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paper/paper.md

@@ -47,7 +47,7 @@ In response to the above issues, previous studies on interactive visualization t
 We first scale and cluster the trajectories across all molecular features to depict the longitudinal changes. For clustering, we use K-means and a built-in elbow method to choose the optimal number. Then, we predict a co-expression network for the extracted patterns, similar to GENIE3 [@GENIE3] creates gene regulatory networks. We also divide the prediction process into individual regression tasks. Each central pattern of a cluster is predicted from the expression patterns of all the other central patterns, using random forests. It is chosen because of its potential to model interacting features and non-linearity without strong assumptions. The Mean Decrease Accuracy of a subset of top predictors whose expression directly influences the expression of the target cluster is taken as an indication of a putative link. That is to say, based on the random forest prediction, if two groups of features are highly linked according to the network, they will have strongly related longitudinal patterns, as shown in Fig \ref{fig:pattern}.
 
 ## Network Navigation
-Navigating the network in the MolPad dashboard follows three steps: First, choose a primary functional annotation. Adjustment options for fine-tuning include network layout and importance threshold for edge density. Bright green notes (Fig \ref{fig:dashboard}.A) represent clusters containing the most features in the chosen functional annotation. Second, brushing on the network reveals patterns of taxonomic composition (Fig \ref{fig:dashboard}.B) and typical trajectories  (Fig \ref{fig:dashboard}.C). The user can also zoom into specific taxonomic annotations by filtering. Third, they may view the feature table (Fig \ref{fig:dashboard}.D), examine the drop-down options for other related function annotations, and click the link for online details for the items of interest. The interface is designed to support iterative exploration, encouraging the use of several steps to answer specific questions, like comparing the distributional patterns between two functions or finding functionally important community members metabolizing a feature of interest. Overall, this aggregation adopted the focus-plus-context approach to address the low interoperability of the network graph, facilitating the examination of high-level details for individual features while providing contextual information about cluster interactions among microbiome data.
+Navigating the network in the MolPad dashboard follows three steps: First, choose a primary functional annotation. Adjustment options for fine-tuning include network layout and importance threshold for edge density. Bright green notes (Fig \ref{fig:dashboard}.A) represent clusters containing the most features in the chosen functional annotation. Second, brushing on the network reveals patterns of taxonomic composition (Fig \ref{fig:dashboard}.B) and typical trajectories  (Fig \ref{fig:dashboard}.C). The user can also zoom into specific taxonomic annotations by filtering. Third, they may view the feature table (Fig \ref{fig:dashboard}.D), examine the drop-down options for other related function annotations, and click the link for online details for the items of interest. The interface is designed to support iterative exploration, encouraging the use of several steps to answer specific questions, like comparing the distributional patterns between two functions or finding functionally important community members metabolizing a feature of interest. By applying a focus-plus-context approach, we can bridge the examination of high-level details related to individual features with contextual information about cluster interactions within the network visualization.
 
 # Case Study: Cheese Data