High-dimensional Data Analysis Research Articles

Unravelling the complexity of environmental exposures and health: A novel exposome-centered framework for occupational and environmental epidemiology

The exposome-centered approach marks a significant shift in occupational and environmental epidemiology, prioritizing a holistic evaluation of environmental exposures throughout an individual's life to elucidate their effects on health. Unlike traditional methods that single out environmental factors for their direct health impacts, this approach seeks an integrated analysis of all non-genetic influences, encompassing specific and general external environments and internal responses. While acknowledging environmental factors' pivotal role in chronic diseases and global mortality, existing methods struggle with precisely measuring and analyzing the broad spectrum of exposures and their combined effects. The exposome framework addresses these gaps through advanced omics technologies, refined exposure assessment models, and comprehensive data analytics. Yet, its application faces hurdles like the requirement for substantial infrastructure, cross-disciplinary training, and new statistical techniques for high-dimensional data analysis. Recent endeavors in applying the exposome concept show its promise in improving our understanding of environmental and occupational health, underlining its importance in advancing epidemiological research and public health. This abstract condenses the rationale, challenges, and ongoing efforts to incorporate the exposome into epidemiol-ogical studies, emphasizing its potential in enhancing the field.

Single-Cell Informatics for Tumor Microenvironment and Immunotherapy.

Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.

Variable selection using data splitting and projection for principal fitted component models in high dimension

Sufficient dimension reduction (SDR) is such an effective way to detect nonlinear relationship between response variable and covariates by reducing the dimensionality of covariates without information loss. The principal fitted component (PFC) model is a way to implement SDR using some class of basis functions, however the PFC model is not efficient when there are many irrelevant or noisy covariates. There have been a few studies on the selection of variables in the PFC model via penalized regression or sequential likelihood ratio test. A novel variable selection technique in the PFC model has been proposed by incorporating a recent development in multiple testing such as mirror statistics and random data splitting. It is highlighted how we construct a mirror statistic in the PFC model using the idea of projection of coefficients to the other space generated from data splitting. The proposed method is superior to some existing methods in terms of false discovery rate (FDR) control and applicability to high-dimensional cases. In particular, the proposed method outperforms other methods as the number of covariates tends to be getting larger, which would be appealing in high dimensional data analysis. Simulation studies and analyses of real data sets have been conducted to show the finite sample performance and the gain that it yields compared to existing methods.

SIMEXBoost: An R package for Analysis of High-Dimensional Error-Prone Data Based on Boosting Method

SIMEXBoost: An R package for Analysis of High-Dimensional Error-Prone Data Based on Boosting Method

The influence of the land-to-sea macroevolutionary transition on vertebral column disparification in Pinnipedia.

The repeated returns of vertebrates to the marine ecosystems since the Triassic serve as an evolutionary model to understand macroevolutionary change. Here we investigate the effects of the land-to-sea transition on disparity and constraint of the vertebral column in aquatic carnivorans (Carnivora; Pinnipedia) to assess how their functional diversity and evolutionary innovations influenced major radiations of crown pinnipeds. We use three-dimensional geometric morphometrics and multivariate analysis for high-dimensional data under a phylogenetic framework to quantify vertebral size and shape in living and extinct pinnipeds. Our analysis demonstrates an important shift in vertebral column evolution by 10-12 million years ago, from an unconstrained to a constrained evolutionary scenario, a point of time that coincides with the major radiation of crown pinnipeds. Moreover, we also demonstrate that the axial skeleton of phocids and otariids followed a different path of morphological evolution that was probably driven by their specialized locomotor strategies. Despite this, we found a significant effect of habitat preference (coastal versus pelagic) on vertebral morphology of crown taxa regardless of the family they belong. In summary, our analysis provides insights into how the land-to-sea transition influenced the complex evolutionary history of pinniped vertebral morphology.

Survival Analysis Based Qos Recommendation for Bus Transportation using Deep Learning

Public transportation play an important role in metropolitan cities.Bus services are an important part of public transportation systems, which are frequently chosen because they are convenient, economical, environmentally beneficial, and less taxing on the infrastructure. The temporal patterns of bus transportation incidents are examined in this study using survival analysis, with a focus on the amount of time until certain events like delays, malfunctions, or safe arrivals at destinations. High-dimensional data is being generated at an increasing rate due to technological advancements. As a result, it is quite difficult to analyze such a dataset adequately. Algorithms for machine learning (ML) have become popular tools for modeling complex and nonlinear interactions in a variety of real-world applications, including as the analysis of high-dimensional survival data. Multilayer Deep Neural Network (DNN) models have achieved impressive results recently. Thus, using Keras and TensorFlow, a Cox-based DNN prediction survival model (DNNSurv model) was created. Its findings, however, were limited to survival datasets with large sample sizes or high dimensionality. Using high dimensional survival data, the proposed work will evaluate the DNNSurv model's prediction ability and compare it to a well-known machine learning survival prediction model (random survival forest). The Proposed Work also offers the best settings for a number of hyperparameters, including tuning parameter selection, for this reason. The suggested approach showed through data analysis that, when compared to the ML model, the DNNSurv model performed better overall in terms of the two primary survival prediction evaluation measures (i.e., the concordance index and the time-dependent AUC).

Abstract 3858: Peripheral CD38+ effector memory (EM) T cells are dynamic biomarkers of response to neoadjuvant immune checkpoint blockade (NeoICB) in patients (pts) with resectable dedifferentiated liposarcoma (DDLPS) and undifferentiated pleomorphic sarcoma (UPS)

Abstract Introduction: NeoICB is a promising approach for pts with DDLPS and UPS. However, biomarkers to identify which pts benefit from this treatment and understanding the mechanisms of response and resistance to ICB remains a challenge. Methods: We conducted a randomized phase 2 trial of neoICB (anti-PD1 +/- CTLA4) in pts with resectable DDLPS (n=17) and UPS (n=10). Pts with UPS received concurrent neoICB and radiation. The primary endpoint of the trial was pathological response; 3 DDLPS and 9 UPS pts were responders (R) and 14 DDLPS and 1 UPS pts were non-responders (NR). Peripheral blood mononuclear cells (PBMCs) were collected at baseline, prior to cycle 2 of neoICB (C2D1), and at the time of surgery (SURG). We performed mass cytometry (CyTOF) to identify immune cells associated with response and their dynamic changes with neoICB. We designed a custom 41-parameter panel using CD45 live cell barcoding with 31 markers for immune profiling, of which 23 were selected to detect key cell lineage and 8 to detect signaling markers. Batch correction and high dimensional data analyses were performed using CyCombine and CyTOF workflow R packages. Longitudinal comparisons between immune cell populations were conducted using Mann-Whitney U Test. Results: Of 79 available PBMC samples from 27 pts, 63 passed quality control after thawing (≥30% viability) and data clean-up (at least 10,000 events). Data was down-sampled to 5000 events for analysis. Of 19 immune cell types identified, monocytes were the most common followed by CD4+ and CD8+ T cells. There was an early increase in CD38+ effector/effector memory (EM) T cells at C2D1 in pts with DDLPS (CD8+ p=0.005, CD4+ p<0.001) and a similar increase observed in pts with UPS (CD8+ C2D1 p=NS, SURG p=0.034; CD4+ C2D1 p=0.024, SURG p=0.06) and these cells had higher Ki67 expression than CD38- T cells (p<0.005). At baseline, R pts with DDLPS had higher fractions of CD38+ EM CD8+ T cells than NR pts (p=0.044). Analysis in the UPS cohort was limited by the low number of NR pts. Higher expression of TIGIT was observed on CD38+ EM CD8+ T cells compared to CD38- counterparts at all time points. Conclusion: Peripheral CD38+ EM CD8+ T cells are associated with response to neoICB in pts with DDLPS. Longitudinal peripheral immune profiling reveals an increase in CD38+ EM CD8+ and CD4+ T cells with neoICB in pts with DDLPS and UPS. These cells are proliferative and express higher levels of TIGIT, which could be a future target for therapeutic exploration. Importantly, changes in the peripheral immune populations may precede changes in the tumor microenvironment (TME) as preliminary analyses of the TME did not identify early (C2D1) changes. TME imaging mass cytometry analyses are currently underway to associate peripheral and intratumor changes in immune populations. Citation Format: Elise F. Nassif, Hsinyi Lu, Michael Stanford, Karen Millerchip, Diana Romero, Jared Malke, Brenna Matejka, Jeffrey E. Gershenwald, Susmita Kumari, Duncan H. Mak, Angelique J. Lin, Jared K. Burks, Wei-Lien Wang, Alexander J. Lazar, Neeta Somaiah, Cara Haymaker, Christina L. Roland, Emily Z. Keung. Peripheral CD38+ effector memory (EM) T cells are dynamic biomarkers of response to neoadjuvant immune checkpoint blockade (NeoICB) in patients (pts) with resectable dedifferentiated liposarcoma (DDLPS) and undifferentiated pleomorphic sarcoma (UPS) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3858.

Neural network big data fusion in remote sensing image processing technology

Abstract Remote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve the existing problems in RS image processing, this study dealt with ConvNext-convolutional neural network (CNN) and big data (BD) in parallel. Moreover, it combined the existing RS image processing with the high dimensional analysis of data and other technologies. In this process, the parallel processing of large data and high-dimensional data analysis technology improves the difficulty and low efficiency of large-scale RS image data processing in the preprocessing stage. The ConvNext-CNN optimizes the two modules of feature extraction and object detection in RS image processing, which improves the difficult problem of complex background recognition and improves the accuracy of RS image processing. At the same time, the performance of RS image processing technology after neural networks (NNs) and BD fusion and traditional RS image processing technology in many aspects are analyzed by experiments. In this study, traditional RS image processing and RS image processing combined with NN and BD were used to process 2,328 sample datasets. The image processing accuracy and recall rate of traditional RS image processing technology were 81 and 82%, respectively, and the F1 score was about 0.81 (F1 value is the reconciled average of accuracy and recall, a metric that combines accuracy and recall to evaluate the quality of the results, a higher F1 value indicates a better overall performance of the retrieval system). The accuracy rate and recall rate of RS image processing technology, which integrates NN and BD, were 97 and 98%, respectively, and its F1 score was about 0.97. After analyzing the process of these experiments and the final output results, it can be determined that the RS image processing technology combined with NN and BD can improve the problems of large-scale data processing difficulty, recognition difficulty under complex background, low processing accuracy and efficiency. In this study, the RS image processing technology combined with NN and BD has stronger adaptability with the help of NN and BD technology, and can adjust parameters and can be applied in more tasks.

Variable selection and subgroup analysis for high-dimensional censored data

This paper proposes a penalized method for high-dimensional variable selection and subgroup identification in the Tobit model. Based on Olsen's [(1978). Note on the uniqueness of the maximum likelihood estimator for the Tobit model. Econometrica: Journal of the Econometric Society, 46(5), 1211–1215. https://doi.org/10.2307/1911445] convex reparameterization of the Tobit negative log-likelihood, we develop an efficient algorithm for minimizing the objective function by combining the alternating direction method of multipliers (ADMM) and generalised coordinate descent (GCD). We also establish the oracle properties of our proposed estimator under some mild regularity conditions. Furthermore, extensive simulations and an empirical data study are conducted to demonstrate the performance of the proposed approach.

Double sparsity garrotized kernel machine in high-dimensional partially linear model

Obtaining excellent prediction accuracy in the high-dimensional partially linear model is particularly important. However, it is difficult to achieve due to the complex relationship between nonparametric covariates and the response. Irrelevant covariates and unimportant data also commonly attenuate the prediction performance of the model. Further, high-dimensional data analysis is challenging for modern statistical studies. To overcome these difficulties, we propose the Double Sparsity Garrotized Kernel Machine (DSGKM) method with an efficient algorithm and its adjusted version for prediction. Specifically, we estimate the nonparametric components using the kernel machine technique, and impose L 1 -norm penalties simultaneously to select relevant covariates and retain the representative data in the final model. Besides, the convergence analysis of the adjusted algorithm is conducted. The advantages of our method are: (i) to sufficiently capture the complex relationship between nonparametric covariates and the response; (ii) to identify relevant covariates and select representative data; and (iii) to achieve higher computational efficiency, especially the situations when both parametric and nonparametric components are high-dimensional. Results on both simulated and real data show that the proposed method outperforms existing methods, even when outliers exist.

Covariance Matrix Estimation for High-Throughput Biomedical Data with Interconnected Communities

Estimating a covariance matrix is central to high-dimensional data analysis. Empirical analyses of high-dimensional biomedical data, including genomics, proteomics, microbiome, and neuroimaging, among others, consistently reveal strong modularity in the dependence patterns. In these analyses, intercorrelated high-dimensional biomedical features often form communities or modules that can be interconnected with others. While the interconnected community structure has been extensively studied in biomedical research (e.g., gene co-expression networks), its potential to assist in the estimation of covariance matrices remains largely unexplored. To address this gap, we propose a procedure that leverages the commonly observed interconnected community structure in high-dimensional biomedical data to estimate large covariance and precision matrices. We derive the uniformly minimum variance unbiased estimators for covariance and precision matrices in closed forms and provide theoretical results on their asymptotic properties. Our proposed method enhances the accuracy of covariance- and precision-matrix estimation and demonstrates superior performance compared to the competing methods in both simulations and real data analyses.

MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete Data

MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete Data

Supervised Clustering of Persian Handwritten Images Using Regularization and Dimension Reduction Methods

Clustering, as a fundamental exploratory data technique, not only is used to discover patterns and structures in complex datasets but also is utilized to group variables in high-dimensional data analysis. Dimension reduction through clustering helps identify important variables and reduce data dimensions without losing significant information. High-dimensional image datasets, such as Persian handwritten images, have numerous pixels, making statistical inference difficult. Such high-dimensionality property pose challenges for analysis and processing, requiring specialized techniques like clustering to extract information. Incorporating response variable information enhances clustering analysis, transforming it into a supervised method. This article evaluates a supervised clustering approach using Ridge and Lasso penalties, comparing them in analyzing a real dataset while identifying important variables. We demonstrate that despite choosing a small number of variables as important variables, Lasso penalty performs relatively well in predicting the labels of new observations for this multi-class dataset.

Lithium-ion battery state of health estimation method based on variational quantum algorithm optimized stacking strategy

Accurate state-of-health (SOH) estimation is critical for the performance and safety of lithium-ion batteries. An innovative method for SOH estimation is proposed by employing a variational quantum algorithm to optimize a stacking integrated learning strategy. The strategy effectively combines multiple model advantages, enhancing the estimation accuracy and generalizability. Using this method, eight sets of health factors are extracted, focusing on the relationship between battery capacity degradation and electrothermal parameters. A stacking integrated learning framework is developed by utilizing diverse primary learners to effectively capture the dynamic changes in health factors. A ridge regression meta-learner is incorporated to address overfitting problems found in primary learners. A significant innovation is the integration of a variational quantum circuit module as the primary learner. This module plays a crucial role in optimizing the hyperparameters for the analysis of complex and high-dimensional battery data. The effectiveness of the method is validated using four different types of batteries, showing a 77.4% improvement in prediction accuracy compared with traditional methods, with the SOH estimation error maintained within a tight margin of 0.67%. The mean absolute error, mean absolute percentage error, and root mean square error with maximum reduction rates are 76.7%, 77.4%, and 62.7%, respectively. The maximum increase in the R-squared coefficient is 5.3%. This study demonstrates the potential of variational quantum algorithms in enhancing the SOH estimation accuracy and opens new possibilities for the advanced health status management of lithium-ion batteries.

The impact of Bayesian optimization on feature selection

Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature selection methods itself depends on hyper-parameters. Bayesian optimization has demonstrated its advantages in automatically configuring the settings of hyper-parameters for various models. However, it remains unclear whether Bayesian optimization can benefit feature selection methods. In this research, we conducted extensive simulation studies to compare the performance of various feature selection methods, with a particular focus on the impact of Bayesian optimization on those where hyper-parameters tuning is needed. We further utilized the gene expression data obtained from the Alzheimer's Disease Neuroimaging Initiative to predict various brain imaging-related phenotypes, where various feature selection methods were employed to mine the data. We found through simulation studies that feature selection methods with hyper-parameters tuned using Bayesian optimization often yield better recall rates, and the analysis of transcriptomic data further revealed that Bayesian optimization-guided feature selection can improve the accuracy of disease risk prediction models. In conclusion, Bayesian optimization can facilitate feature selection methods when hyper-parameter tuning is needed and has the potential to substantially benefit downstream tasks.

Multimodal integration of neuroimaging and genetic data for the diagnosis of mood disorders based on computer vision models

Mood disorders, particularly major depressive disorder (MDD) and bipolar disorder (BD), are often underdiagnosed, leading to substantial morbidity. Harnessing the potential of emerging methodologies, we propose a novel multimodal fusion approach that integrates patient-oriented brain structural magnetic resonance imaging (sMRI) scans with DNA whole-exome sequencing (WES) data. Multimodal data fusion aims to improve the detection of mood disorders by employing established deep-learning architectures for computer vision and machine-learning strategies. We analyzed brain imaging genetic data of 321 East Asian individuals, including 147 patients with MDD, 78 patients with BD, and 96 healthy controls. We developed and evaluated six fusion models by leveraging common computer vision models in image classification: Vision Transformer (ViT), Inception-V3, and ResNet50, in conjunction with advanced machine-learning techniques (XGBoost and LightGBM) known for high-dimensional data analysis. Model validation was performed using a 10-fold cross-validation. Our ViT ⊕ XGBoost fusion model with MRI scans, genomic Single Nucleotide polymorphism (SNP) data, and unweighted polygenic risk score (PRS) outperformed baseline models, achieving an incremental area under the curve (AUC) of 0.2162 (32.03% increase) and 0.0675 (+8.19%) and incremental accuracy of 0.1455 (+25.14%) and 0.0849 (+13.28%) compared to SNP-only and image-only baseline models, respectively. Our findings highlight the opportunity to refine mood disorder diagnostics by demonstrating the transformative potential of integrating diverse, yet complementary, data modalities and methodologies.

Estimation of multiple networks with common structures in heterogeneous subgroups

Network estimation has been a critical component of high-dimensional data analysis and can provide an understanding of the underlying complex dependence structures. Among the existing studies, Gaussian graphical models have been highly popular. However, they still have limitations due to the homogeneous distribution assumption and the fact that they are only applicable to small-scale data. For example, cancers have various levels of unknown heterogeneity, and biological networks, which include thousands of molecular components, often differ across subgroups while also sharing some commonalities. In this article, we propose a new joint estimation approach for multiple networks with unknown sample heterogeneity, by decomposing the Gaussian graphical model (GGM) into a collection of sparse regression problems. A reparameterization technique and a composite minimax concave penalty are introduced to effectively accommodate the specific and common information across the networks of multiple subgroups, making the proposed estimator significantly advancing from the existing heterogeneity network analysis based on the regularized likelihood of GGM directly and enjoying scale-invariant, tuning-insensitive, and optimization convexity properties. The proposed analysis can be effectively realized using parallel computing. The estimation and selection consistency properties are rigorously established. The proposed approach allows the theoretical studies to focus on independent network estimation only and has the significant advantage of being both theoretically and computationally applicable to large-scale data. Extensive numerical experiments with simulated data and the TCGA breast cancer data demonstrate the prominent performance of the proposed approach in both subgroup and network identifications.

Analysis of High-Dimensional and Complex Data, such as Genomic Data, Neuroimaging Data, and Text Data, Using Machine Learning and Dimension Reduction Techniques in Pakistan

Analysis of High-Dimensional and Complex Data, such as Genomic Data, Neuroimaging Data, and Text Data, Using Machine Learning and Dimension Reduction Techniques in Pakistan

Enrichment of circulating helper-T (Th) and unswitched memory-B (NSwM-B) cell populations and responsiveness to immune checkpoint blockade (ICB) in microsatellite-stable (MSS)/mismatch repair-proficient (pMMR) colorectal cancer (CRC).

150 Background: The anti-tumor activity of ICB in MSS/pMMR-CRC is limited. However, recent studies have highlighted the possibility of inducing ICB responsiveness by oxaliplatin-based chemotherapy. The understanding of underlying mechanisms and identification of rational biomarkers are major focus areas in immuno-oncology. Methods: Patients with previously untreated, unresectable abdominal metastases from MSS/pMMR-CRC were randomly assigned to the oxaliplatin-based standard Nordic FLOX regimen (control arm) or 2 FLOX cycles followed by 2 ICB (nivolumab) cycles in a repeat sequential schedule (experimental arm). Radiologic response assessment was done every 8 weeks, and the study arms reached identical primary endpoint – median progression-free survival (PFS) 9.3 months. Experimental-arm patients at one study center had peripheral blood mononuclear cells (PBMC) prepared at study entry (V1), after the initial 2 FLOX cycles (V3), and after the succeeding 2 nivolumab cycles (V5). The PBMC were incubated with 19 cell-surface antibodies and analyzed by mass cytometry. Patients were categorized in PFS >9.3 months (improved; n = 7) or PFS <9.3 months (shortened; n = 6). Live cell clusters were identified by hierarchically applying automated 1D gates to the available lineage markers. After gating, all cells were assigned to soft clusters according to phenotypes. The cluster sizes were used to compute the differential abundance of immune-cell populations and a Peacock test assessed multivariate changes between the improved and shortened PFS groups. Results: Circulating CD4+ (Th) cells and CD27+IgD+ (NSwM-B) cells were concurrently enriched in the improved PFS group. After the initial 2 FLOX cycles (V3), median increase (from V1) was 2.89% (of lymphocyte count) for Th cells and 0.54% (of leukocyte count) for NSwM-B cells (p < 0.001). Continued responses occurred over the succeeding 2 nivolumab cycles (until V5) with median increase of 1.19% for Th cells and 0.19% for NSwM-B cells (p < 0.001). The association between the immune-cell alterations and PFS was validated by categorizing the cases in above or below the median increase in Th and NSwM-B cell populations from V1 to V5. The high-increase group achieved median PFS 15.0 months (95% CI, 10.8-19.2) compared to 4.0 months (95% CI, 1.9-22.1) for the low-increase group (log-rank; p = 0.002). Conclusions: This unsupervised analysis of high-dimensional PBMC data from a limited case number revealed that circulating Th and NSwM-B cell populations may mediate ICB responsiveness invoked by short-course oxaliplatin-based chemotherapy in patients with abdominal metastases from MSS/pMMR-CRC. Owing to weaknesses of such a post-hoc analysis, further studies are needed to confirm the generalizability of these findings. Clinical trial information: NCT003388190 .

Feature Extraction and Classification Analysis of High-Dimensional Biological Data Based on Dimensionality Reduction Fusion Method

Identification and extraction of characterized information from complex high-dimensional biological data is a very meaningful issue. The dimensionality reduction fusion method based on random forest, feature extraction and neural network is proposed to recognize and classify two datasets of mRNA and lncRNA. It is shown that the proposed fusion method achieved accurate identification/classification of cancer and non-cancer groups, and simultaneously selected identity variables that have biological relevance to lung cancer (tumor) as potential biomarkers from a large number of variables. It is considered as an effective tool and theoretical support for lung cancer identification in clinical application, and it can be extended to other kinds of cancer or biological data. Ultimately, an advanced method for feature extraction and classification analysis of high-dimensional data is provided.