Article
Microbiology
Caizhi Huang, Benjamin John Callahan, Michael C. Wu, Shannon T. Holloway, Hayden Brochu, Wenbin Lu, Xinxia Peng, Jung-Ying Tzeng
Summary: In this study, a local collapsing test method called POST is proposed to effectively utilize phylogenetic information by supervising the phylogenetic distance and the outcome-OTU association. Simulation studies and real data applications demonstrate that POST can better identify outcome-associated microbial features at the OTU level.
Article
Computer Science, Artificial Intelligence
Till Hendrik Schulz, Tamas Horvath, Pascal Welke, Stefan Wrobel
Summary: Weisfeiler-Lehman graph kernels are still one of the most prevalent graph kernels after more than a decade, thanks to their impressive predictive performance and time complexity. However, their binary comparison based on label equality may be too rigid for certain graph classes. To address this limitation, we propose a generalization of the Weisfeiler-Lehman graph kernels that considers a more natural and fine-grained similarity between labels. We demonstrate that this similarity can be efficiently calculated using the Wasserstein distance between vectors representing the labels. Our generalization outperforms other state-of-the-art graph kernels in terms of predictive performance on datasets with structurally complex graphs.
Article
Biochemical Research Methods
Merce Llabres, Francesc Rossello, Gabriel Valiente
Summary: The Generalized Robinson-Foulds (GRF) distance is a metric for comparing structures with high resolution and can be computed in linear time, distinct from the RF distance.
JOURNAL OF COMPUTATIONAL BIOLOGY
(2021)
Article
Biotechnology & Applied Microbiology
Hoang N. H. Tran, Trang Nguyen Hoang Thu, Phu Huu Nguyen, Chi Nguyen Vo, Khanh Van Doan, Chau Nguyen Ngoc Minh, Ngoc Tuan Nguyen, Van Ngoc Duc Ta, Khuong An Vu, Thanh Danh Hua, To Nguyen Thi Nguyen, Tan Trinh Van, Trung Pham Duc, Ba Lap Duong, Phuc Minh Nguyen, Vinh Chuc Hoang, Duy Thanh Pham, Guy E. Thwaites, Lindsay J. Hall, Daniel J. Slade, Stephen Baker, Vinh Hung Tran, Hao Chung The
Summary: Perturbations in the gut microbiome, specifically the presence of overabundant Fusobacterium nucleatum, have been associated with colorectal cancer. This study analyzed the genomic diversity of Fusobacterium in Vietnamese CRC patients and found significant differences in the abundance of oral bacteria in tumor microbiomes. The research also identified diverse subtypes of F. nucleatum within individual patients.
NPJ BIOFILMS AND MICROBIOMES
(2022)
Article
Biology
Shulei Wang, T. Tony Cai, Hongzhe Li
Summary: Quantitative comparison of microbial composition from different populations is a fundamental task in various microbiome studies. The study introduces a new maximum type test, detector of active flow on a tree, which demonstrates effectiveness in detecting differences in microbial composition between populations, particularly in cases of sparse phylogenetic composition difference. Empirical evidence supports the method's power against sparse differences in phylogenetic microbial composition.
Article
Biochemical Research Methods
Ye Wang, Tathagata Bhattacharya, Yuchao Jiang, Xiao Qin, Yue Wang, Yunlong Liu, Andrew J. Saykin, Li Chen
Summary: With the development and decreasing cost of next-generation sequencing technologies, the study of the human microbiome has become a rapidly expanding research field with various clinical applications. Building a prediction model for clinical outcomes based on microbiome data is essential for improving prediction performance. The phylogenetic tree represents a unique correlation structure of microbiome and can be important for this purpose.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Wei Bai, Mei Dong, Longhai Li, Cindy Feng, Wei Xu
Summary: The study demonstrated the effectiveness of randomized quantile residuals (RQRs) in diagnosing zero-inflated generalized linear mixed models (GLMMs) for sequencing count data through large-scale simulation studies and application to a real microbiome dataset. RQRs were shown to closely align with the nominal level in type I error rates, while scatter-plots and Q-Q plots of RQRs were useful in distinguishing between good and bad models. The results also indicated that RQRs could successfully diagnose the OTU counts at the genus level in the microbiome dataset, suggesting their utility in analyzing zero-inflated count data.
BMC BIOINFORMATICS
(2021)
Article
Mathematical & Computational Biology
Seonjin Kim, Hyunkeun Ryan Cho, Mi-Ok Kim
Summary: The study proposes a nonparametric bivariate varying coefficient generalized linear model to predict future mean response trajectories by utilizing a combination of kernel and spline methods. The research also develops a new bootstrap approach for statistical inference and applies the methodology to the Framingham Heart Study.
STATISTICS IN MEDICINE
(2021)
Article
Mathematics
Hongying Wu, Zhiqiang Zhou, Caijuan Kang
Summary: This paper proposes a new approach to construct willow tree (WT) for generalized hyperbolic (GH) Levy processes. Compared to classical methods, the proposed approach has two advantages: it avoids moments matching and reduces the error of European option pricing, improving the stability and accuracy of WT.
JOURNAL OF MATHEMATICS
(2023)
Article
Biotechnology & Applied Microbiology
Qilin Hong, Guanhua Chen, Zheng-Zheng Tang
Summary: PhyloMed is a phylogeny-based mediation analysis method that addresses the challenges of compositional and high-dimensional microbiome data. It discovers mediation signals by analyzing subcompositions defined on the phylogenetic tree, producing well-calibrated mediation test p-values and higher discovery power than existing methods.
Article
Biochemical Research Methods
Bing Li, Tian Wang, Min Qian, Shuang Wang
Summary: Studies have shown that the human microbiome is linked to human health and diseases and can be used for predictive purposes. Statistical methods have been developed to analyze microbiome data, with a focus on capturing different information using various distance metrics. Prediction models, including deep learning methods, have been created to utilize taxa abundance profiles and taxonomic relationships. It has also been found that multiple forms of microbiome profiles can be associated with a health outcome. However, there is currently no prediction model that incorporates multiple forms of microbiome-outcome associations. To address this, a multi-kernel machine regression (MKMR) method has been proposed to capture different types of microbiome signals and improve prediction accuracy.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Health Care Sciences & Services
Yusuke Saigusa, Shinto Eguchi, Osamu Komori
Summary: The generalized linear mixed model (GLMM) is a common method for analyzing longitudinal and clustered data in biological sciences. However, issues of model complexity and misspecification can arise. This paper extends the standard GLMM to a nonlinear mixed-effects model based on quasi-linear modeling, providing an estimation algorithm and a conditional AIC for the proposed model. Performance under model misspecification is evaluated in simulation studies, and the proposed model is shown to capture heterogeneity in respiratory illness data.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2022)
Article
Computer Science, Information Systems
Qi Xiao, Yunchuan Qin, Kenli Li, Zhuo Tang, Fan Wu, Zhizhong Liu
Summary: Text similarity measurement is crucial in artificial intelligence tasks. However, there is a lack of focus on resource-limited scenarios. This paper proposes a lightweight and semantically rich text similarity measurement model that outperforms other methods, especially in resource-limited scenes.
INFORMATION SCIENCES
(2022)
Article
Nutrition & Dietetics
Jieping Yang, Rupo Lee, Zachary Schulz, Albert Hsu, Jonathan Pai, Scarlet Yang, Susanne M. Henning, Jianjun Huang, Jonathan P. P. Jacobs, David Heber, Zhaoping Li
Summary: We investigated the effects of mixed tree nuts (MTNs) on tryptophan (Trp) metabolism and cardiovascular risk markers in overweight individuals. Our findings suggest that consumption of MTNs during weight loss can improve Trp-kynurenine metabolism, as well as increase Trp-serotonin and Trp-indole metabolism. However, no significant changes in gut microbiota were observed between the MTNs and pretzel groups.
Article
Computer Science, Hardware & Architecture
Shenghui Li, Ying Shi, Linna Hu, Zhenxing Sun
Summary: The control performance of the compliant actuator system was optimized using a generalized predictive control method, which is almost model-free and has proven system stability. Experimental tests on a series elastic actuator driven exoskeleton robot confirmed the effectiveness of the proposed methods.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Biochemical Research Methods
Eugene Urrutia, Li Chen, Haibo Zhou, Yuchao Jiang
Article
Biochemical Research Methods
Li Chen, Feng Wang, Emily C. Bruggeman, Chao Li, Bing Yao
Article
Biochemical Research Methods
Guodong Yang, Aiqun Ma, Zhaohui S. Qin, Li Chen
Article
Biochemistry & Molecular Biology
Lixia Qin, Qian Xu, Ziyi Li, Li Chen, Yujing Li, Nannan Yang, Zhenhua Liu, Jifeng Guo, Lu Shen, Emily G. Allen, Chao Chen, Chao Ma, Hao Wu, Xiongwei Zhu, Peng Jin, Beisha Tang
HUMAN MOLECULAR GENETICS
(2020)
Article
Biochemical Research Methods
Li Chen
Review
Biochemical Research Methods
Wenwen Mei, Zhiwen Jiang, Yang Chen, Li Chen, Aziz Sancar, Yuchao Jiang
Summary: This study conducted a comprehensive analysis of seven algorithms commonly used for circadian rhythm detection, evaluating their accuracy, reproducibility, and robustness to key variables. Guidelines for method selection in circadian rhythm detection were provided, applicable to different types of high-throughput omics data.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Oncology
Amit Kumar Mitra, Harish Kumar, Vijay Ramakrishnan, Li Chen, Linda Baughn, Shaji Kumar, S. Vincent Rajkumar, Brian G. Van Ness
BLOOD CANCER JOURNAL
(2020)
Article
Pharmacology & Pharmacy
Ahmed Ullah Mishuk, Li Chen, Philippe Gaillard, Salisa Westrick, Richard A. Hansen, Jingjing Qian
Summary: A study based on data from the Medical Expenditure Panel Survey in the United States from 2002 to 2017 found that the overall proportion of PPI users increased, with trends observed among adults aged 65 years and older, different genders, ethnicities, geographic regions, insurance types, and obesity. Despite the increase in PPI use, the average expenditure per patient decreased significantly.
JOURNAL OF THE AMERICAN PHARMACISTS ASSOCIATION
(2021)
Article
Cell Biology
Janise N. Kuehner, Junyu Chen, Emily C. Bruggeman, Feng Wang, Yangping Li, Chongchong Xu, Zachary T. McEachin, Ziyi Li, Li Chen, Chadwick M. Hales, Zhexing Wen, Jingjing Yang, Bing Yao
Summary: The study found that 5hmC undergoes dynamic changes during early human brain development and is associated with AD pathology. AD organoids display cellular and molecular phenotypes similar to those observed in human AD brains. These data suggest a highly coordinated molecular system that may be dysregulated in early developing AD organoids.
Article
Biochemical Research Methods
Li Chen, Ye Wang, Fengdi Zhao
Summary: Although genome-wide association studies have identified many variants related to complex traits in non-coding regions, they may not be causal. By utilizing a deep transfer learning model that leverages both generic and context-specific functional non-coding variants, the prediction accuracy for functional non-coding variants can be improved.
Article
Biochemical Research Methods
Ye Wang, Li Chen
Summary: This study proposes a multi-modal deep learning framework that predicts genome-wide quantitative epigenetic signals by considering personal genetic variations and traits, evaluating the functional consequence of non-coding variants. By applying it to Alzheimer's disease research data, the method demonstrates accuracy and practicality.
Article
Biochemical Research Methods
Aman Agarwal, Li Chen
Summary: The researchers developed a supervised multi-modal deep learning model to predict tissue/cell type-specific promoter-enhancer (PE) and promoter-promoter (PP) interactions. The model utilized a comprehensive set of features, including genomic sequence, epigenetic signal, anchor distance, evolutionary features, and DNA structural features. The proposed approach outperformed state-of-the-art deep learning methods, especially in predicting PE interactions. The performance could be further improved by using computationally inferred biologically relevant tissues/cell types in the pretraining.
Article
Biochemistry & Molecular Biology
Li Chen, Andrew J. Saykin, Bing Yao, Fengdi Zhao
Summary: Traditional approaches for diagnosing Alzheimer's disease (AD) are invasive and expensive. In this study, the researchers developed two multi-task deep autoencoders to predict AD progression and reconstruct DNA methylation profiles using peripheral blood samples. The proposed method outperforms existing machine learning approaches and shows promising results.
COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
(2022)
Article
Endocrinology & Metabolism
Nan Huo, Li Chen, Ahmed Ullah Mishuk, Chao Li, Richard A. Hansen, Ilene Harris, Zippora Kiptanui, Zhong Wang, Sarah K. Dutcher, Jingjing Qian
Article
Public, Environmental & Occupational Health
Lindsey A. Hohmann, Tessa J. Hastings, David R. Ha, Kimberly B. Garza, Sally A. Huston, Li Chen, Salisa C. Westrick
RESEARCH IN SOCIAL & ADMINISTRATIVE PHARMACY
(2019)