The spatial distribution of proteome at subcellular levels provides clues for protein functions, thus is important to human biology and medicine. Imaging-based methods are one of the most important approaches for predicting protein subcellular location. Although deep neural networks have shown impressive performance in a number of imaging tasks, its application to protein subcellular localization has not been sufficiently explored. In this study, we developed a deep imaging-based approach to localize the proteins at subcellular levels. Based on deep image features extracted from convolutional neural networks (CNNs), both single-label and multi-label locations can be accurately predicted. Particularly, the multi-label prediction is quite a challenging task. Here we developed a criterion learning strategy to exploit the label-attribute relevancy and label-label relevancy. A criterion that was used to determine the final label set was automatically obtained during the learning procedure. We concluded an optimal CNN architecture that could give the best results. Besides, experiments show that compared with the hand-crafted features, the deep features present more accurate prediction with less features. The implementation for the proposed method is available at https://github.com/RanSuLab/ProteinSubcellularLocation.

MOTIVATION: Non-small-cell lung carcinoma (NSCLC) mainly consists of two subtypes: lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). It has been reported that the genetic and epigenetic profiles vary strikingly between LUAD and LUSC in the process of tumorigenesis and development. Efficient and precise treatment can be made if subtypes can be identified correctly. Identification of discriminative expression signatures has been explored recently to aid the classification of NSCLC subtypes. RESULTS: In this study, we designed a classification model integrating both mRNA and long non-coding RNA (lncRNA) expression data to effectively classify the subtypes of NSCLC. A gene selection algorithm, named WGRFE, was proposed to identify the most discriminative gene signatures within the recursive feature elimination (RFE) framework. GeneRank scores considering both expression level and correlation, together with the importance generated by classifiers were all taken into account to improve the selection performance. Moreover, a module-based initial filtering of the genes was performed to reduce the computation cost of RFE. We validated the proposed algorithm on The Cancer Genome Atlas (TCGA) dataset. The results demonstrate that the developed approach identified a small number of expression signatures for accurate subtype classification and particularly, we here for the first time show the potential role of LncRNA in building computational NSCLC subtype classification models. AVAILABILITY AND IMPLEMENTATION: The R implementation for the proposed approach is available at https://github.com/RanSuLab/NSCLC-subtype-classification.

Drug-induced hepatotoxicity may cause acute and chronic liver disease, leading to great concern for patient safety. It is also one of the main reasons for drug withdrawal from the market. Toxicogenomics data has been widely used in hepatotoxicity prediction. In our study, we proposed a multi-dose computational model to predict the drug-induced hepatotoxicity based on gene expression and toxicity data. The dose/concentration information after drug treatment is fully utilized in our study based on the dose-response curve, thus a more informative representative of the dose-response relationship is considered. We also proposed a new feature selection method, named MEMO, which is also one important aspect of our multi-dose model in our study, to deal with the high-dimensional toxicogenomics data. We validated the proposed model using the TG-GATEs, which is a large database recording toxicogenomics data from multiple views. The experimental results show that the drug-induced hepatotoxicity can be predicted with high accuracy and efficiency using the proposed predictive model.