A drug repurposing method based on inhibition effect on gene regulatory network

Li, Xianbin; Liao, Minzhen; Wang, Bing; Zan, Xiangzhen; Huo, Yanhao; Liu, Yue; Bao, Zhenshen; Xu, Peng; Liu, Wenbin

doi:10.1016/j.csbj.2023.09.007

Cited by 3 publications

(1 citation statement)

References 76 publications

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“…In the simulated spatio-temporal proteomics datasets, BANDLE(Dirichlet) and TransGCN achieved better predictive performance in protein translocation identification according to the area under the receiver operating characteristic (ROC) curve (AUC) [ 52 ] metric ( Figure S1 ), while TransGCN got the highest area under precision-recall (PR) curve (AUPR) on most simulated datasets except on the datasets of fraction swapping ( Figure 2A ). For example, TransGCN (AUPR = 0.8287) demonstrated superior performance with the highest AUPR compared to MR2016 (AUPR = 0.4067), MR2017 (AUPR = 0.4079), MS (AUPR = 0.3351), TRANSPIRE (AUPR = 0.2368), BANDLE(Dirichlet) (AUPR = 0.7046), BANDLE(Pólya-Gamma) (AUPR = 0.6858) and scGCN (AUPR = 0.5605) on the simulated dataset with random batch effects.…”

Section: Resultsmentioning

confidence: 99%

TransGCN: a semi-supervised graph convolution network–based framework to infer protein translocations in spatio-temporal proteomics

Wang,

Zhang,

Han

et al. 2024

Briefings in Bioinformatics

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Protein subcellular localization (PSL) is very important in order to understand its functions, and its movement between subcellular niches within cells plays fundamental roles in biological process regulation. Mass spectrometry–based spatio-temporal proteomics technologies can help provide new insights of protein translocation, but bring the challenge in identifying reliable protein translocation events due to the noise interference and insufficient data mining. We propose a semi-supervised graph convolution network (GCN)–based framework termed TransGCN that infers protein translocation events from spatio-temporal proteomics. Based on expanded multiple distance features and joint graph representations of proteins, TransGCN utilizes the semi-supervised GCN to enable effective knowledge transfer from proteins with known PSLs for predicting protein localization and translocation. Our results demonstrate that TransGCN outperforms current state-of-the-art methods in identifying protein translocations, especially in coping with batch effects. It also exhibited excellent predictive accuracy in PSL prediction. TransGCN is freely available on GitHub at https://github.com/XuejiangGuo/TransGCN.

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Section: Resultsmentioning

confidence: 99%

TransGCN: a semi-supervised graph convolution network–based framework to infer protein translocations in spatio-temporal proteomics

Wang,

Zhang,

Han

et al. 2024

Briefings in Bioinformatics

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Integrated edge information and pathway topology for drug-disease associations

Li,

Zan,

Liu

et al. 2024

iScience

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IA´ Tools for the development of investigative skills

Gaviria Alvarado

2023

LatIA

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This article explores how the artificial intelligence (IA) it is transforming the education in natural sciences by means of strategies pedagogic innovators. The IA allows the learning personalization, adjusting the content and the rhythm to the individual necessities of the students, what improves the understanding and retention of complex concepts significantly. Also, the use of simulations and virtual models believe interactive and visual learning environments, enriching the educational experience. These tools also foment the development of critical and creative skills, promoting a more active and collaborative approach in the resolution of scientific problems. On the whole, these strategies not only improve the effectiveness of learning, but rather they also prepare the students to face the challenges of the XXI century with a solid base in science and technology.

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A drug repurposing method based on inhibition effect on gene regulatory network

Cited by 3 publications

References 76 publications

TransGCN: a semi-supervised graph convolution network–based framework to infer protein translocations in spatio-temporal proteomics

TransGCN: a semi-supervised graph convolution network–based framework to infer protein translocations in spatio-temporal proteomics

Integrated edge information and pathway topology for drug-disease associations

IA´ Tools for the development of investigative skills

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