13 Works

Block-Wise Variable Selection for Clustering Via Latent States of Mixture Models

Beomseok Seo, Lin Lin & Jia Li
Mixture modeling is a major paradigm for clustering in statistics. In this article, we develop a new block-wise variable selection method for clustering by exploiting the latent states of the hidden Markov model on variable blocks or the Gaussian mixture model. The variable blocks are formed by depth-first-search on a dendrogram created based on the mutual information between any pair of variables. It is demonstrated that the latent states of the variable blocks together with...

Block-Wise Variable Selection for Clustering Via Latent States of Mixture Models

Beomseok Seo, Lin Lin & Jia Li
Mixture modeling is a major paradigm for clustering in statistics. In this article, we develop a new block-wise variable selection method for clustering by exploiting the latent states of the hidden Markov model on variable blocks or the Gaussian mixture model. The variable blocks are formed by depth-first-search on a dendrogram created based on the mutual information between any pair of variables. It is demonstrated that the latent states of the variable blocks together with...

Elevated Ras related GTP binding B (RRAGB) expression predicts poor overall survival and constructs a prognostic nomogram for colon adenocarcinoma

Jianjia Xiao, Qingqing Liu, Weijie Wu, Ying Yuan, Jie Zhou, Jieyu Shi & Shaorong Zhou
Currently, no articles have explored the roles of RRAGB gene in the occurrence and development of cancer. By means of The Cancer Genome Atlas (TCGA) data mining, we found that this gene might be a novel prognostic predictor for colon adenocarcinoma (COAD). Hence, this article was carried out to explore its roles in COAD and associations with immunity. RRAGB single-gene expression matrix and corresponding clinical information were extracted from TCGA database. Univariate/multivariate cox regression analyses...

Structural insights into dpCoA-RNA decapping by NudC

Wei Zhou, Zeyuan Guan, Fen Zhao, Yage Ye, Fang Yang, Ping Yin & Delin Zhang
Various kinds of cap structures, such as m7G, triphosphate groups, NAD and dpCoA, protect the 5′ terminus of RNA. The cap structures bond covalently to RNA and affect its stability, translation, and transport. The removal of the caps is mainly executed by Nudix hydrolase family proteins, including Dcp2, RppH and NudC. Numerous efforts have been made to elucidate the mechanism underlying the removal of m7G, triphosphate group, and NAD caps. In contrast, few studies related...

Transcriptome profiling revealed heat stress-responsive genes in Arabidopsis through integrated bioinformatics analysis

Meili Guo, Xin Liu, Jiahui Wang, Yusu Jiang, Jinhuan Yu & Jing Gao
Heat stress is an environmental challenge that reduces plant productivity and growth. Plants have developed corresponding mechanisms to survive this adverse environmental stress. To demonstrate the mechanisms of how plants adapt to the environmental challenge, the heat response experiments involving Arabidopsis thaliana were retrieved from the GEO database. After quantile normalization of the GEO raw data, the differentially expressed genes (DEGs) in response to heat stress were identified by robust rank aggregation (RRA) algorithm, including...

Structural insights into dpCoA-RNA decapping by NudC

Wei Zhou, Zeyuan Guan, Fen Zhao, Yage Ye, Fang Yang, Ping Yin & Delin Zhang
Various kinds of cap structures, such as m7G, triphosphate groups, NAD and dpCoA, protect the 5′ terminus of RNA. The cap structures bond covalently to RNA and affect its stability, translation, and transport. The removal of the caps is mainly executed by Nudix hydrolase family proteins, including Dcp2, RppH and NudC. Numerous efforts have been made to elucidate the mechanism underlying the removal of m7G, triphosphate group, and NAD caps. In contrast, few studies related...

Elevated Ras related GTP binding B (RRAGB) expression predicts poor overall survival and constructs a prognostic nomogram for colon adenocarcinoma

Jianjia Xiao, Qingqing Liu, Weijie Wu, Ying Yuan, Jie Zhou, Jieyu Shi & Shaorong Zhou
Currently, no articles have explored the roles of RRAGB gene in the occurrence and development of cancer. By means of The Cancer Genome Atlas (TCGA) data mining, we found that this gene might be a novel prognostic predictor for colon adenocarcinoma (COAD). Hence, this article was carried out to explore its roles in COAD and associations with immunity. RRAGB single-gene expression matrix and corresponding clinical information were extracted from TCGA database. Univariate/multivariate cox regression analyses...

Transcriptome profiling revealed heat stress-responsive genes in Arabidopsis through integrated bioinformatics analysis

Meili Guo, Xin Liu, Jiahui Wang, Yusu Jiang, Jinhuan Yu & Jing Gao
Heat stress is an environmental challenge that reduces plant productivity and growth. Plants have developed corresponding mechanisms to survive this adverse environmental stress. To demonstrate the mechanisms of how plants adapt to the environmental challenge, the heat response experiments involving Arabidopsis thaliana were retrieved from the GEO database. After quantile normalization of the GEO raw data, the differentially expressed genes (DEGs) in response to heat stress were identified by robust rank aggregation (RRA) algorithm, including...

First-Order Newton-Type Estimator for Distributed Estimation and Inference

Xi Chen, Weidong Liu & Yichen Zhang
This article studies distributed estimation and inference for a general statistical problem with a convex loss that could be nondifferentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward divide-and-conquer stochastic gradient descent approach. Our theory shows that there is a restriction on the number of machines and this restriction becomes more...

First-order Newton-type Estimator for Distributed Estimation and Inference

Xi Chen, Weidong Liu & Yichen Zhang
This paper studies distributed estimation and inference for a general statistical problem with a convex loss that could be non-differentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward Divide-and-Conquer Stochastic Gradient Descent (DC-SGD) approach. Our theory shows that there is a restriction on the number of machines and this restriction becomes...

First-Order Newton-Type Estimator for Distributed Estimation and Inference

Xi Chen, Weidong Liu & Yichen Zhang
This article studies distributed estimation and inference for a general statistical problem with a convex loss that could be nondifferentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward divide-and-conquer stochastic gradient descent approach. Our theory shows that there is a restriction on the number of machines and this restriction becomes more...

An enhanced methodology for predicting protein-protein interactions between human and hepatitis C virus via ensemble learning algorithms

Xin Liu, Liang Wang, Cheng-Hao Liang, Ya-Ping Lu, Ting Yang & Xiao Zhang
Hepatitis C virus (HCV) is responsible for a variety of human life-threatening diseases, which include liver cirrhosis, chronic hepatitis, fibrosis and hepatocellular carcinoma (HCC) . Computational study of protein-protein interactions between human and HCV could boost the findings of antiviral drugs in HCV therapy and might optimize the treatment procedures for HCV infections. In this analysis, we constructed a prediction model for protein-protein interactions between HCV and human by incorporating the features generated by pseudo...

An enhanced methodology for predicting protein-protein interactions between human and hepatitis C virus via ensemble learning algorithms

Xin Liu, Liang Wang, Cheng-Hao Liang, Ya-Ping Lu, Ting Yang & Xiao Zhang
Hepatitis C virus (HCV) is responsible for a variety of human life-threatening diseases, which include liver cirrhosis, chronic hepatitis, fibrosis and hepatocellular carcinoma (HCC) . Computational study of protein-protein interactions between human and HCV could boost the findings of antiviral drugs in HCV therapy and might optimize the treatment procedures for HCV infections. In this analysis, we constructed a prediction model for protein-protein interactions between HCV and human by incorporating the features generated by pseudo...

Registration Year

  • 2021
    13

Resource Types

  • Dataset
    13

Affiliations

  • Third Affiliated Hospital of Guangzhou Medical University
    13
  • Wuhan University
    11
  • Chinese Academy of Sciences
    10
  • Zhejiang University
    9
  • Chinese Academy of Medical Sciences & Peking Union Medical College
    9
  • Southern Medical University
    8
  • Sun Yat-sen University
    7
  • Air Force Medical University
    6
  • Huazhong University of Science and Technology
    6
  • Shanghai Jiao Tong University
    6