scispace - formally typeset
Search or ask a question
Institution

Tongji University

EducationShanghai, 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.


Papers
More filters
Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
Jing Deng1, Yisheng Shao1, Naiyun Gao1, Chaoqun Tan1, Shiqing Zhou1, Xuhao Hu1 
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

Journal ArticleDOI
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

Journal ArticleDOI
Jie Li1, Jianbing Chen1
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

Showing all 76610 results

NameH-indexPapersCitations
Gang Chen1673372149819
Yang Yang1642704144071
Georgios B. Giannakis137132173517
Jian Li133286387131
Jianlin Shi12785954862
Zhenyu Zhang118116764887
Ju Li10962346004
Peng Wang108167254529
Qian Wang108214865557
Yan Zhang107241057758
Richard B. Kaner10655766862
Han-Qing Yu10571839735
Wei Zhang104291164923
Fabio Marchesoni10460774687
Feng Li10499560692
Network Information
Related Institutions (5)
Shanghai Jiao Tong University
184.6K papers, 3.4M citations

95% related

Zhejiang University
183.2K papers, 3.4M citations

94% related

Nanjing University
105.5K papers, 2.2M citations

93% related

Peking University
181K papers, 4.1M citations

92% related

Fudan University
117.9K papers, 2.6M citations

92% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023238
20221,051
20219,713
20208,502
20197,517
20186,352