scholarly journals Infer disease-associated microbial biomarkers based on metagenomic and metatranscriptomic data

2021 ◽  
Author(s):  
Zhaoqian Liu ◽  
Qi Wang ◽  
Dongjun Chung ◽  
Qin Ma ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Low Rank ◽  
Connected Components ◽  
Biomarker Identification ◽  
Gene Expression Matrix ◽  
Large Size ◽  
Microbial Biomarkers ◽  
Key Species ◽  
Novel Method ◽  
Inflammatory Bowel ◽  
Disease Subtypes

AbstractUnveiling disease-associated microbial biomarkers (e.g., key species, genes, and pathways) is an efficient strategy for the diagnosis and therapy of diseases. However, the heterogeneity and large size of microbial data bring tremendous challenges for fundamental characteristics discovery. We present IDAM, a novel method for disease-associated biomarker identification from metagenomic and metatranscriptomic data, without requiring prior metadata. It integrates gene context conservation and regulatory mechanism through a mathematical model for maximizing the number of connected components between local-low rank submatrices of a gene expression matrix and known uber-operon structures. We applied IDAM to 813 inflammatory bowel disease-associated datasets and showed IDAM outperformed existing methods in microbial biomarker identification. In addition, the identified biomarkers successfully distinguished disease subtypes and showcased their power in discovering novel disease subtypes/states. IDAM is freely available at https://github.com/OSU-BMBL/IDAM.

Multiview Common Subspace Clustering via Coupled Low Rank Representation

2021 ◽  
Vol 12 (4) ◽  
pp. 1-25
Author(s):  
Stanley Ebhohimhen Abhadiomhen ◽  
Zhiyang Wang ◽  
Xiangjun Shen ◽  
Jianping Fan
Keyword(s):  
Common Knowledge ◽  
Subspace Clustering ◽  
Laplacian Matrix ◽  
Low Rank ◽  
Connected Components ◽  
Similarity Matrix ◽  
Benchmark Datasets ◽  
Low Rank Representation ◽  
Low Dimensional ◽  
Shared Structure

Multi-view subspace clustering (MVSC) finds a shared structure in latent low-dimensional subspaces of multi-view data to enhance clustering performance. Nonetheless, we observe that most existing MVSC methods neglect the diversity in multi-view data by considering only the common knowledge to find a shared structure either directly or by merging different similarity matrices learned for each view. In the presence of noise, this predefined shared structure becomes a biased representation of the different views. Thus, in this article, we propose a MVSC method based on coupled low-rank representation to address the above limitation. Our method first obtains a low-rank representation for each view, constrained to be a linear combination of the view-specific representation and the shared representation by simultaneously encouraging the sparsity of view-specific one. Then, it uses the k -block diagonal regularizer to learn a manifold recovery matrix for each view through respective low-rank matrices to recover more manifold structures from them. In this way, the proposed method can find an ideal similarity matrix by approximating clustering projection matrices obtained from the recovery structures. Hence, this similarity matrix denotes our clustering structure with exactly k connected components by applying a rank constraint on the similarity matrix’s relaxed Laplacian matrix to avoid spectral post-processing of the low-dimensional embedding matrix. The core of our idea is such that we introduce dynamic approximation into the low-rank representation to allow the clustering structure and the shared representation to guide each other to learn cleaner low-rank matrices that would lead to a better clustering structure. Therefore, our approach is notably different from existing methods in which the local manifold structure of data is captured in advance. Extensive experiments on six benchmark datasets show that our method outperforms 10 similar state-of-the-art compared methods in six evaluation metrics.

scholarly journals A personalized network framework reveals predictive axis of anti-TNF response across diseases

2021 ◽  
Author(s):  
Shiran Gerassy-Vainberg ◽  
Elina Starosvetsky ◽  
Renaud Gaujoux ◽  
Alexandra Blatt ◽  
Naama Maimon ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Drug Response ◽  
Computational Approach ◽  
High Dimensional ◽  
Biomarker Identification ◽  
Individual Level ◽  
Medical Need ◽  
Inflammatory Bowel ◽  
Intermediate Monocytes ◽  
Blood Data

Personalized treatment of complex diseases is an unmet medical need pushing towards drug biomarker identification of one drug-disease combination at a time. Here, we used a novel computational approach for modeling cell-centered individual-level network dynamics from high-dimensional blood data to predict infliximab response and uncover individual variation of non-response. We identified and validated that the RAC1-PAK1 axis is predictive of infliximab response in inflammatory bowel disease. Intermediate monocytes, which closely correlated with inflammation state, play a key role in the RAC1-PAK1 responses, supporting their modulation as a therapeutic target. This axis also predicts response in Rheumatoid arthritis, validated in three public cohorts. Our findings support pan-disease drug response diagnostics from blood, implicating common mechanisms of drug response or failure across diseases.

scholarly journals Multi-“-Omics” Profiling in Patients With Quiescent Inflammatory Bowel Disease Identifies Biomarkers Predicting Relapse

2020 ◽  
Vol 26 (10) ◽  
pp. 1524-1532
Author(s):  
Nienke Z Borren ◽  
Damian Plichta ◽  
Amit D Joshi ◽  
Gracia Bonilla ◽  
Ruslan Sadreyev ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Confidence Interval ◽  
Risk Score ◽  
Crohn Disease ◽  
Odds Ratio ◽  
Risk Scores ◽  
Clinical Relapse ◽  
Cell Surface Glycoprotein ◽  
Microbial Biomarkers ◽  
Inflammatory Bowel

Abstract Background Inflammatory bowel diseases (IBD) are characterized by intermittent relapses, and their course is heterogeneous and unpredictable. Our aim was to determine the ability of protein, metabolite, or microbial biomarkers to predict relapse in patients with quiescent disease. Methods This prospective study enrolled patients with quiescent Crohn disease and ulcerative colitis, defined as the absence of clinical symptoms (Harvey-Bradshaw Index ≤ 4, Simple Clinical Colitis Activity Index ≤ 2) and endoscopic remission within the prior year. The primary outcome was relapse within 2 years, defined as symptomatic worsening accompanied by elevated inflammatory markers resulting in a change in therapy or IBD-related hospitalization or surgery. Biomarkers were tested in a derivation cohort, and their performance was examined in an independent validation cohort. Results Our prospective cohort study included 164 patients with IBD (108 with Crohn disease, 56 with ulcerative colitis). Upon follow-up for a median of 1 year, 22 patients (13.4%) experienced a relapse. Three protein biomarkers (interleukin-10, glial cell line–derived neurotrophic factor, and T-cell surface glycoprotein CD8 alpha chain) and 4 metabolomic markers (propionyl-L-carnitine, carnitine, sarcosine, and sorbitol) were associated with relapse in multivariable models. Proteomic and metabolomic risk scores independently predicted relapse with a combined area under the curve of 0.83. A high proteomic risk score (odds ratio = 9.11; 95% confidence interval, 1.90-43.61) or metabolomic risk score (odds ratio = 5.79; 95% confidence interval, 1.24-27.11) independently predicted a higher risk of relapse over 2 years. Fecal metagenomics showed an increased abundance of Proteobacteria (P = 0.0019, q = 0.019) and Fusobacteria (P = 0.0040, q = 0.020) and at the species level Lachnospiraceae_bacterium_2_1_58FAA (P = 0.000008, q = 0.0009) among the relapses. Conclusions Proteomic, metabolomic, and microbial biomarkers identify a proinflammatory state in quiescent IBD that predisposes to clinical relapse.

scholarly journals Regularized Auto-Encoder-Based Separation of Defects from Backgrounds for Inspecting Display Devices

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 533 ◽  
Author(s):  
Heeyeon Jo ◽  
Jeongtae Kim
Keyword(s):  
Experimental Studies ◽  
Low Rank ◽  
Reconstruction Method ◽  
Low Rank Approximation ◽  
Display Devices ◽  
Singular Value Decomposition Method ◽  
Novel Method ◽  
Rank Approximation ◽  
Improved Performance ◽  
Value Decomposition

We investigated a novel method for separating defects from the background for inspecting display devices. Separation of defects has important applications such as determining whether the detected defects are truly defective and the quantification of the degree of defectiveness. Although many studies on estimating patterned background have been conducted, the existing studies are mainly based on the approach of approximation by low-rank matrices. Because the conventional methods face problems such as imperfect reconstruction and difficulty of selecting the bases for low-rank approximation, we have studied a deep-learning-based foreground reconstruction method that is based on the auto-encoder structure with a regression layer for the output. In the experimental studies carried out using mobile display panels, the proposed method showed significantly improved performance compared to the existing singular value decomposition method. We believe that the proposed method could be useful not only for inspecting display devices but also for many applications that involve the detection of defects in the presence of a textured background.

Sparse Logistic Regression with Lp Penalty for Biomarker Identification

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Zhenqiu Liu ◽  
Feng Jiang ◽  
Guoliang Tian ◽  
Suna Wang ◽  
Fumiaki Sato ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Microarray Data ◽  
High Dimensional ◽  
Computational Results ◽  
Smooth Approximation ◽  
Biomarker Identification ◽  
Sparse Logistic Regression ◽  
Regularization Parameters ◽  
Novel Method ◽  
Convex Regularization

In this paper, we propose a novel method for sparse logistic regression with non-convex regularization Lp (p <1). Based on smooth approximation, we develop several fast algorithms for learning the classifier that is applicable to high dimensional dataset such as gene expression. To the best of our knowledge, these are the first algorithms to perform sparse logistic regression with an Lp and elastic net (Le) penalty. The regularization parameters are decided through maximizing the area under the ROC curve (AUC) of the test data. Experimental results on methylation and microarray data attest the accuracy, sparsity, and efficiency of the proposed algorithms. Biomarkers identified with our methods are compared with that in the literature. Our computational results show that Lp Logistic regression (p <1) outperforms the L1 logistic regression and SCAD SVM. Software is available upon request from the first author.

scholarly journals A Novel Method for LWIR Hyperspectral Target Detection by Means of a Subspace-Based Approach

Proceedings ◽  
2019 ◽  
Vol 27 (1) ◽  
pp. 47
Author(s):  
Matteo Moscadelli ◽  
Nicola Acito ◽  
Marco Diani ◽  
Giovanni Corsini
Keyword(s):  
Target Detection ◽  
Simulated Data ◽  
Thermal Infrared ◽  
Spectral Emissivity ◽  
Low Rank ◽  
Spectral Variability ◽  
Basis Matrix ◽  
New Approach ◽  
Detection Techniques ◽  
Novel Method

In this work, we present a new approach to detect materials with known spectral emissivity, in data acquired by thermal infrared hyperspectral systems. The method takes into account the spectral variability of the downwelling radiance, commonly neglected in most target detection techniques. We address such variability supposing that the downwelling radiance spans a low-rank subspace, whose basis matrix is learned off-line by means of MODTRAN. We evaluate the performance of the method with simulated data, and present results that show the effectiveness of the proposed algorithm.

Comparison of Inflammatory Bowel Disease Subtypes Among Hispanics, Non-Hispanic Whites and African Americans: A Systematic Review and Meta-Analysis

2017 ◽  
Vol 152 (5) ◽  
pp. S974-S975
Author(s):  
Danny J. Avalos ◽  
Oriana M. Damas ◽  
Marc Zuckerman ◽  
Vladimir Paez ◽  
Majd Michael ◽  
...  
Keyword(s):  
Systematic Review ◽  
Inflammatory Bowel Disease ◽  
African Americans ◽  
Bowel Disease ◽  
Meta Analysis ◽  
Inflammatory Bowel ◽  
Disease Subtypes
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