High-dimensional Data Analysis Research Articles

Personalizing Dietary Polyphenols for Health Maintenance and Disease Management: A Nutrigenetic Approach.

This literature review provides examples of the influence of certain genetic variants on health outcomes after dietary polyphenol consumption or supplementation. Available evidence is organized according to the major classes of polyphenols (flavonoids, phenolic acids, stilbenes, lignans, and tannins) and their derived subgroups. Nutrigenetic studies have identified mainly single nucleotide polymorphisms located within genes involved in the biotransformation of phenolic acids, stilbenes, lignans and several flavonoid molecules. These genetic variants may affect polyphenol metabolism rates and related predisposition to chronic non-communicable diseases. Moreover, differential cardiometabolic outcomes upon polyphenol supplementation as dietary sources or nutraceuticals have been modulated by specific genotypes. Although current evidence is still limited, growing gene-polyphenol interactions are contributing to systematically elucidate the biological functions of polyphenols; determine individual risk phenotypes to specific diseases or particular responses upon polyphenol exposure; and facilitate the prescription of personalized genotype-based doses of dietary polyphenols to optimize related health benefits. Additionally, the integration of genetics with other omics insights (epigenomics, transcriptomics, metagenomics, and metabolomics) trough biological systems and high-dimensional data analyses and interpretation may provide a more comprehensive understanding of polyphenol metabolism for precision nutrition applications in health and disease.

Generalizing the Cross Product to N Dimensions: A Novel Approach for Multidimensional Analysis and Applications

This paper presents a generalization of the cross product to N dimensions, extending the classical operation beyond its traditional confines in three-dimensional space. By redefining the cross product to accommodate N−1 arguments in N dimensions, a framework has been established that retains the core properties of orthogonality, magnitude, and anticommutativity. The proposed method leverages the determinant approach and introduces the polar sine function to calculate the magnitude of the cross product, linking it directly to the volume of an N-dimensional parallelotope. This generalization not only enriches the theoretical foundation of vector calculus but also opens up new applications in high-dimensional data analysis, machine learning, and multivariate time series. The results suggest that this extension of the cross product could serve as a powerful tool for modeling complex interactions in multi-dimensional spaces, with potential implications across various scientific and engineering disciplines.

Robustness Analysis and Forecasting of High-Dimensional Financial Time Series Data

Robustness Analysis and Forecasting of High-Dimensional Financial Time Series Data

A comparative evaluation of clustering-based outlier detection

Abstract We perform an extensive experimental evaluation of clustering-based outlier detection methods. These methods offer benefits such as efficiency, the possibility to capitalize on more mature evaluation measures, more developed subspace analysis for high-dimensional data and better explainability, and yet they have so-far been neglected in literature. To our knowledge, our work is the first effort to analytically and empirically study their advantages and disadvantages. Our main goal is to evaluate whether or not clustering-based techniques can compete in efficiency and effectiveness against the most studied state-of-the-art algorithms in the literature. We consider the quality of the results, the resilience against different types of data and variations in parameter configuration, the scalability, and the ability to filter out inappropriate parameter values automatically based on internal measures of clustering quality. It has been recently shown that several classic, simple, unsupervised methods surpass many deep learning approaches and, hence, remain at the state-of-the-art of outlier detection. We therefore study 14 of the best classic unsupervised methods, in particular 11 clustering-based methods and 3 non-clustering-based ones, using a consistent parameterization heuristic to identify the pros and cons of each approach. We consider 46 real and synthetic datasets with up to 125k points and 1.5k dimensions aiming to achieve plausibility with the broadest possible diversity of real-world use cases. Our results indicate that the clustering-based methods are on par with (if not surpass) the non-clustering-based ones, and we argue that clustering-based methods like KMeans−− should be included as baselines in future benchmarking studies, as they often offer a competitive quality at a relatively low run time, besides several other benefits.

A Deep Ensemble Gene Selection and Attention-guided Classification Framework for Robust Cancer Diagnosis from Microarray Data

Microarray technology has enabled unprecedented insight into cancer diagnosis through large-scale gene expression analysis. However, the high dimensionality and complexity of microarray datasets pose significant challenges, as only a small subset of genes is typically informative, with the remainder introducing noise and complicating classification. Traditional gene selection methods, including filter, wrapper, and hybrid techniques, have achieved promising results but often fail to capture complex gene interactions, suffer from computational inefficiencies, or lack interpretability. This study presents DEGS-AGC (Deep Ensemble Gene Selection and Attention-Guided Classification), a novel integrated framework for gene selection and classification. DEGS-AGC is designed to address these limitations through two primary components: Deep Ensemble Gene Selection (DEGS), which leverages ensemble learning with Random Forest, XGBoost, and Deep Neural Networks to select relevant genes while reducing redundancy via sparse autoencoders, and Attention-Guided Classification (AGC), where an attention mechanism dynamically assigns weights to genes to improve interpretability and classification precision. The DEGS-AGC framework was evaluated against traditional methods, using consistent classification models for robust comparisons. Evaluation metrics demonstrated the potential of DEGS-AGC as an effective tool for high-dimensional biomedical data analysis. The results highlighted the ability of DEGS-AGC to offer accurate, interpretable, and computationally feasible solutions for cancer diagnosis, advancing the development of data-driven personalized approaches in healthcare.

Harnessing Unsupervised Ensemble Learning for Biomedical Applications: A Review of Methods and Advances

Advancements in data availability and computational techniques, including machine learning, have transformed the field of bioinformatics, enabling the robust analysis of complex, high-dimensional, and heterogeneous biomedical data. This paper explores how diverse bioinformatics tasks, including differential expression analysis, network inference, and somatic mutation calling, can be reframed as binary classification tasks, thereby providing a unifying framework for their analysis. Traditional single-method approaches often fail to generalize across datasets due to differences in data distributions, noise levels, and underlying biological contexts. Ensemble learning, particularly unsupervised ensemble approaches, emerges as a compelling solution by integrating predictions from multiple algorithms to leverage their strengths and mitigate weaknesses. This review focuses on the principles and recent advancements in ensemble learning, with a particular emphasis on unsupervised ensemble methods. These approaches demonstrate their ability to address critical challenges in bioinformatics, such as the lack of labeled data and the integration of predictions from algorithms operating on different scales. Overall, this paper highlights the transformative potential of ensemble learning in advancing predictive accuracy, robustness, and interpretability across diverse bioinformatics applications.

Degree-heterogeneous Latent Class Analysis for High-dimensional Discrete Data

The latent class model is a widely used mixture model for multivariate discrete data. Besides the existence of qualitatively heterogeneous latent classes, real data often exhibit additional quantitative heterogeneity nested within each latent class. The modern latent class analysis also faces extra challenges, including the high-dimensionality, sparsity, and heteroskedastic noise inherent in discrete data. Motivated by these phenomena, we introduce the Degree-heterogeneous Latent Class Model and propose an easy-to-implement HeteroClustering algorithm for it. HeteroClustering uses heteroskedastic PCA with l 2 normalization to remove degree effects and perform clustering in the top singular subspace of the data matrix. We establish the result of exact clustering under minimal signal-to-noise conditions. We further investigate the estimation and inference of the high-dimensional continuous item parameters in the model, which are crucial to interpreting and finding useful markers for latent classes. We provide comprehensive procedures for global testing and multiple testing of these parameters with valid error controls. The superior performance of our methods is demonstrated through extensive simulations and applications to three diverse real-world datasets from political voting records, genetic variations, and single-cell sequencing.

Diffusive topology preserving manifold distances for single-cell data analysis

Manifold learning techniques have emerged as crucial tools for uncovering latent patterns in high-dimensional single-cell data. However, most existing dimensionality reduction methods primarily rely on 2D visualization, which can distort true data relationships and fail to extract reliable biological information. Here, we present DTNE (diffusive topology neighbor embedding), a dimensionality reduction framework that faithfully approximates manifold distance to enhance cellular relationships and dynamics. DTNE constructs a manifold distance matrix using a modified personalized PageRank algorithm, thereby preserving topological structure while enabling diverse single-cell analyses. This approach facilitates distribution-based cellular relationship analysis, pseudotime inference, and clustering within a unified framework. Extensive benchmarking against mainstream algorithms on diverse datasets demonstrates DTNE's superior performance in maintaining geodesic distances and revealing significant biological patterns. Our results establish DTNE as a powerful tool for high-dimensional data analysis in uncovering meaningful biological insights.

Bowerbird courtship-inspired feature selection for efficient high-dimensional data analysis using a novel meta-heuristic

The challenge of feature selection in high-dimensional datasets is critical in enhancing machine learning model performance and interpretability while reducing computational costs. This paper introduces a novel feature selection method inspired by the unique courtship behaviors of bowerbirds, termed Bowerbird Courtship-Inspired Feature Selection (BBFS). Mimicking the decorative strategies and selective adaptation of bowerbirds, BBFS optimizes the selection of feature subsets through a meta-heuristic approach that efficiently balances exploration and exploitation. We compare the effectiveness of BBFS with established algorithms like Binary Cuckoo Search Optimization (BCSO) and Binary Grey Wolf Optimization (BGWO) across seven diverse datasets. Our results demonstrate that BBFS consistently selects fewer features and maintains or enhances classification accuracy using Decision Trees and Naive Bayes classifiers. This study underscores the potential of BBFS as a scalable and robust tool for high-dimensional data analysis, offering significant improvements over traditional and contemporary feature selection methods.Graphical

Sex hormone-dependent host-microbiome interactions and cardiovascular risk (XCVD): design of a longitudinal multi-omics cohort study

IntroductionCardiovascular diseases (CVDs) present differently in women and men, influenced by host-microbiome interactions. The roles of sex hormones in CVD outcomes and gut microbiome in modifying these effects are poorly...

Subgroup analysis of high dimensional survival data based on penalized fusion and model averaging

Subgroup analysis of high dimensional survival data based on penalized fusion and model averaging

Unveiling the power of high-dimensional cytometry data with cyCONDOR

High-dimensional cytometry (HDC) is a powerful technology for studying single-cell phenotypes in complex biological systems. Although technological developments and affordability have made HDC broadly available in recent years, technological advances were not coupled with an adequate development of analytical methods that can take full advantage of the complex data generated. While several analytical platforms and bioinformatics tools have become available for the analysis of HDC data, these are either web-hosted with limited scalability or designed for expert computational biologists, making their use unapproachable for wet lab scientists. Additionally, end-to-end HDC data analysis is further hampered due to missing unified analytical ecosystems, requiring researchers to navigate multiple platforms and software packages to complete the analysis. To bridge this data analysis gap in HDC we develop cyCONDOR, an easy-to-use computational framework covering not only all essential steps of cytometry data analysis but also including an array of downstream functions and tools to expand the biological interpretation of the data. The comprehensive suite of features of cyCONDOR, including guided pre-processing, clustering, dimensionality reduction, and machine learning algorithms, facilitates the seamless integration of cyCONDOR into clinically relevant settings, where scalability and disease classification are paramount for the widespread adoption of HDC in clinical practice. Additionally, the advanced analytical features of cyCONDOR, such as pseudotime analysis and batch integration, provide researchers with the tools to extract deeper insights from their data. We use cyCONDOR on a variety of data from different tissues and technologies demonstrating its versatility to assist the analysis of high-dimensional data from preprocessing to biological interpretation.

Research on Lipidomic Profiling and Biomarker Identification for Osteonecrosis of the Femoral Head.

Objectives: Abnormal lipid metabolism is increasingly recognized as a contributing factor to the development of osteonecrosis of the femoral head (ONFH). This study aimed to explore the lipidomic profiles of ONFH patients, focusing on distinguishing between traumatic ONFH (TONFH) and non-traumatic ONFH (NONFH) subtypes and identifying potential biomarkers for diagnosis and understanding pathogenesis. Methods: Plasma samples were collected from 92 ONFH patients (divided into TONFH and NONFH subtypes) and 33 healthy normal control (NC) participants. Lipidomic profiling was performed using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Data analysis incorporated a machine learning-based feature selection method, least absolute shrinkage and selection operator (LASSO) regression, to identify significant lipid biomarkers. Results: Distinct lipidomic signatures were observed in both TONFH and NONFH groups compared to the NC group. LASSO regression identified 11 common lipid biomarkers that signify shared metabolic disruptions in both ONFH subtypes, several of which exhibited strong diagnostic performance with areas under the curve (AUCs) > 0.7. Additionally, subtype-specific lipid markers unique to TONFH and NONFH were identified, providing insights into the differential pathophysiological mechanisms underlying these subtypes. Conclusions: This study highlights the importance of lipidomic profiling in understanding ONFH-associated metabolic disorders and demonstrates the utility of machine learning approaches, such as LASSO regression, in high-dimensional data analysis. These findings not only improve disease characterization but also facilitate the discovery of diagnostic and mechanistic biomarkers, paving the way for more personalized therapeutic strategies in ONFH.

Automatic extraction of fine structural information in angle-resolved photoemission spectroscopy by multi-stage clustering algorithm

Unsupervised clustering method has shown strong capabilities in automatically categorizing the ARPES (ARPES: angle-resolved photoemission spectroscopy) spatial mapping dataset. However, there is still room for improvement in distinguishing subtle differences caused by different layers and substrates. Here, we propose a method called Multi-Stage Clustering Algorithm (MSCA). Using the K-means clustering results/metrics for real space in different energy-momentum windows as the input of the second round K-means clustering for momentum space, the energy-momentum windows that exhibit subtle inhomogeneity in real space will be highlighted. It recognizes different types of electronic structures both in real space and momentum space in spatially resolved ARPES dataset. This method can be used to capture the areas of interest, and is especially suitable for samples with complex band dispersions, and can be a practical tool to any high dimensional scientific data analysis.

Recognition of Radix Bupleuri origin using laser-induced breakdown spectroscopy (LIBS) combined with deep learning and machine learning algorithms

In this study, laser-induced breakdown spectroscopy (LIBS) was utilized to classify Radix Bupleuri from six origins to acquire good results. Several feature variable selection methods have been used to preprocess the experimental data on the LIBS. Machine learning and deep learning algorithms were used to build classification models, respectively. The accuracy of all four classification models on the prediction set is higher than 99%. Among them, the classification model constructed by the residual neural network (ResNet) algorithm demonstrated the optimal prediction accuracy both in the full waveform and feature variable selection. For the independent prediction set, the ResNet classification model still demonstrated optimal prediction with 88.33% accuracy. On the importance ranking of feature variables, the elements as Cl, Mg, Fe, Ba, Li, O, and He were considered to play an important role in the identification of the origin of Radix Bupleuri. This study demonstrated that LIBS combined with deep learning and machine learning algorithms can provide a more efficient and reliable method for the origin identification of Radix Bupleuri. In addition, it can be found that the application of deep learning algorithms can realize the prediction output more efficiently and accurately for the analysis of high-dimensional data of LIBS.

Quantum Algorithms for Image Processing: Enhancing Computational Efficiency and Accuracy in High-Dimensional Visual Data Analysis

Quantum algorithms have emerged as a transformative approach to address the computational challenges of high-dimensional image processing tasks. This research investigates the integration of quantum computing principles, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), to optimize processes like edge detection, noise reduction, and image segmentation. These quantum-enhanced techniques promise significant improvements in computational efficiency and accuracy compared to classical methods, especially when handling complex visual data in real-time applications like medical imaging, satellite imagery, and autonomous systems. Additionally, the study explores hybrid quantum-classical architectures to bridge current limitations in noisy intermediate-scale quantum (NISQ) devices, ensuring resource optimization and scalability. The potential of quantum algorithms to provide faster, more precise solutions is evaluated alongside challenges like error mitigation and algorithmic stability. This research highlights the future role of quantum computing in revolutionizing image processing, paving the way for breakthroughs in computer vision and artificial intelligence.

Integrative subgroup analysis for high-dimensional mixed-type multi-response data

Integrative subgroup analysis for high-dimensional mixed-type multi-response data

Water resource vulnerability assessment in Hubei Province: a case study

In view of the different views of academia on the weight allocation of vulnerability assessment indicators, this study creatively proposed a data-based objective evaluation framework of water resource vulnerability, and applied it to the evaluation of water resource vulnerability in Hubei Province. According to the conceptual model of DPSIR proposed by the United Nations, five vulnerability factors are proposed: driving force, pressure, state, influence and response. In this study, 15 indicators were selected and the projection tracing model was used to identify vulnerability. Aiming at the complex problem of optimization calculation of projection index function in the projection tracing model, the accelerated genetic algorithm is used to speed up the optimization speed, solves the optimization problem in the process of projection tracing, and determines the objective weight of all indicators. Example calculation shows that the model can deal with complex multi-index optimization problems, and is an effective way to solve the comprehensive evaluation of complex vulnerability, and the weighting method is important for the evaluation of water resources vulnerability. The results of this paper show that the combination of projection tracing method and machine learning algorithm can improve the efficiency, objectivity and accuracy of high-dimensional data analysis, and can provide scientific basis for policy makers.

Mathematical foundation of High-Dimensional Data Analysis: Leveraging Topology and Geometry for Enhanced Model Interpretability in AI

One of the most important challenges for modern AI and machine learning is the analysis of high-dimensional data. Traditional methods face serious complications in such cases due to high complexity of datasets: the curse of dimensionality, overfitting, and lack of transparency of model behavior. In this paper, we adopt a novel approach to analyze high-dimensional data; topological and geometric techniques will be exploited, taking advantage of better model interpretability and deeper insights into the structure. Precisely, we discuss Topological Data Analysis, mainly Persistent Homology (Edelsbrunner et al., 2002), which allows the extraction of topological features-like loops and connected components that enable the extracting knowledge about the global structure of data. We also see how some concepts of differential geometry and Riemannian geometry (Do Carmo, 1976) can be used to cast light on manifold data structure lying at the heart of any attempt at modeling intrinsic patterns in high-dimensional spaces. We will review how these mathematical pillars, combined with state-of-the-art techniques for dimensionality reduction like t-SNE, UMAP, Principal Component Analysis, are able to provide interpretable and low-dimensional representations of high-dimensional data that can be used to understand models and make decisions. Case studies are also included, which explain the practical working of these methods in AI systems and show how much complex models can be made transparent using these, especially in domains that are very critical, such as healthcare (Caruana et al., 2015), finance (Chen et al., 2018), and autonomous systems ( Wang et al., 2019). We also discuss some of the difficulties in using these methods for practical applications: computational complexity; the need for large-scale data processing (Bengio et al., 2007); and integration of topological and geometric intuition with the rest of the machine learning pipeline (Zhu et al., 2020). We conclude with possible future directions of research toward fine-tuning these methods and exploring their broader applicability to AI in its quest for more robust, interpretable, and reliable AI models. Given this work, we focus on how linking topology, geometry, and AI bears great promise for solving one of today's critical challenges: model interpretability in high-dimensional data analysis.

The Effect of Regularized Regression and Tree-Based Missing Data Imputation Methods on Classification Performance in High Dimensional Data

Missing data is an important problem in the analysis and classification of high dimensional data. The aim of this study is to compare the effects of four different missing data imputation methods on classification performance in high dimensional data. In this study, missing data imputation methods were evaluated using data sets, whose independent variables between mixed correlated with each other, for binary dependent variable, p=500 independent variables, n=150 units and 1000 times running simulation. Missing data structures were created according to different missing rates. Different datasets were obtained by imputing the missing values using different methods. Regularized regression methods such as least absolute shrinkage and selection operator (lasso) and elastic net regression were used for imputation, as well as tree-based methods such as support vector machine and classification and regression trees. At the end of simulation, the classification scores of the methods were obtained by gradient boosting machine and the missing data prediction performances were evaluated according to the distance of these scores from the reference. Our simulation demonstrates that regularized regression methods outperform tree-based methods in classifying high dimensional datasets. Additionally, it was found that the increase in the amount of missing values reduced the classification performance of the methods in high dimensional data.