Institution
Tongji University
Education•Shanghai, China•
About: Tongji University is a education organization based out in Shanghai, China. It is known for research contribution in the topics: Population & Adsorption. The organization has 76116 authors who have published 81176 publications receiving 1248911 citations. The organization is also known as: Tongji & Tóngjì Dàxué.
Topics: Population, Adsorption, Cancer, Finite element method, Lung cancer
Papers published on a yearly basis
Papers
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TL;DR: A crosstalk between acetylation and ubiquitylation is revealed by competing for the same lysine residues in the regulation of fatty acid synthesis and cell growth in response to glucose.
275 citations
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University of Cambridge1, Papworth Hospital2, Université Paris-Saclay3, French Institute of Health and Medical Research4, Peking Union Medical College5, University of Bologna6, Vanderbilt University7, University of Utah8, Tokai University9, University Hospital Heidelberg10, Tongji University11, Kyorin University12, Columbia University Medical Center13, Pierre-and-Marie-Curie University14
TL;DR: Patients with PAH and BMPR2 mutations present at a younger age with more severe disease, and are at increased risk of death, and death or transplantation, compared with those without BM PR2 mutations.
275 citations
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TL;DR: A magnetic nanoscaled catalyst cobalt ferrite (CoFe2O4) was successfully prepared and used for the activation of oxone to generate sulfate radicals for the degradation of diclofenac and exhibited the best catalytic performance.
275 citations
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TL;DR: A novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences is presented.
Abstract: Protein-protein interactions (PPIs) play crucial roles in the execution of various cellular processes and form the basis of biological mechanisms. Although large amount of PPIs data for different species has been generated by high-throughput experimental techniques, current PPI pairs obtained with experimental methods cover only a fraction of the complete PPI networks, and further, the experimental methods for identifying PPIs are both time-consuming and expensive. Hence, it is urgent and challenging to develop automated computational methods to efficiently and accurately predict PPIs. We present here a novel hierarchical PCA-EELM (principal component analysis-ensemble extreme learning machine) model to predict protein-protein interactions only using the information of protein sequences. In the proposed method, 11188 protein pairs retrieved from the DIP database were encoded into feature vectors by using four kinds of protein sequences information. Focusing on dimension reduction, an effective feature extraction method PCA was then employed to construct the most discriminative new feature set. Finally, multiple extreme learning machines were trained and then aggregated into a consensus classifier by majority voting. The ensembling of extreme learning machine removes the dependence of results on initial random weights and improves the prediction performance. When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 87.00% prediction accuracy with 86.15% sensitivity at the precision of 87.59%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Experimental results demonstrate that proposed PCA-EELM outperforms the SVM method by 5-fold cross-validation. Besides, PCA-EELM performs faster than PCA-SVM based method. Consequently, the proposed approach can be considered as a new promising and powerful tools for predicting PPI with excellent performance and less time.
275 citations
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TL;DR: A uniform and rigorous theoretical basis for the family of newly developed probability density evolution method is provided and the principle of preservation of probability is revisited from the two descriptions: the state space description and the random event description.
275 citations
Authors
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Name | H-index | Papers | Citations |
---|---|---|---|
Gang Chen | 167 | 3372 | 149819 |
Yang Yang | 164 | 2704 | 144071 |
Georgios B. Giannakis | 137 | 1321 | 73517 |
Jian Li | 133 | 2863 | 87131 |
Jianlin Shi | 127 | 859 | 54862 |
Zhenyu Zhang | 118 | 1167 | 64887 |
Ju Li | 109 | 623 | 46004 |
Peng Wang | 108 | 1672 | 54529 |
Qian Wang | 108 | 2148 | 65557 |
Yan Zhang | 107 | 2410 | 57758 |
Richard B. Kaner | 106 | 557 | 66862 |
Han-Qing Yu | 105 | 718 | 39735 |
Wei Zhang | 104 | 2911 | 64923 |
Fabio Marchesoni | 104 | 607 | 74687 |
Feng Li | 104 | 995 | 60692 |