Overall system architecture.

<p>We implemented both the one-stage and the two-stage method. (a) Data generation part. (b) One-stage method. Five-class type classifier for the one-stage method. (c) Two-stage method. The DDI detection classifier distinguishes positive DDI instances from negative instances. The DDI type classifier receives the predicted positive instances from the detection classifier as a testing set.</p

Examples of three common types of error cases.

<p>Examples of three common types of error cases.</p

The statistics from the PK DDI corpus after preprocessing.

<p>The statistics from the PK DDI corpus after preprocessing.</p

Comparison between our proposed model and existing models.

<p>Comparison between our proposed model and existing models.</p

Overall system architecture.

<p>We implemented both the one-stage and the two-stage method. (a) Data generation part. (b) One-stage method. Five-class type classifier for the one-stage method. (c) Two-stage method. The DDI detection classifier distinguishes positive DDI instances from negative instances. The DDI type classifier receives the predicted positive instances from the detection classifier as a testing set.</p

Performance changes of our model on the PK DDI in vivo dataset by removing features.

<p>Performance changes of our model on the PK DDI in vivo dataset by removing features.</p

Search process to find the best hyperparameters used for our model.

<p>Search process to find the best hyperparameters used for our model.</p

DDI relation types and explanations.

<p>DDI relation types and explanations.</p

Automatic Context-Specific Subnetwork Discovery from Large Interaction Networks

<div><p>Genes act in concert via specific networks to drive various biological processes, including progression of diseases such as cancer. Under different phenotypes, different subsets of the gene members of a network participate in a biological process. Single gene analyses are less effective in identifying such core gene members (subnetworks) within a gene set/network, as compared to gene set/network-based analyses. Hence, it is useful to identify a discriminative classifier by focusing on the subnetworks that correspond to different phenotypes. Here we present a novel algorithm to automatically discover the important subnetworks of closely interacting molecules to differentiate between two phenotypes (context) using gene expression profiles. We name it COSSY (COntext-Specific Subnetwork discoverY). It is a non-greedy algorithm and thus unlikely to have local optima problems. COSSY works for any interaction network regardless of the network topology. One added benefit of COSSY is that it can also be used as a highly accurate classification platform which can produce a set of interpretable features.</p></div

The architecture of our recursive neural network model.

<p>Our model is a variation of the binary tree-LSTM model. (1) The words in a sentence. The names of drug targets are underlined. (2) Vector representation of a word through the word embedding lookup process. (3) Subtree containment feature represents the importance of a node. (4) Position feature vector representing the relative distance of two target drugs from the current word position. (5) An example of the position feature vector. The current word is “accelerated.” (6) The size of the concatenated vector input <i>x</i>₀ of our model is 10 (size of the subtree containment feature; (3) in the figure) + 20 (size of the position feature; (4) in the figure) + 200 (size of the word embedding; (2) in the figure).</p