Identification of Specific Role of SNX Family in Gastric Cancer Prognosis Evaluation

2021 ◽  
Author(s):  
Beibei Hu ◽  
Guohui Yin ◽  
Xuren Sun
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Artificial Neural Network ◽  
Predictive Power ◽  
Bioinformatic Analysis ◽  
Specific Role ◽  
Immune Infiltration ◽  
Kaplan Meier ◽  
Artificial Neural

Abstract Project: We here perform a systematic bioinformatic analysis to uncover the role of SNX family in clinical outcome of gastric cancer (GC). Methods: Comprehensive bioinformatic analysis were realized with online tools such as TCGA, GEO, String, Timer, cBioportal and Kaplan-Meier Plotter. Statistic analysis was conducted with R language, and artificial neural network was constructed using Python.Results: Our analysis demonstrated that SNX4/5/6/7/8/10/13/14/15/16/20/22/25/27/30 were higher expressed in GC, whereas SNX1/17/21/24/33 were in the opposite expression profiles. Clustering results gave the relative transcriptional levels of 30 SNXs in tumor, and it was totally consistent to the inner relevance of SNXs at mRNA level. Protein-Protein Interaction (PPI) map showed closely and complex connection among 33 SNXs. Tumor immune infiltration analysis asserted that SNX1/3/9/18/19/21/29/33, SNX1/17/18/20/21/29/31/33, SNX1/2/3/6/10/18/29/33, and SNX1/2/6/10/17/18/20/29 were strongly correlated with four kinds of survival related TIICs, including Cancer associated fibroblast, endothelial cells, macrophages and Tregs. Kaplan-Meier survival analysis based on GEO presented more satisfactory results than that based on TCGA-STAD did, and all the 29 SNXs were statistically significant, SNX12/23/28 excluded. SNXs alteration contributed to MSI or higher level of MSI-H (hyper-mutated MSI or high level of MSI), and other malignancy such as mutation of TP53, ARID1A and MLH1.The multivariate cox model performed excellently in patients risk classification, for those with higher risk-score suffered from OS period and susceptibility to death as well as tumor immune infiltration. Compared to previous researches, our ANN models shown a predictive power at a middle-upper level, with AUC of 0.87/0.72, 0.84/0.72, 0.90/0.71, 0.94/0.66, 0.83/0.71, 0.88/0.72 corresponding to one-, three- and five-year OS and RFS estimation, but we were totally sure that those models would perform great better if given larger-size samples, which served as evidence to specific role of SNX family in prognosis assessment in GC. Conclusion: The SNX family shows great value in postoperative survival of GC, and artificial neural network models constructed using SNXs transcriptional data manifest excellent predictive power in both OS and RFS evaluation.

scholarly journals Artificial Neural Network Based Study Predicting GS-441524 As Potential Inhibitor of SARS COV-2 Activator Protein Furin: A Polypharmacology Approach

2021 ◽  
Author(s):  
Dhanalakshmi Menamadathil ◽  
Kajari Das ◽  
Medha Pandya ◽  
Sejal Shah ◽  
Ayushman Gadnayak ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bacterial Infections ◽  
Viral Infections ◽  
Drug Repurposing ◽  
Network Pharmacology ◽  
Self Organizing Maps ◽  
Human Proteins ◽  
Artificial Neural

Abstract Furin, a pro-protein convertase, plays a significant role of biological scissor in bacterial, viral, and even mammalian substrates which in turn decides the fate of many viral and bacterial infections along with the numerous ailments caused by cancer, diabetes, inflammations, and neurological disorders. In the wake of the current pandemic caused by the virus SARS COV-2, furin has become the center of attraction for researchers. In the present work, we have searched for novel inhibitors against this interesting human target from FDA-approved antivirals. To enhance the selection of new inhibitors we employed Kohonen’s-artificial neural network-based self-organizing maps for ligand based virtual screening. Promising results were obtained which can help in drug repurposing and network pharmacology studies addressing the errors due to promiscuity/polypharmacology. We found 15 existing FDA antivirals having the potential to inhibit furin. Among these, six compounds have targets on other important human proteins (LDLR, FCGR1A, PCK1, TLR7, DNA and PNP) also. These 15 drugs inhibiting furin could be studied in patients having many viral infections including SARS COV-2, which is known to have many interacting motifs like NSPs, ORFs, and spike protein. We also propose two promising candidate FDA drugs GS-441524 and Grazoprevir (MK-5172) to repurpose as inhibitors of furin. The best results were observed with GS-441524.

scholarly journals Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

Nanomaterials ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 697 ◽  
Author(s):  
Milad Sadeghzadeh ◽  
Heydar Maddah ◽  
Mohammad Hossein Ahmadi ◽  
Amirhosein Khadang ◽  
Mahyar Ghazvini ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Sol Gel ◽  
Back Propagation ◽  
Thermal Conductivity Ratio ◽  
Self Organizing Map ◽  
Artificial Neural ◽  
The Heat Transfer Coefficient

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.

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