Bai et al 2023a - Revision history

Pingzhang at 03:13, 5 March 2024

2024-03-05T03:13:45Z

Pingzhang at 03:13, 5 March 2024

2024-03-05T03:13:11Z

Pingzhang at 03:12, 5 March 2024

2024-03-05T03:12:26Z

Pingzhang at 03:05, 5 March 2024

2024-03-05T03:05:20Z

Pingzhang at 03:04, 5 March 2024

2024-03-05T03:04:06Z

Pingzhang at 03:03, 5 March 2024

2024-03-05T03:03:32Z

Pingzhang at 08:45, 29 August 2023

2023-08-29T08:45:43Z

Pingzhang at 06:50, 29 August 2023

2023-08-29T06:50:08Z

Rimni at 10:48, 28 August 2023

2023-08-28T10:48:28Z

Rimni: /* 2.5.8 Final loss calculation */

2023-08-28T10:43:18Z

‎2.5.8 Final loss calculation

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 '''Keywords''': Tripartite graph, dual contrast learning, graph convolutional network, miRNA-disease association prediction
-==<sub>Corresponding author: Ping Zhang and Li Li</sub><sub>Subscript text</sub>==
+==<sub>Corresponding author: Ping Zhang and Li Li</sub>==
 ==1. Introduction==

@@ Line 19: / Line 19: @@
 '''Keywords''': Tripartite graph, dual contrast learning, graph convolutional network, miRNA-disease association prediction
-==Corresponding author: Ping Zhang and Li Li==
+==<sub>Corresponding author: Ping Zhang and Li Li</sub><sub>Subscript text</sub>==
 ==1. Introduction==

@@ Line 19: / Line 19: @@
 '''Keywords''': Tripartite graph, dual contrast learning, graph convolutional network, miRNA-disease association prediction
-Corresponding author: Ping Zhang and Li Li
+==Corresponding author: Ping Zhang and Li Li==
 ==1. Introduction==

@@ Line 19: / Line 19: @@
 '''Keywords''': Tripartite graph, dual contrast learning, graph convolutional network, miRNA-disease association prediction
-Corresponding author: Ping Zhang and LiLi
+Corresponding author: Ping Zhang and Li Li
 ==1. Introduction==

@@ Line 19: / Line 19: @@
 '''Keywords''': Tripartite graph, dual contrast learning, graph convolutional network, miRNA-disease association prediction
-corresponding author: Ping Zhang and LiLi
+Corresponding author: Ping Zhang and LiLi
 ==1. Introduction==

← Older revision		Revision as of 03:03, 5 March 2024
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	'''Keywords''': Tripartite graph, dual contrast learning, graph convolutional network, miRNA-disease association prediction		'''Keywords''': Tripartite graph, dual contrast learning, graph convolutional network, miRNA-disease association prediction
		+
		+	corresponding author: Ping Zhang and LiLi

	==1. Introduction==		==1. Introduction==

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 |-
-|style="text-align: center;"| [[Image:Draft_Ping_628687260-image5.png|388px]]
+|style="text-align: center;"| [[File:Bai_et_al_2023a_6281_auc_com.png|388px]]
-|style="text-align: center;"| [[Image:Draft_Ping_628687260-image6.png|center|388px]]
+|style="text-align: center;"| [[File:Bai_et_al_2023a_2540_aupr_com.png|center|388px]]
 |-
 |style="text-align: center;font-size: 75%;padding-bottom:10px;"|(a)

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 <big>'''TDCMDA: Tripartite graph-based integrating dual-layer contrast learning into graph convolutional network for miRNA-disease association identification'''</big></div>
-'''Jing Bai<sup>1</sup>, Ping Zhang<sup>1</sup>''' ''<sup>*</sup>'' ''' and Li Li<sup>1,2 </sup>''' ''<sup>*</sup>''
+'''Jing Bai<sup>1</sup>, Ping Zhang<sup>1</sup>''' ''<sup>*</sup>'' ''' and Li Li<sup>1,2</sup>''' ''<sup>*</sup>''
 ''<sup>1 </sup>Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, P.R. China''

← Older revision		Revision as of 10:48, 28 August 2023
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−	Breast neoplasms are the most prevalent cancer among women in the world and the second most common malignancy in China. [43]. In [[#tab-2\|Table 2]] of the predicted results of TDCMDA, we can observe that the HMDD v3.2 database has verified 48 of the top 50 miRNAs anticipated to be linked to breast neoplasms. The 36th-ranked hsa-mir-10b, a common breast cancer oncogenic factor, was one of the first miRNAs found to affect cancer metastasis and could promote cellular value addition and migration in breast cancer through the FUT8/p-AKT axis[46]. The 47th-ranked hsa-mir-206 was the first miRNA associated with breast carcinogenesis when Lorio et al. compared the distinction between typical and abnormal miRNA expression and breast neoplasms tissues. In research by Ge et al., it was discovered that Hsa-mir-206 directly inhibited the PFKFB3 molecule, having an impact on the glycolytic process as well as cellular value addition and migration in breast cancer cells ~~[47]~~.	+	Breast neoplasms are the most prevalent cancer among women in the world and the second most common malignancy in China. [43]. In [[#tab-2\|Table 2]] of the predicted results of TDCMDA, we can observe that the HMDD v3.2 database has verified 48 of the top 50 miRNAs anticipated to be linked to breast neoplasms. The 36th-ranked hsa-mir-10b, a common breast cancer oncogenic factor, was one of the first miRNAs found to affect cancer metastasis and could promote cellular value addition and migration in breast cancer through the FUT8/p-AKT axis [46]. The 47th-ranked hsa-mir-206 was the first miRNA associated with breast carcinogenesis when Lorio et al. compared the distinction between typical and abnormal miRNA expression and breast neoplasms tissues. In research by Ge et al. [47], it was discovered that Hsa-mir-206 directly inhibited the PFKFB3 molecule, having an impact on the glycolytic process as well as cellular value addition and migration in breast cancer cells.

	<div class="center" style="font-size: 75%;">'''Table 2'''. Validation results of predicted associations for breast neoplasms as an unknown disease</div>		<div class="center" style="font-size: 75%;">'''Table 2'''. Validation results of predicted associations for breast neoplasms as an unknown disease</div>

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 ====2.5.8 Final loss calculation====
-The decoder we use is inner score, which applies the sigmoid function after linear transformation of the input to obtain the final prediction score.Using the approach suggested by Rendle et al. [33], the relationship of miRNA-disease was represented as a triple, where m represents miRNA, d represents disease, and l represents the associated label of miRNA-disease. A miRNA ranking function for each disease association was learned through the BPR model, maximizing the marginal probabilities described above
+The decoder we use is inner score, which applies the sigmoid function after linear transformation of the input to obtain the final prediction score.Using the approach suggested by Rendle et al. [33], the relationship of miRNA-disease was represented as a triple, where m represents miRNA, <math>d</math> represents disease, and <math>l</math> represents the associated label of miRNA-disease. A miRNA ranking function for each disease association was learned through the BPR model, maximizing the marginal probabilities described above
 {| class="formulaSCP" style="width: 100%; text-align: center;"