Qu Niu 2023a - Revision history

Rimni at 09:22, 23 June 2023

2023-06-23T09:22:52Z

Rimni: /* 2.3 Model architecture */

2023-06-23T09:21:28Z

‎2.3 Model architecture

Rimni at 14:10, 22 June 2023

2023-06-22T14:10:50Z

Rimni at 14:07, 22 June 2023

2023-06-22T14:07:51Z

Rimni: /* References */

2023-06-22T14:01:03Z

‎References

Rimni at 13:18, 22 June 2023

2023-06-22T13:18:41Z

Rimni at 11:18, 22 June 2023

2023-06-22T11:18:23Z

Rimni: /* 3. Results */

2023-06-22T11:17:28Z

‎3. Results

Rimni at 11:14, 22 June 2023

2023-06-22T11:14:53Z

Show changes

Rimni: /* 2.4 Experimental setting and hyperparameters */

2023-06-22T09:49:10Z

‎2.4 Experimental setting and hyperparameters

@@ Line 194: / Line 194: @@
 We used ten-fold cross-validation (10-CV) to test the performance of dpTF-GCN. Because of the limited number of true labeled genes, we randomly selected 10 genes as the validation dataset for case study and excluded them from the training and testing process. The remaining known associated genes were randomly divided into ten equal-sized subsets. We repeated the cross-validation process ten times and used each subset as the testing dataset once while using the other nine subsets as the training dataset. We chose AUC and AUPR as the main evaluation metrics because they can measure the performance of model without a specific threshold. We also computed some threshold-based metrics such as precision (PRE), recall (REC), accuracy (ACC) and F1-score (F1).
-The hyperparameters in dpTF-GCN are the number of layers <math display="inline">y\in \left\{ 2,3\right\}</math>, learning rate of optimizer <math display="inline">\gamma \in \left\{ 0.00001,\, 0.00005,\, 0.0001,\, 0.0002,\, 0.0005\right\}</math>  and the total training epochs <math display="inline">\alpha \in \left\{ 1000,\, 2000,\, 4000,\, 6000,\, 8000\right\}</math>. By adjusting the parameters empirically, we set <math display="inline">y=</math><math>3</math>, <math display="inline">\gamma =0.0001</math> and <math display="inline">\alpha =</math><math>4000</math> for dpTF-GCN in the following experiments.
+The hyperparameters in dpTF-GCN are the number of layers <math display="inline">y\in \left\{ 2,3\right\}</math>, learning rate of optimizer <math display="inline">\gamma \in \left\{ 0.00001,\, 0.00005,\,\right.</math><math display="inline">\left.0.0001,\, 0.0002,\, 0.0005\right\}</math>  and the total training epochs <math display="inline">\alpha \in \left\{ 1000,\, 2000,\, 4000,\right.</math><math display="inline">\left.\, 6000,\, 8000\right\}</math>. By adjusting the parameters empirically, we set <math display="inline">y=</math><math>3</math>, <math display="inline">\gamma =0.0001</math> and <math display="inline">\alpha =</math><math>4000</math> for dpTF-GCN in the following experiments.
 ==3. Results==

@@ Line 185: / Line 185: @@
 {| class="wikitable" style="margin: 0em auto 0.1em auto;border-collapse: collapse;width:auto;"
 |-style="background:white;"
-|style="text-align: center;padding:10px;"| [[Image:Draft_Qu_612822743-image1.png|600px]]
+|style="text-align: center;padding:10px;"| [[Image:Draft_Qu_612822743-image1.png|700px]]
 |-
 | style="background:#efefef;text-align:left;padding:10px;font-size: 85%;"| '''Figure 1'''. Workflow of dpTF-GCN

@@ Line 228: / Line 228: @@
 {| class="wikitable" style="margin: 0em auto 0.1em auto;border-collapse: collapse;width:auto;"
 |-style="background:white;"
-|style="text-align: center;padding:10px;"| [[Image:Draft_Qu_612822743-image4.png|360px]]
+|style="text-align: center;padding:10px;"| [[Image:Draft_Qu_612822743-image4.png|400px]]
 |-
 | style="background:#efefef;text-align:left;padding:10px;font-size: 85%;"| '''Figure 4'''. Comparisons with other different methods on same dataset
 |}
 ===3.3 Ablation experiment of graph components===

@@ Line 214: / Line 214: @@
 <div id='img-3'></div>
-{| class="wikitable" style="margin: 0em auto 0.1em auto;border-collapse: collapse;width:70%;"
+{| class="wikitable" style="margin: 0em auto 0.1em auto;border-collapse: collapse;width:80%;"
 |-style="background:white;"
-|style="text-align: center;padding:10px;"| [[Image:Draft_Qu_612822743-image3.png|550px]]
+|style="text-align: center;padding:10px;"| [[Image:Draft_Qu_612822743-image3.png|650px]]
 |-
 | style="background:#efefef;text-align:left;padding:10px;font-size: 85%;"| '''Figure 3'''. Performance during dpTF-GCN training. (a) AUC and AUPR curves generated by dpTF-GCN on each fold dataset. (b) Training loss curves of each fold dataset

@@ Line 415: / Line 415: @@
 [30] Fang L., Li Y., Ma L., Xu Q., Tan F. et al. Grndb: Decoding the gene regulatory networks in diverse human and mouse conditions. Nucleic Acids Res., 49:D97-D103, 2020.
-[31] Piñero J., Saüch J., Sanz F. and Furlong L.I. The DisGeNET cytoscape app: Exploring and visualizing disease genomics data. Comput. and Struct. Biotechnol. J., 19:2960-2967, 2021.
+[31] Piñero J., Saüch J., Sanz F., Furlong L.I. The DisGeNET cytoscape app: Exploring and visualizing disease genomics data. Comput. Struct. Biotechnol. J., 19:2960-2967, 2021.
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+[33] Joshi A., Rienks M., Theofilatos K., Mayr M. Systems biology in cardiovascular disease: A multiomics approach. Nat. Rev. Cardiol., 18:313-330, 2021.
 [34] Gaudelet T., Day B., Jamasb A.R., Soman J., Regep C. et al. Utilizing graph machine learning within drug discovery and development. Brief. Bioinformatics, 22:bbab159, 2021.
-[35] Yu Z., Huang F., Zhao X., Xiao W. and Zhang W. Predicting drug–disease associations through layer attention graph convolutional network. Brief. Bioinformatics, 22:bbaa243, 2021.
+[35] Yu Z., Huang F., Zhao X., Xiao W., Zhang W. Predicting drug–disease associations through layer attention graph convolutional network. Brief. Bioinformatics, 22:bbaa243, 2021.
 [36] Deng Y., Xu X., Qiu Y., Xia J., Zhang W. et al. A multimodal deep learning framework for predicting drug–drug interaction events. Bioinformatics, 36:4316-4322, 2020.
-[37] Kipf T.N. and Welling M. Semi-supervised classification with graph convolutional networks. arXiv, 1609.02907, 2016.
+[37] Kipf T.N., Welling M. Semi-supervised classification with graph convolutional networks. arXiv, 1609.02907, 2016.
-[38] Defferrard M., Bresson X. and Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. Adv. Neural Inf. Process Syst., 29, 2016.
+[38] Defferrard M., Bresson X., Vandergheynst P. Convolutional neural networks on graphs with fast localized spectral filtering. Proceedings of the 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, pp. 3844–3852, 2016.
-[39] Hammond D.K., Vandergheynst P. and Gribonval R. Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal., 30:129-150, 2011.
+[39] Hammond D.K., Vandergheynst P., Gribonval R. Wavelets on graphs via spectral graph theory. Appl. Comput. Harmon. Anal., 30:129-150, 2011.
-[40] Xu J., Cai L., Liao B., Zhu W., Wang P. et al. Identifying potential mirnas–disease associations with probability matrix factorization. Front. Genet., 10, 2019.
+[40] Xu J., Cai L., Liao B., Zhu W., Wang P. et al. Identifying potential mirnas–disease associations with probability matrix factorization. Front. Genet., 10, 1234, 2019.
 [41] Guo Z.H., You Z.H., Huang D.S., Yi H.C., Zheng K. et al. Meshheading2vec: A new method for representing mesh headings as vectors based on graph embedding algorithm. Brief. Bioinformatics, 22:2085-2095, 2020.
-[42] Xie M., Xu Y., Zhang Y., Hwang T. and Kuang R. Network-based phenome-genome association prediction by bi-random walk. PLoS ONE, 10:e0125138, 2015.
+[42] Xie M., Xu Y., Zhang Y., Hwang T., Kuang R. Network-based phenome-genome association prediction by bi-random walk. PLoS ONE, 10:e0125138, 2015.
-[43] Shang J. and Sun Y. Cherry: A computational method for accurate prediction of virus–prokaryotic interactions using a graph encoder–decoder model. Brief. Bioinformatics, 23:bbac182, 2022.
+[43] Shang J., Sun Y. Cherry: A computational method for accurate prediction of virus–prokaryotic interactions using a graph encoder–decoder model. Brief. Bioinformatics, 23:bbac182, 2022.
-[44] Li C.C. and Liu B. Motifcnn-fold: Protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks. Brief. Bioinformatics, 21:2133-2141, 2019.
+[44] Li C.C., Liu B. Motifcnn-fold: Protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks. Brief. Bioinformatics, 21:2133-2141, 2019.

@@ Line 333: / Line 333: @@
 |}
-=4 Discussions=
+==4. Discussions==
 Network-based methods are widely used for predicting and analyzing the associations between biological entities, such as genes and diseases. In this study, we propose a novel GCN-based computational approach, called dpTF-GCN, to predict the genes that are associated with degenerative polyarthritis. Our method dpTF-GCN can automatically learn the low-dimensional representations of genes and transcription factors by systematically integrating the complex topology of the heterogeneous network that consists of gene regulatory relationships, the neighborhood information of genes and transcription factors, and the gene- and transcription factor-specific attributes. The learned embeddings and the associated genes classification models are jointly optimized in an end-to-end fashion. We conduct extensive experiments to evaluate the performance of our method on recovering missing associated genes from the training data, and on discovering novel potential associated genes for degenerative polyarthritis that are not present in the training data. Satisfactory results confirm the excellent performance of dpTF-GCN.
@@ Line 341: / Line 341: @@
 Degenerative polyarthritis is a condition that causes inflammation and pain in multiple joints and there is a need for more effective and personalized treatments for it. Our research aims to address this need by providing a novel approach for identifying potential associated genes for degenerative polyarthritis. We use a sophisticated GCN model that can capture both local and global features of the gene regulatory information. The dpTF-GCN not only offers as a new way to treat degenerative polyarthritis at the molecular level but also provides a valuable resource and method for screening drug targets. Furthermore, our research also has implications for the integration of artificial intelligence and precision medicine.
-'''Acknowledgement:''' The authors thank to lab members for assistance.
+==Acknowledgement==
-'''Funding Statement:''' This study was supported  by the National Social Science Fund West Project of China (21XJY015, to Zhenggeng Qu) and Shaanxi Provincial Philosophy and Social Science Research Project (2023QN0233, to Zhenggeng Qu) .
+The authors thank to lab members for assistance.
 '''Conflicts of Interest: '''The authors declare that they have no conflicts of interest to report regarding the present study.
@@ Line 349: / Line 353: @@
 ==References==
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+<div class="auto" style="text-align: left;width: auto; margin-left: auto; margin-right: auto;font-size: 85%;">
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+[7] Chen X., Sun L.G., Zhao Y. Ncmcmda: Mirna-disease association prediction through neighborhood constraint matrix completion. Brief. Bioinformatics, 22:485-496, 2020.
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+[9] Chen X., Zhu C.C., Yin J. Ensemble of decision tree reveals potential mirna-disease associations. PLoS Comput. Biol., 15:e1007209, 2019.
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@@ Line 295: / Line 295: @@
 |  0.911
 |  style="text-align:left;"|PMID:22447075, PMID:30317598, PMID:24934217, PMID:29511609, PMID:28343378, PMID:29845461
-|-
+|-style="text-align:center"
 | TBX5
 | 0.910
 | style="text-align:left;"|PMID:31376087, PMID:25320281
-|-
+|-style="text-align:center"
 | IL33
 | 0.903
 | style="text-align:left;"|PMID:29867945, PMID:26520876, PMID:21441054, PMID:29095435
-|-
+|-style="text-align:center"
 | RNR2
 | 0.898
 | style="text-align:left;"|PMID:15567815
-|-
+|-style="text-align:center"
 | SAA2
 | 0.877
 | style="text-align:left;"|PMID:25849372
-|-
+|-style="text-align:center"
 | CPB1
 | 0.871
 | style="text-align:left;"|PMID:24449579, PMID:21804193
-|-
+|-style="text-align:center"
 | MGP
 | 0.866
 | style="text-align:left;"|PMID: 21724703, PMID: 31215457, PMID: 28855172
-|-
+|-style="text-align:center"
 | TNNC1
 | 0.855
 | style="text-align:left;"|PMID:21762512, PMID:28722504
-|-
+|-style="text-align:center"
 | TKTL1
 | 0.843
 | style="text-align:left;"|PMID:28719557, PMID:27996342, PMID:29143404, PMID:30671597
-|-
+|-style="text-align:center"
 | PRPF3
 | 0.829

@@ Line 190: / Line 190: @@
 |}
-==2.4 Experimental setting and hyperparameters==
+===2.4 Experimental setting and hyperparameters===
 We used ten-fold cross-validation (10-CV) to test the performance of dpTF-GCN. Because of the limited number of true labeled genes, we randomly selected 10 genes as the validation dataset for case study and excluded them from the training and testing process. The remaining known associated genes were randomly divided into ten equal-sized subsets. We repeated the cross-validation process ten times and used each subset as the testing dataset once while using the other nine subsets as the training dataset. We chose AUC and AUPR as the main evaluation metrics because they can measure the performance of model without a specific threshold. We also computed some threshold-based metrics such as precision (PRE), recall (REC), accuracy (ACC) and F1-score (F1).
-The hyperparameters in dpTF-GCN are the number of layers <math display="inline">y\in \left\{ 2,3\right\}</math> , learning rate of optimizer <math display="inline">\gamma \in \left\{ 0.00001,\, 0.00005,\, 0.0001,\, 0.0002,\, 0.0005\right\}</math>  and the total training epochs <math display="inline">\alpha \in \left\{ 1000,\, 2000,\, 4000,\, 6000,\, 8000\right\}</math> . By adjusting the parameters empirically, we set <math display="inline">y=</math><math>3</math>, <math display="inline">\gamma =0.0001</math> and <math display="inline">\alpha =</math><math>4000</math> for dpTF-GCN in the following experiments.
+The hyperparameters in dpTF-GCN are the number of layers <math display="inline">y\in \left\{ 2,3\right\}</math>, learning rate of optimizer <math display="inline">\gamma \in \left\{ 0.00001,\, 0.00005,\, 0.0001,\, 0.0002,\, 0.0005\right\}</math>  and the total training epochs <math display="inline">\alpha \in \left\{ 1000,\, 2000,\, 4000,\, 6000,\, 8000\right\}</math>. By adjusting the parameters empirically, we set <math display="inline">y=</math><math>3</math>, <math display="inline">\gamma =0.0001</math> and <math display="inline">\alpha =</math><math>4000</math> for dpTF-GCN in the following experiments.
 =3 Results=