miRbiom

The goal was to create predictive models of miRNA expression based on the gene expression data. The RBP interaction network and its associated proteins obtained from miRNA biogenesis model based on the Bayesian network analysis were used for prediction of the miRNA expression level. The XGBoost regression model was used to build predictive model. XGBoost stands for extreme gradient boosting. For a given dataset with n samples and m features, $$D=\left\{\left(X_{i},y_{i}\right)\right\}\left(|D|=n,X_{i}\varepsilon R^{m},y_{i}\varepsilon R\right)$$ a tree ensemble model uses K additive function to predict the output $$ estimate\left(y_{i}\right)=Φ\left(X_{i}\right)= \sum_{k=1}^{K}f_{k}\left(X_{i}\right),f_{k}\varepsilon F...............(1) $$ where $$F=f_{k}=w_{q\left(x\right)}\left(R_{m}→T,W\varepsilon R^{T}\right)$$ is the space of regression trees. Here ‘q ‘represents the structure of each tree that maps an example to the corresponding leaf index. T is the number of leaves in a tree. Each $ f_{k} $ corresponds to an independent tree structure q and leaf weight w. Unlike decision trees, each regression tree contains a continuous score on each of leaf, we use $w_{i}$ to represent score on i-th leaf. To learn the set of functions used in the model, we minimize the following regularized objective $$ L\left(Φ\right)= \sum_{i}l\left(estimate\left(y_{i}\right),y_{i}\right)+\sum_{k}\Omega \left(f_{k}\right)...............(2) $$ where, $$ \Omega \left(f_{k}\right)= \gamma^{T}+\frac{1}{2}\lambda ||w||^{2}$$ Here ‘ι’ is a differentiable convex loss function that measure the difference between the prediction $estimate(y_{i}$ and target $y_{i})$ . The second term, Ω, penalizes the complexity of the regression tree function. The additional regulizer term helps to smoothen the final learned weight to avoid over-fitting. Intuitively, the regularized objective will tend to select a model employing simple and predictive functions.

The RNA-seq and sRNA-seq expression data were collected from TCGA database for seven different tissues (both normal and cancer). These data are independent of the 47 experimental conditions used in the construction of miRNA biogenesis models. Additional eight different tissue condition data were used separately from TCGA for further validation of the tool. The predictive models were validated considering the following statistical measures: $$ RMSE= { \sqrt{\sum_{i=1}^{n}{\left(Predicted\left(y_{i}\right) - observed\left(y_{i}\right)\right)^{2} \over n}}}..............(3) $$ To check the predictive accuracy of each model Relative mean absolute percentage error (RMAPE) was used. The RMAPE is widely used to validate forecast accuracy, which provides an indication of the average size of prediction error expressed as a percentage of the relevant observed value irrespective of whether that prediction error is positive or negative. $$ RMAPE= \frac{1}{n}{ \sum_{i=1}^{n}|\left(observed\left(y_{i}\right) - Predicted\left(y_{i}\right)\right)| \over observed\left(y_{i}\right)}\times100 .....(4)$$ $$ Model\space Accuracy = 100 - RMAPE ..............................(5) $$ Once the model has been trained. We can't assume, it can work well on data that it hasn't seen before. In other words, it can not be certain that the model will have the desired accuracy and variance in the environment of production. It needs some form of confirmation that the predictions our model is sending out are accurate. For that, our model needs to be validated. The process of determining whether the numerical results which quantify hypothesized relationships between variables are appropriate as data descriptions is known as validation. To evaluate the performance of any machine learning model we need to test it on some unseen data. Based on the models performance on unseen data we can say weather our model is Under-fitting/Over-fitting/Well generalized. Cross-validation (CV) is one of the authentication technique used to test the effectiveness of the build model, and is also a re-sampling procedure used to evaluate that model. We need to hold a sample / portion of the data that is not used to train the model to perform a CV. A 10-fold cross-validation method was applied to verify the accuracy of the models, and accuracy was tested based on the RMSE value.

Figure-3: This diagram used zoomable graphic and tabular view to illustrate predicted mature-miRNA. Bubble size shows log2 expression value where larger size has higher expression value. Each bubble is linked with enrichment analysis. A searchable and downloadable table with links for further study for a specific mature-miRNA along with its enricment analysis button is also provided here.

To test how close miRbiom predicts to the actual expression profile, this module has been provided. The user needs to provide actual miRNAs expression data of same experimental condition. After submission of the data we get results from the webserver which provides accuracy(%), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and line plot of expression value between actual vs predicted miRNAs using plotly.js.

Figure-4: This snapshot shows the process and results of benchmarking the model using actual miRNAs expression data fron the same experimental condition.

Enrichment analysis is done using Enrichr in the background. Snapshot of tabular view of enrichment analysis result given below when clicking on bubble (ex. hsa-let-7a-2-3p) in bubble plot or clicking on GE Analysis column of corresponding to miRNA (ex. hsa-let-7a-2-3p) in expression table.

Figure-5: This snapshot shows the results of the enrichment analysis. Click on the link in the table of any pathway to get map their associated genes with the KEGG pathway.

Path Mapper is a tool for integration and visualisation of data based on the KEGG pathway. Path mapper was implemented using PATHVIEW API. The path mapper mapps the gene where the log2(expression value) > = 0 is colored with yellow color and log2(expression value) less than 0 is colored with red in the pathway graph.

Figure-6: This image illustrated mapped pathway of associated gens of selected KEGG pathway