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  1. Article ; Online: Zero is not absence: censoring-based differential abundance analysis for microbiome data.

    Chan, Lap Sum / Li, Gen

    Bioinformatics (Oxford, England)

    2024  Volume 40, Issue 2

    Abstract: Motivation: Microbiome data analysis faces the challenge of sparsity, with many entries recorded as zeros. In differential abundance analysis, the presence of excessive zeros in data violates distributional assumptions and creates ties, leading to an ... ...

    Abstract Motivation: Microbiome data analysis faces the challenge of sparsity, with many entries recorded as zeros. In differential abundance analysis, the presence of excessive zeros in data violates distributional assumptions and creates ties, leading to an increased risk of type I errors and reduced statistical power.
    Results: We developed a novel normalization method, called censoring-based analysis of microbiome proportions (CAMP), for microbiome data by treating zeros as censored observations, transforming raw read counts into tie-free time-to-event-like data. This enables the use of survival analysis techniques, like the Cox proportional hazards model, for differential abundance analysis. Extensive simulations demonstrate that CAMP achieves proper type I error control and high power. Applying CAMP to a human gut microbiome dataset, we identify 60 new differentially abundant taxa across geographic locations, showcasing its usefulness. CAMP overcomes sparsity challenges, enabling improved statistical analysis and providing valuable insights into microbiome data in various contexts.
    Availability and implementation: The R package is available at https://github.com/lapsumchan/CAMP.
    MeSH term(s) Humans ; Microbiota ; Gastrointestinal Microbiome ; Research Design ; Data Analysis
    Language English
    Publishing date 2024-02-06
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btae071
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 2374: Show issues Location:
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  2. Article: Generalized Co-Clustering Analysis via Regularized Alternating Least Squares.

    Li, Gen

    Computational statistics & data analysis

    2020  Volume 150

    Abstract: Biclustering is an important exploratory analysis tool that simultaneously clusters rows (e.g., samples) and columns (e.g., variables) of a data matrix. Checkerboard-like biclusters reveal intrinsic associations between rows and columns. However, most ... ...

    Abstract Biclustering is an important exploratory analysis tool that simultaneously clusters rows (e.g., samples) and columns (e.g., variables) of a data matrix. Checkerboard-like biclusters reveal intrinsic associations between rows and columns. However, most existing methods rely on Gaussian assumptions and only apply to matrix data. In practice, non-Gaussian and/or multi-way tensor data are frequently encountered. A new CO-clustering method via Regularized Alternating Least Squares (CORALS) is proposed, which generalizes biclustering to non-Gaussian data and multi-way tensor arrays. Non-Gaussian data are modeled with single-parameter exponential family distributions and co-clusters are identified in the natural parameter space via sparse CANDECOMP/PARAFAC tensor decomposition. A regularized alternating (iteratively reweighted) least squares algorithm is devised for model fitting and a deflation procedure is exploited to automatically determine the number of co-clusters. Comprehensive simulation studies and three real data examples demonstrate the efficacy of the proposed method. The data and code are publicly available.
    Language English
    Publishing date 2020-05-04
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1478763-5
    ISSN 0167-9473
    ISSN 0167-9473
    DOI 10.1016/j.csda.2020.106989
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: CRISPR Empowers Tree Bioengineering for a Sustainable Future.

    Li, Gen / Qi, Yiping

    The CRISPR journal

    2023  Volume 6, Issue 4, Page(s) 305–307

    MeSH term(s) Trees ; Clustered Regularly Interspaced Short Palindromic Repeats/genetics ; CRISPR-Cas Systems/genetics ; Gene Editing ; Bioengineering
    Language English
    Publishing date 2023-07-31
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Comment
    ZDB-ID 3017891-5
    ISSN 2573-1602 ; 2573-1599
    ISSN (online) 2573-1602
    ISSN 2573-1599
    DOI 10.1089/crispr.2023.29161.gli
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  4. Article ; Online: Expression Quantitative Trait Loci Analysis in Multiple Tissues.

    Li, Gen

    Methods in molecular biology (Clifton, N.J.)

    2019  Volume 2082, Page(s) 231–237

    Abstract: Expression quantitative trait loci (eQTL) analysis identifies genetic variants that regulate the expression level of a gene. The genetic regulation may persist or vary in different tissues. When data are available on multiple tissues, it is often desired ...

    Abstract Expression quantitative trait loci (eQTL) analysis identifies genetic variants that regulate the expression level of a gene. The genetic regulation may persist or vary in different tissues. When data are available on multiple tissues, it is often desired to borrow information across tissues and conduct an integrative analysis. Here we describe a multi-tissue eQTL analysis procedure, which improves the identification of different types of eQTL and facilitates the assessment of tissue specificity.
    MeSH term(s) Alleles ; Computational Biology/methods ; Gene Expression ; Gene Expression Profiling/methods ; Genotype ; Organ Specificity ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci ; Software ; Web Browser
    Language English
    Publishing date 2019-12-03
    Publishing country United States
    Document type Journal Article
    ISSN 1940-6029
    ISSN (online) 1940-6029
    DOI 10.1007/978-1-0716-0026-9_16
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Tumor suppressor let-7 acts as a key regulator for pluripotency gene expression in Muse cells.

    Li, Gen / Wakao, Shohei / Kitada, Masaaki / Dezawa, Mari

    Cellular and molecular life sciences : CMLS

    2024  Volume 81, Issue 1, Page(s) 54

    Abstract: In embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), the expression of an RNA-binding pluripotency-relevant protein, LIN28, and the absence of its antagonist, the tumor-suppressor microRNA (miRNA) let-7, play a key role in ... ...

    Abstract In embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs), the expression of an RNA-binding pluripotency-relevant protein, LIN28, and the absence of its antagonist, the tumor-suppressor microRNA (miRNA) let-7, play a key role in maintaining pluripotency. Muse cells are non-tumorigenic pluripotent-like stem cells residing in the bone marrow, peripheral blood, and organ connective tissues as pluripotent surface marker SSEA-3(+). They express pluripotency genes, differentiate into triploblastic-lineage cells, and self-renew at the single cell level. Muse cells do not express LIN28 but do express let-7 at higher levels than in iPSCs. In Muse cells, we demonstrated that let-7 inhibited the PI3K-AKT pathway, leading to sustainable expression of the key pluripotency regulator KLF4 as well as its downstream genes, POU5F1, SOX2, and NANOG. Let-7 also suppressed proliferation and glycolysis by inhibiting the PI3K-AKT pathway, suggesting its involvement in non-tumorigenicity. Furthermore, the MEK/ERK pathway is not controlled by let-7 and may have a pivotal role in maintaining self-renewal and suppression of senescence. The system found in Muse cells, in which the tumor suppressor let-7, but not LIN28, tunes the expression of pluripotency genes, might be a rational cell system conferring both pluripotency-like properties and a low risk for tumorigenicity.
    MeSH term(s) Alprostadil ; Phosphatidylinositol 3-Kinases/genetics ; Proto-Oncogene Proteins c-akt ; Embryonic Stem Cells ; Gene Expression
    Chemical Substances Alprostadil (F5TD010360) ; Phosphatidylinositol 3-Kinases (EC 2.7.1.-) ; Proto-Oncogene Proteins c-akt (EC 2.7.11.1)
    Language English
    Publishing date 2024-01-23
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 1358415-7
    ISSN 1420-9071 ; 1420-682X
    ISSN (online) 1420-9071
    ISSN 1420-682X
    DOI 10.1007/s00018-023-05089-9
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 195: Show issues Location:
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  6. Article ; Online: ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.

    Li, Gen / Yao, Sijie / Fan, Long

    Journal of chemical information and modeling

    2024  Volume 64, Issue 2, Page(s) 340–347

    Abstract: Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster and more accurate method to predict the impact of the mutations on protein stability is helpful for ... ...

    Abstract Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster and more accurate method to predict the impact of the mutations on protein stability is helpful for protein design and understanding the phenotypic variation. Recent studies have shown that protein embedding will be particularly powerful at modeling sequence information with context dependence, such as subcellular localization, variant effect, and secondary structure prediction. Herein, we introduce a novel method, ProSTAGE, which is a deep learning method that fuses structure and sequence embedding to predict protein stability changes upon single point mutations. Our model combines graph-based techniques and language models to predict stability changes. Moreover, ProSTAGE is trained on a larger data set, which is almost twice as large as the most used S2648 data set. It consistently outperforms all existing state-of-the-art methods on mutation-affected problems as benchmarked on several independent data sets. The protein embedding as the prediction input achieves better results than the previous results, which shows the potential of protein language models in predicting the effect of mutations on proteins. ProSTAGE is implemented as a user-friendly web server.
    MeSH term(s) Neural Networks, Computer ; Proteins/genetics ; Proteins/chemistry ; Protein Stability ; Protein Structure, Secondary ; Mutation
    Chemical Substances Proteins
    Language English
    Publishing date 2024-01-02
    Publishing country United States
    Document type Journal Article
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.3c01697
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1230: Show issues Location:
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  7. Book ; Online: A non-asymptotic distributional theory of approximate message passing for sparse and robust regression

    Li, Gen / Wei, Yuting

    2024  

    Abstract: Characterizing the distribution of high-dimensional statistical estimators is a challenging task, due to the breakdown of classical asymptotic theory in high dimension. This paper makes progress towards this by developing non-asymptotic distributional ... ...

    Abstract Characterizing the distribution of high-dimensional statistical estimators is a challenging task, due to the breakdown of classical asymptotic theory in high dimension. This paper makes progress towards this by developing non-asymptotic distributional characterizations for approximate message passing (AMP) -- a family of iterative algorithms that prove effective as both fast estimators and powerful theoretical machinery -- for both sparse and robust regression. Prior AMP theory, which focused on high-dimensional asymptotics for the most part, failed to describe the behavior of AMP when the number of iterations exceeds $o\big({\log n}/{\log \log n}\big)$ (with $n$ the sample size). We establish the first finite-sample non-asymptotic distributional theory of AMP for both sparse and robust regression that accommodates a polynomial number of iterations. Our results derive approximate accuracy of Gaussian approximation of the AMP iterates, which improves upon all prior results and implies enhanced distributional characterizations for both optimally tuned Lasso and robust M-estimator.
    Keywords Mathematics - Statistics Theory ; Computer Science - Information Theory ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing ; Statistics - Machine Learning
    Subject code 519
    Publishing date 2024-01-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Exploring the Complex Ramifications of Post-Intensive Care as a Chronic Condition: Urgent Calls for Deeper Investigation into the Enigma.

    Liu, Tao / Ding, Huiru / Zhao, Zhihao / Li, Gen / Jiang, Rongcai

    QJM : monthly journal of the Association of Physicians

    2024  

    Language English
    Publishing date 2024-01-16
    Publishing country England
    Document type Journal Article
    ZDB-ID 1199985-8
    ISSN 1460-2393 ; 0033-5622 ; 1460-2725
    ISSN (online) 1460-2393
    ISSN 0033-5622 ; 1460-2725
    DOI 10.1093/qjmed/hcae015
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  9. Article ; Online: TiCNet: Transformer in Convolutional Neural Network for Pulmonary Nodule Detection on CT Images.

    Ma, Ling / Li, Gen / Feng, Xingyu / Fan, Qiliang / Liu, Lizhi

    Journal of imaging informatics in medicine

    2024  Volume 37, Issue 1, Page(s) 196–208

    Abstract: Lung cancer is the leading cause of cancer death. Since lung cancer appears as nodules in the early stage, detecting the pulmonary nodules in an early phase could enhance the treatment efficiency and improve the survival rate of patients. The development ...

    Abstract Lung cancer is the leading cause of cancer death. Since lung cancer appears as nodules in the early stage, detecting the pulmonary nodules in an early phase could enhance the treatment efficiency and improve the survival rate of patients. The development of computer-aided analysis technology has made it possible to automatically detect lung nodules in Computed Tomography (CT) screening. In this paper, we propose a novel detection network, TiCNet. It is attempted to embed a transformer module in the 3D Convolutional Neural Network (CNN) for pulmonary nodule detection on CT images. First, we integrate the transformer and CNN in an end-to-end structure to capture both the short- and long-range dependency to provide rich information on the characteristics of nodules. Second, we design the attention block and multi-scale skip pathways for improving the detection of small nodules. Last, we develop a two-head detector to guarantee high sensitivity and specificity. Experimental results on the LUNA16 dataset and PN9 dataset showed that our proposed TiCNet achieved superior performance compared with existing lung nodule detection methods. Moreover, the effectiveness of each module has been proven. The proposed TiCNet model is an effective tool for pulmonary nodule detection. Validation revealed that this model exhibited excellent performance, suggesting its potential usefulness to support lung cancer screening.
    Language English
    Publishing date 2024-01-10
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2948-2933
    ISSN (online) 2948-2933
    DOI 10.1007/s10278-023-00904-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: MRBEE: A bias-corrected multivariable Mendelian randomization method.

    Lorincz-Comi, Noah / Yang, Yihe / Li, Gen / Zhu, Xiaofeng

    HGG advances

    2024  Volume 5, Issue 3, Page(s) 100290

    Abstract: Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association ... ...

    Abstract Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies. However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using bias-corrected estimating equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.
    Language English
    Publishing date 2024-04-06
    Publishing country United States
    Document type Journal Article
    ISSN 2666-2477
    ISSN (online) 2666-2477
    DOI 10.1016/j.xhgg.2024.100290
    Database MEDical Literature Analysis and Retrieval System OnLINE

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