive model employing Bi-LSTM with the FastText pre-trained model achieved an impressive 94% accuracy in classifying COVID-19 news reports. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Contrastive Learning

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A non-parametric U-statistic testing approach for multi-arm clinical trials with multivariate longitudinal data

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Journal of Multivariate Analysis 2025年 209卷

作者： Ghosh, Dhrubajyoti Luo, Sheng Department of Biostatistics & Bioinformatics Duke University United States

Randomized clinical trials (RCTs) often involve multiple longitudinal primary outcomes to comprehensively assess treatment efficacy. The Longitudinal Rank-Sum Test (LRST) Xu et al. (2025), a robust U-statistics-based, non-parametric, rank-based method, effectively controls Type I error and enhances statistical power by leveraging the temporal structure of the data without relying on distributional assumptions. However, the LRST is limited to two-arm comparisons. To address the need for comparing multiple doses against a control group in many RCTs, we extend the LRST to a multi-arm setting. This novel multi-arm LRST provides a flexible and powerful approach for evaluating treatment efficacy across multiple arms and outcomes, with a strong capability for detecting the most effective dose in multi-arm trials. Extensive simulations demonstrate that this method maintains excellent Type I error control while providing greater power compared to the two-arm LRST with multiplicity adjustments. Application to the Bapineuzumab (Bapi) 301 trial further validates the multi-arm LRST's practical utility and robustness, confirming its efficacy in complex clinical trial analyses. © 2025 Elsevier Inc.

关键词： Longitudinal data Rank-Sum Test U-statistics

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Power and sample size calculation for multivariate longitudinal trials using the longitudinal rank sum test

arXiv

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arXiv 2025年

作者： Ghosh, Dhrubajyoti Xu, Xiaoming Luo, Sheng Department of Biostatistics and Bioinformatics Duke University DurhamNC27606 United States

Neurodegenerative diseases such as Alzheimer’s and Parkinson’s often exhibit complex, multivariate longitudinal outcomes that require advanced statistical methods to comprehensively evaluate treatment efficacy. The Longitudinal Rank Sum Test (LRST) offers a nonparametric framework to assess global treatment effects across multiple longitudinal endpoints without requiring multiplicity corrections. This study develops a robust methodology for power and sample size estimation specific to the LRST, integrating theoretical derivations, asymptotic properties, and practical estimation techniques. Validation through numerical simulations demonstrates the accuracy of the proposed methods, while real-world applications to clinical trials in Alzheimer’s and Parkinson’s disease highlight their practical significance. This framework facilitates the design of efficient, well-powered trials, advancing the evaluation of treatments for complex diseases with multivariate longitudinal outcomes. © 2025, CC BY.

关键词： Neurodegenerative diseases

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