Integrated Analysis Method Research Articles

Closed Circuit of 3-Dimensional Polymer Powders

This article describes the most important stages of testing the levels of innovative readiness of a new machine and material construction solutions for closed loops of polymer powders in 3D additive manufacturing. The aim of this study was to indicate the state and directions of development of the technology of geometric recycling of polymer powders in 3D additive manufacturing, the need to control the geometric quality of the powders, and the quality of the design of machines for their processing in a closed circuit. The method was aimed at creating a strategy and verifying the stages of development of a new idea for stabilizing the geometric and dynamic design features of polymer powders for additive manufacturing (3D printing). Connections and relationships of the variable design features of powders, machines and devices with variable postulated states of products and processes allowed for the analysis, knowledge, description and development of variables for stabilizing geometric features of polymer powders in recycling. The results show the possibilities of supporting innovative creativity according to the adopted method, allow for determining the level of compliance of the quality of the products of the technical system, the effectiveness of preparatory processes for recycling and the non-toxicity of the products and processes of the new technology and their closed loop. The goal of developing useful design values, according to an integrated method of analysis, assessment and environmental development, was achieved, including the proposal of design features of razor blade shredders, supported by genetic algorithms, and devices for stabilizing polymer powders in additive manufacturing with recycling.

A continuous-discontinuous coupling computational method for multi-material mixtures

Considering the important role of particle-reinforced composites (PRCs), and to accurately assess the mechanical properties of PRC with arbitrarily complex internal structures, this paper proposes a new continuous-discontinuous coupled computational method, i.e., the multi-material integrated analysis (MIA) method, by combining the advantages of the discontinuous deformation analysis (DDA) method, the numerical manifold method (NMM), and the meshless method, for the continuous-discontinuous dual state of this kind of materials when they are subjected to forces. The core idea of this method is by establishing a single physical cover (PC) on each particle and defining a unified and piecewise approximation function over it to achieve displacement coordination between the particles and the matrix. In this model, the particles can maintain the contact and separation characteristics like the blocks, while the matrix is able to achieve the bonding function by overlapping different covers. Two numerical integration schemes, namely single-point Gaussian integration and Monte Carlo integration, are proposed to address the integration issues in stiffness matrix calculations. Meanwhile by considering the similarity between NMM and meshless method interpolation function construction, this paper combines circular coverage and 0th-order moving least squares to construct the interpolation function to form the displacement approximation function for the whole solution space. Moreover, the method of lattice retrieval (also Nearest Neighbor Search, NNS) is invoked for particle contact determination to improve the efficiency of contact and coverage relationship determination. The validity of the model is verified by taking asphalt mixture, a typical PRC, as a demonstrative case study. The results show that the MIA method solves the one-sided problem that the existing algorithms usually only consider the continuous or discontinuous properties of PRC, and provides a new method for comprehensively analyzing the micromechanical response of PRC and solving its large deformation problem.

HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity.

Epidemiologic and genetic studies in many complex diseases suggest subgroup disparities (e.g. by sex, race) in disease course and patient outcomes. We consider this from the standpoint of integrative analysis where we combine information from different views (e.g. genomics, proteomics, clinical data). Existing integrative analysis methods ignore the heterogeneity in subgroups, and stacking the views and accounting for subgroup heterogeneity does not model the association among the views. We propose Heterogeneity in Integration and Prediction (HIP), a statistical approach for joint association and prediction that leverages the strengths in each view to identify molecular signatures that are shared by and specific to a subgroup. We apply HIP to proteomics and gene expression data pertaining to chronic obstructive pulmonary disease (COPD) to identify proteins and genes shared by, and unique to, males and females, contributing to the variation in COPD, measured by airway wall thickness. Our COPD findings have identified proteins, genes, and pathways that are common across and specific to males and females, some implicated in COPD, while others could lead to new insights into sex differences in COPD mechanisms. HIP accounts for subgroup heterogeneity in multi-view data, ranks variables based on importance, is applicable to univariate or multivariate continuous outcomes, and incorporates covariate adjustment. With the efficient algorithms implemented using PyTorch, this method has many potential scientific applications and could enhance multiomics research in health disparities. HIP is available at https://github.com/lasandrall/HIP, a video tutorial at https://youtu.be/O6E2OLmeMDo and a Shiny Application at https://multi-viewlearn.shinyapps.io/HIP_ShinyApp/ for users with limited programming experience.

MorphLink: Bridging Cell Morphological Behaviors and Molecular Dynamics in Multi-modal Spatial Omics.

Multi-modal spatial omics data are invaluable for exploring complex cellular behaviors in diseases from both morphological and molecular perspectives. Current analytical methods primarily focus on clustering and classification, and do not adequately examine the relationship between cell morphology and molecular dynamics. Here, we present MorphLink, a framework designed to systematically identify disease-related morphological-molecular interplays. MorphLink has been evaluated across a wide array of datasets, showcasing its effectiveness in extracting and linking interpretable morphological features with various molecular measurements in multi-modal spatial omics analyses. These linkages provide a transparent depiction of cellular behaviors that drive transcriptomic heterogeneity and immune diversity across different regions within diseased tissues, such as cancer. Additionally, MorphLink is scalable and robust against cross-sample batch effects, making it an efficient method for integrative spatial omics data analysis across samples, cohorts, and modalities, and enhancing the interpretation of results for large-scale studies.

ScParser: sparse representation learning for scalable single-cell RNA sequencing data analysis

The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes. Notably, the extended scParser pinpoints biological processes in cell subpopulations that contribute to disease pathogenesis. scParser achieves favorable performance in cell clustering compared to state-of-the-art methods and has a broad and diverse applicability.

Bootstrap Evaluation of Association Matrices (BEAM) for Integrating Multiple Omics Profiles with Multiple Outcomes.

Large datasets containing multiple clinical and omics measurements for each subject motivate the development of new statistical methods to integrate these data to advance scientific discovery. We propose bootstrap evaluation of association matrices (BEAM), which integrates multiple omics profiles with multiple clinical endpoints. BEAM associates a set omic features with clinical endpoints via regression models and then uses bootstrap resampling to determine statistical significance of the set. Unlike existing methods, BEAM uniquely accommodates an arbitrary number of omic profiles and endpoints. In simulations, BEAM performed similarly to the theoretically best simple test and outperformed other integrated analysis methods. In an example pediatric leukemia application, BEAM identified several genes with biological relevance established by a CRISPR assay that had been missed by univariate screens and other integrated analysis methods. Thus, BEAM is a powerful, flexible, and robust tool to identify genes for further laboratory and/or clinical research evaluation. Source code, documentation, and a vignette for BEAM are available on GitHub at: https://github.com/annaSeffernick/BEAMR . The R package is available from CRAN at: https://cran.r-project.org/package=BEAMR . Stanley.Pounds@stjude.org. Supplementary data are available at the journal's website.

An integrated approach to analyzing matrix-crack-induced stiffness degradation and medium leakage in linerless composite vessels

This paper proposes an integrated analysis method for linerless composite vessels considering matrix-crack-induced stiffness degradation and permeation. Stiffness degradation models are obtained using machine learning regression, the data sets of which consist of finite element analysis (FEA) results and virtual samples. Then, the effective leakage conductance is obtained by using the linear elastic fracture mechanics and the micro-cracking leakage mechanism. The proposed integrated method is applied to analyze vessel structures designed by the grid theory, which has variable ply thicknesses and layup angles. Simulation results show that whether the structure at cryogenic temperature cracks and leaks more easily depends on its dimensions and stacking sequences.

Strength analysis method of CMCs tip shroud structure considering random distribution of preform properties

This paper considers the impact of the random distribution of yarn properties on the mechanical and failure behavior of ceramic matrix composites (CMCs) structures and proposes an integrated analysis method based on preform-structure. Tensile tests of CMCs tip shroud specimens were carried out, and the digital image correlation technique was used to record the deformation of the specimens in real-time during loading. Based on the actual structure, a preform model of the CMCs tip shroud was established, and the kernel density estimation method was used to obtain the distribution of the yarn cross-sectional area. The random distribution of the cross-sectional area is equivalent to material properties, simulating the dispersion of the performance of CMCs. The progressive damage method simulates the failure mode during the specimen's loading process. The peak load error is 1.5 %, and the failure modes such as transverse shear damage, stitching yarn fracture, and interlaminar separation were successfully predicted.

A Curved Surface Integral Method for Reliability Analysis of Multiple Failure Modes System with Non-Overlapping Failure Domains

Abstract In the study of reliability of systems with multiple failure modes, approximations can be obtained by calculating the probability of failure for each state function. The first-order reliability method and the second-order reliability method are effective, but they may introduce significant errors when dealing with certain nonlinear situations. Simulation methods such as line sampling method and response surface method can solve implicit function problems, but the large amount of calculation results in low efficiency. The curved surface integral method (CSI) has good accuracy in dealing with nonlinear problems. Therefore, a system reliability analysis method (CSIMMS) is proposed on the basis of CSI for solving multiple failure modes system reliability problems with non-overlapping failure domains. The order of magnitude of the failure probability is evaluated based on the reliability index and the degree of nonlinearity, ignoring the influence of low order of magnitude failure modes, and reducing the calculation of the system failure probability. Finally, CSIMMS and other methods are compared by three numerical examples, and the results show the stability and accuracy of the proposed method.

High DOCK1 expression identifies a distinct prognostic subgroup of pediatric acute myeloid leukemia: Results of the Japanese Pediatric Leukemia/Lymphoma Study Group AML-05 trial.

The molecular pathogenesis of acute myeloid leukemia (AML) was dramatically clarified over the latest two decades. Several important molecular markers were discovered in patients with AML that have helped to improve the risk stratification. However, developing new treatment strategies for relapsed/refractory acute myeloid leukemia (AML) is crucial due to its poor prognosis. To overcome this difficulty, we performed an assay for transposase-accessible chromatin with sequencing (ATAC-seq) in 10 AML patients with various gene alterations. ATAC-seq is based on direct in vitro sequencing adaptor transposition into native chromatin, and is a rapid and sensitive method for integrative epigenomic analysis. ATAC-seq analysis revealed increased accessibility of the DOCK1 gene in patients with AML harboring poor prognostic factors. Following the ATAC-seq results, quantitative reverse transcription polymerase chain reaction was used to measure DOCK1 gene expression levels in 369 pediatric patients with de novo AML. High DOCK1 expression was detected in 132 (37%) patients. The overall survival (OS) and event-free survival (EFS) among patients with high DOCK1 expression were significantly worse than those patients with low DOCK1 expression (3-year EFS: 34% vs. 60%, p<.001 and 3-year OS: 60% vs. 80%, p<.001). To investigate the significance of high DOCK1 gene expression, we transduced DOCK1 into MOLM14 cells, and revealed that cytarabine in combination with DOCK1 inhibitor reduced the viability of these leukemic cells. Our results indicate that a DOCK1 inhibitor might reinforce the effects of cytarabine and other anti-cancer agents in patients with AML with high DOCK1 expression.

Twin Scholarships of Glycomedicine and Precision Medicine in Times of Single-Cell Multiomics.

Systems biology and multiomics research expand the prospects of planetary health innovations. In this context, this mini-review unpacks the twin scholarships of glycomedicine and precision medicine in the current era of single-cell multiomics. A significant growth in glycan research has been observed over the past decade, unveiling and establishing co- and post-translational modifications as dynamic indicators of both pathological and physiological conditions. Systems biology technologies have enabled large-scale and high-throughput glycoprofiling and access to data-intensive biological repositories for global research. These advancements have established glycans as a pivotal third code of life, alongside nucleic acids and amino acids. However, challenges persist, particularly in the simultaneous analysis of the glycome and transcriptome in single cells owing to technical limitations. In addition, holistic views of the complex molecular interactions between glycomics and other omics types remain elusive. We underscore and call for a paradigm shift toward the exploration of integrative glycan platforms and analysis methods for single-cell multiomics research and precision medicine biomarker discovery. The integration of multiple datasets from various single-cell omics levels represents a crucial application of systems biology in understanding complex cellular processes and is essential for advancing the twin scholarships of glycomedicine and precision medicine.

An improved approximate integral method for nonlinear reliability analysis

In order to evaluate the failure probability corresponding to the Limit State Function (LSF) in structural reliability, the First Order Reliability Method (FORM) linearizes the LSF and directly calculates the failure probability based on the Most Probable Point (MPP). But this method is unable to effectively handle nonlinear problems. The Second Order Reliability Method (SORM), on the other hand, utilizes the Hessian matrix to obtain curvature information at the MPP and computes the failure probability with Breitung's second-order approximation formula. However, SORM is unable to maintain satisfactory computational accuracy in highly nonlinear problems due to its lack of detailed analysis on the shape of the limit state surface. To address this issue, this paper proposes an Improved Approximation Integration Method (IAIM). The initial approximation of the limit state surface is based on the intersection region between the hypersphere and the n-dimensional paraboloid. Subsequently, a statistical analysis of the deviation between the aforementioned region and its projected area is conducted to construct a corresponding fitting function, which is then utilized for dimensionality reduction in the multidimensional integration of the approximation area. Finally, several examples are presented to demonstrate the accuracy and feasibility of the proposed IAIM.

Integrated Geophysical Models for Interpretations of Heterogeneous Subsurface Environments

Near-surface characterization can be vulnerable to misinterpretation due to limited resolution and methodological limitations. Validation from different parameters is necessary to substantiate the results and reduce errors during interpretations. This research aims to develop an alternative integrated method of a 2-D cross-plot model to enhance subsurface interpretations based on the model's criteria, resulting in better geological interpretation. Geophysical methods such as electrical resistivity and seismic refraction are utilized in a test model and a case study area to observe the capabilities of the integrated method approach. The goal of this study was to cluster two or more different parameters into a single model for a direct presentation of the subsurface model. This includes the characterization of subsurface properties, data integration using cross-plot analysis, and the development of a 2-D cross-plot model. This method visually represents the relationship between two or more attributes, allowing for the identification of anomalies. In the test model, the lithology consists of sandy silt derived from granitic residual soil, whereas in the case study model, it was dominated by weathered granitic residual soil with the presence of saturated zones. The 2-D cross-plot model provides a comprehensive interpretation where Quadrant, Q1 shows the vulnerable zones for sliding mass bodies. The plane weakness was successfully identified based on the cross-plot model as the weathered zone, and subsurface features were determined. The estimated volume of mass movement was successfully calculated for the case study area based on the determination of the sliding plane. The integrated method of cross-plot analysis along with the development of 2-D cross-plot models proves to be an informative approach for subsurface characterization and enhances subsurface imaging.

Joint multi-omics discriminant analysis with consistent representation learning using PANDA.

Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis-based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods. Herein we present PAN-omics Discriminant Analysis (PANDA), a joint discriminant analysis method that seeks omics-specific discriminant common spaces by jointly learning consistent discriminant latent representations for each omics. PANDA jointly maximizes between-class and minimizes within-class omics variations in a common space and simultaneously models the relationships among omics at the consistency representation and cross-omics correlation levels, overcoming the need for compromise between discrimination and correlation as with the existing integrative multi-omics methods. Because of the consistency representation learning incorporated into the objective function of PANDA, this method seeks a common discriminant space to minimize the differences in distributions among omics, can lead to a more robust latent representations than other methods, and is against the inconsistency of the different omics. We compared PANDA to 10 other state-of-the-art multi-omics data integration methods using both simulated and real-world multi-omics datasets and found that PANDA consistently outperformed them while providing meaningful discriminant latent representations. PANDA is implemented using both R and MATLAB, with codes available at https://github.com/WuLabMDA/PANDA.

A statistical integrative analysis method for microRNA and gene expression

AbstractMicroRNA (miRNA), a type of non‐coding RNA, is known to suppress the expression of target genes and there is an increasing number of studies analyzing various miRNAs in cancer. In an integrated analysis of miRNA and gene expressions, it is often difficult to obtain a significant result with a small amount of data. In this study, we proposed a method that allows integrated analysis even with a small amount of miRNA and gene expression data, and examined its validity. The method is based on a group comparison between target genes having specific functions controlled by a specific miRNA and a background group of randomly selected genes. We conducted a simulation to verify the validity of the proposed method. The simulation results indicated that the method can detect significant differences in expression of target genes controlled by a miRNA showing significant expression change. We then applied the proposed method to real data derived from multiple myeloma cells. As a result, some functions were detected as significantly changed due to a significant change of miRNA.

Identifying potential drug targets for varicose veins through integration of GWAS and eQTL summary data.

Varicose veins (VV) are a common chronic venous disease that is influenced by multiple factors. It affects the quality of life of patients and imposes a huge economic burden on the healthcare system. This study aimed to use integrated analysis methods, including Mendelian randomization analysis, to identify potential pathogenic genes and drug targets for VV treatment. This study conducted Summary-data-based Mendelian Randomization (SMR) analysis and colocalization analysis on data collected from genome-wide association studies and cis-expression quantitative trait loci databases. Only genes with PP.H4 > 0.7 in colocalization were chosen from the significant SMR results. After the above analysis, we screened 12 genes and performed Mendelian Randomization (MR) analysis on them. After sensitivity analysis, we identified four genes with potential causal relationships with VV. Finally, we used transcriptome-wide association studies and The Drug-Gene Interaction Database data to identify and screen the remaining genes and identified four drug targets for the treatment of VV. We identified four genes significantly associated with VV, namely, KRTAP5-AS1 [Odds ratio (OR) = 1.08, 95% Confidence interval (CI): 1.05-1.11, p = 1.42e-10] and PLEKHA5 (OR = 1.13, 95% CI: 1.06-1.20, p = 6.90e-5), CBWD1 (OR = 1.05, 95% CI: 1.01-1.11, p = 1.42e-2) and CRIM1 (OR = 0.87, 95% CI: 0.81-0.95, p = 3.67e-3). Increased expression of three genes, namely, KRTAP5-AS1, PLEKHA5, and CBWD1, was associated with increased risk of the disease, and increased expression of CRIM1 was associated with decreased risk of the disease. These four genes could be targeted for VV therapy. We identified four potential causal proteins for varicose veins with MR. A comprehensive analysis indicated that KRTAP5-AS1, PLEKHA5, CBWD1, and CRIM1 might be potential drug targets for varicose veins.

Auxiliary diagnosis and prognosis evaluation of KIF4A, RAD51AP1 and CDKN3 in esophageal cancer

To investigate the expression of mRNA in esophageal cancer (ESCA) tissues and its potential and diagnostic and prognostic value by high-throughput sequencing data. Using the Cancer Genome Atlas Program (TCGA) database in USA by integrative bioinformatics analysis methods, the gene expression profiles and clinical data of 173 patients with ECSA were collected. The mRNA expression levels in ESCA tissue and para-cancerous tissue samples were analyzed using DESeq2, edgeR and limma to screen the differentially expressed genes (DEGs). DEGs-related protein network diagrams were drawn. GO and KEGG function enrichment analysis were performed and the hub genes were screened and the survival analysis of hub genes was analyzed. Genes related to the prognosis of ESCA were selected and their prognostic value in ESCA was analyzed. Finally, the receiver operating characteristic curve was drawn to evaluate its diagnostic value. The results showed that using TCGA cancer data, a total of 620 up-regulated DEGs and 668 down-regulated DEGs with significant differential expression between ESCA and para-cancerous tissues were screened. DEGs were mainly involved in receptor complexes, ubiquitin ligase complexes, etc., playing GTPase activity, phospholipid binding, and other molecular functions, and participating in the regulation of intracellular substance transport, small molecule metabolism, and other biological processes. Protein functional enrichment analysis showed that these proteins were mainly enriched in the IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, Epstein-Barr virus infection, neutrophil extracellular trap formation, and other pathways involved in the formation and development process of ESCA. Survival analysis showed that the overall survival rate of ESCA patients with high expression of KIF4A, RAD51AP1, and CDKN3 was significantly shortened, and the difference was statistically significant (P<0.05). Furthermore, the areas under the curve (AUC) of KIF4A, RAD51AP1, and CDKN3 for diagnosing esophageal cancer were 0.956, 0.951 and 0.979, respectively, with sensitivities and specificities both exceeding 80%. Additionally, ROC results of the combined diagnostic model of these three genes showed an AUC of 0.979, with sensitivities and specificities of 0.914 and 1, respectively. This indicates that KIF4A, RAD51AP1 and CDKN3 have individual or combined auxiliary diagnostic value for ESCA. In conclusion, KIF4A, RAD51AP1 and CDKN3 have high diagnostic efficiency for ESCA, and their increased expression is closely related to the prognosis, suggesting that these three genes could be used as auxiliary diagnostic and prognostic factors for ESCA.

Are factor scores measurement invariant?

There has been increased interest in practical methods for integrative analysis of data from multiple studies or samples, and using factor scores to represent constructs has become a popular and practical alternative to latent variable models with all individual items. Although researchers are aware that scores representing the same construct should be on a similar metric across samples-namely they should be measurement invariant-for integrative data analysis, the methodological literature is unclear whether factor scores would satisfy such a requirement. In this note, we show that even when researchers successfully calibrate the latent factors to the same metric across samples, factor scores-which are estimates of the latent factors but not the factors themselves-may not be measurement invariant. Specifically, we prove that factor scores computed based on the popular regression method are generally not measurement invariant. Surprisingly, such scores can be noninvariant even when the items are invariant. We also demonstrate that our conclusions generalize to similar shrinkage scores in item response models for discrete items, namely the expected a posteriori scores and the maximum a posteriori scores. Researchers should be cautious in directly using factor scores for cross-sample analyses, even when such scores are obtained from measurement models that account for noninvariance. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Knowledge-guided learning methods for integrative analysis of multi-omics data

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

Modified Method of Chain Substitutions as an Alternative to the Integral Method of Economic Analysis

The aim is to present the results of the development of a modified method of chain substitutions, which is based on the use of the arithmetic mean sum of the results of the influence of each factor on the indicator of interest, taking into account the priority of each factor in all possible variants. At the same time, from the point of view of accuracy, the results obtained using the modified technique practically do not differ from the results of the integral method, however, they exceed it in terms of using a simpler mathematical apparatus. The relevance of the work is determined by the fact that in modern economic conditions (noticeably increased inflation, problems with energy prices), the issue of applying methods of deterministic factor analysis of expenses and incomes becomes especially significant in order to determine the size of the impact of each factor on a specific economic indicator as accurately as possible. However, the chain substitution method used in the vast majority of cases for deterministic factor analysis is inferior in accuracy to the integral method. The scientific novelty of the work lies in the fact that the author uses strict mathematical proofs of the coincidence of the accuracy of the results of the modified methodology and the integral method for various types of deterministic factor models (additive, multiplicative, multiple), which are supported by real practical calculations. Conclusions: the proposed modified method of chain substitutions, due to its mathematical simplicity and proven accuracy of the results obtained, can be widely used in real practical calculations using methods of economic analysis, especially taking into account the computer implementation of algorithms developed in this technique.