Classification of Imbalanced Bioassay Data with Features Learned Using Stacked Autoencoder

Bioassay data classification is an important task in drug discovery. However, the data used in classification is highly imbalanced, leading to inaccuracies in classification for the minority class. We propose a novel approach for classification in which we train separate models by using different features that are derived by training stacked autoencoders (SAE). Experiments are performed on 7 bioassay datasets, in which each data file consists of feature descriptors for every compound along with class label of compound being active, or inactive. We first perform data cleaning using borderline synthetic minority oversampling technique (SMOTE) followed by removing the Tomek links, and then learn different features hierarchically, based on the cleaned data or feature vectors. We then train separate cost-sensitive feed-forward neural network (FNN) classifiers using the hierarchical features in order to obtain the final classification. To increase the True Positive Rate (TPR), a test sample is labeled as active if at least one classifier predicts it as active. In this paper, we demonstrate that by data cleaning and learning separate classifiers one can improve the TPR and F1 score when compared to other machine learning approaches. To the best of our knowledge, the researchers have not yet attempted the use of SAE and FNN for classifying bioassay data.