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SCIENTIA SINICA Informationis, Volume 51 , Issue 6 : 927(2021) https://doi.org/10.1360/SSI-2019-0142

Prediction of circRNA-disease associations based on multiple biological data

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  • ReceivedJul 4, 2019
  • AcceptedJan 5, 2020
  • PublishedMay 13, 2021

Abstract


Funded by

国家自然科学基金(61672334,61972451,61902230)

中央高校基本科研业务费专项资金(201901010)


Author information






References

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  • Figure 1

    (Color online) The effect of different parameter values for EDNMF algorithm in 10-fold cross-validation. protectłinebreak (a) $\lambda_1=0.25$; (b) $\lambda_1=0.5$; (c) $\lambda_1=1$; (d) $\lambda_1=1.5$; (e) $\lambda_1=2$; (f) $\lambda_1=3$

  • Figure 1

    (Color online) The effect of different parameter values for EDNMF algorithm in 10-fold cross-validation. protectłinebreak (a) $\lambda_1=0.25$; (b) $\lambda_1=0.5$; (c) $\lambda_1=1$; (d) $\lambda_1=1.5$; (e) $\lambda_1=2$; (f) $\lambda_1=3$

  • Figure 2

    (Color online) Compared with different algorithm on ROC curve and AUC value. (a) The performance of 5-fold cross-validation; (b) the performance of 10-fold cross-validation

  • Figure 2

    (Color online) Compared with different algorithm on ROC curve and AUC value. (a) The performance of 5-fold cross-validation; (b) the performance of 10-fold cross-validation

  • Figure 3

    (Color online) Comparison of different algorithms on precision. (a) The performance of 5-fold cross-validation; (b) the performance of 10-fold cross-validation

  • Figure 3

    (Color online) Comparison of different algorithms on precision. (a) The performance of 5-fold cross-validation; (b) the performance of 10-fold cross-validation

  • Figure 4

    (Color online) Comparison of different algorithms on 6 diseases in the process of 10-fold cross-validation

  • Figure 4

    (Color online) Comparison of different algorithms on 6 diseases in the process of 10-fold cross-validation

  • Figure 5

    (Color online) The interactive network method validate the top 10 results in predicting the associations between circRNAs and colorectal cancer. Crimson color represents the top 10 circRNAs; light red color represents the known circRNAs associated with colorectal cancer; blue represents gene corresponding to the known circRNAs associated with colorectal cancer; green color represents gene corresponding to the top 10 circRNAs; orange represents genes associated with colorectal cancer

  • Figure 5

    (Color online) The interactive network method validate the top 10 results in predicting the associations between circRNAs and colorectal cancer. Crimson color represents the top 10 circRNAs; light red color represents the known circRNAs associated with colorectal cancer; blue represents gene corresponding to the known circRNAs associated with colorectal cancer; green color represents gene corresponding to the top 10 circRNAs; orange represents genes associated with colorectal cancer

  •   

    Algorithm 1 EDNMF算法: 计算$W$, $H$使$Y~\approx~WH$

    Require:circRNA–疾病关联关系矩阵$Y$, circRNA表达谱矩阵$T$, 疾病相似性矩阵$D$.

    Output: 收敛后的矩阵$Y$.

    初始化基矩阵$W$;

    初始化系数矩阵$H$;

    $i~\Leftarrow~1$;

    while $i~\neq~{\rm~MaxIter}$ do

    更新基矩阵$W$和系数矩阵$H$:

    $W_{ik}~\leftarrow~\frac{(YH'~+~\lambda_2T)_{ik}}{(WH'H~+~(\lambda_1~+~\lambda_2)W)_{ik}}W_{ik}$;

    $H_{ik}~\leftarrow~\frac{(W'Y~+~\lambda_3HD)_{ik}}{(W'WH~+~\lambda_1HH'H~+~\lambda_3H)_{ik}}H_{ik}$;

    更新$Y$矩阵;

    if $Y$矩阵收敛 then

    跳出当前while循环;

    end if

    $i$ = $i$ + 1;

    end while

    Return收敛后的$Y$矩阵.

  •   

    Algorithm 1 EDNMF算法: 计算$W$, $H$使$Y~\approx~WH$

    Require:circRNA–疾病关联关系矩阵$Y$, circRNA表达谱矩阵$T$, 疾病相似性矩阵$D$.

    Output: 收敛后的矩阵$Y$.

    初始化基矩阵$W$;

    初始化系数矩阵$H$;

    $i~\Leftarrow~1$;

    while $i~\neq~{\rm~MaxIter}$ do

    更新基矩阵$W$和系数矩阵$H$:

    $W_{ik}~\leftarrow~\frac{(YH'~+~\lambda_2T)_{ik}}{(WH'H~+~(\lambda_1~+~\lambda_2)W)_{ik}}W_{ik}$;

    $H_{ik}~\leftarrow~\frac{(W'Y~+~\lambda_3HD)_{ik}}{(W'WH~+~\lambda_1HH'H~+~\lambda_3H)_{ik}}H_{ik}$;

    更新$Y$矩阵;

    if $Y$矩阵收敛 then

    跳出当前while循环;

    end if

    $i$ = $i$ + 1;

    end while

    Return收敛后的$Y$矩阵.

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