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Backgrounds: Traditional Chinese medicine and Western medicine combination (TCM-WMC) increased the complexity of compounds ingested. Objective: To develop a method for screening hepatotoxic compounds in TCM-WMC based on chemical structures using artificial intelligence (AI) methods. Methods: Drug-induced liver injury (DILI) data was collected from the public databases and published literatures. The total dataset formed by DILI data was randomly divided into training set and test set at a ratio of 3:1 approximately. Machine learning models of SGD (Stochastic Gradient Descent), kNN (k-Nearest Neighbor), SVM (Support Vector Machine), NB (Naive Bayes), DT (Decision Tree), RF (Random Forest), ANN (Artificial Neural Network), AdaBoost, LR (Logistic Regression) and one deep learning model (deep belief network, DBN) were adopted to construct models for screening hepatotoxic compounds. Result: Dataset of 2035 hepatotoxic compounds was collected in this research, in which 1505 compounds were as training set and 530 compounds were as test set. Results showed that RF obtained 0.838 of classification accuracy (CA), 0.827 of F1-score, 0.832 of Precision, 0.838 of Recall, 0.814 of area under the curve (AUC) on the training set and 0.767 of CA, 0.731 of F1, 0.739 of Precision, 0.767 of Recall, 0.739 of AUC on the test set, which was better than other eight machine learning methods. The DBN obtained 82.2% accuracy on the test set, which was higher than any other machine learning models on the test set. Conclusion: The DILI AI models were expected to effectively screen hepatotoxic compounds in TCM-WMC. [ABSTRACT FROM AUTHOR]

Visual Question Answering(VQA), an important task to evaluate the cross-modal understanding capability of an Artificial Intelligence model, has been a hot research topic in both computer vision and natural language processing communities. Recently, graph-based models have received growing interest in VQA, for its potential of modeling the relationships between objects as well as its formidable interpretability. Nonetheless, those solutions mainly define the similarity between objects as their semantical relationships, while largely ignoring the critical point that the difference between objects can provide more information for establishing the relationship between nodes in the graph. To achieve this, we propose an object-difference based graph learner, which learns question-adaptive semantic relations by calculating inter-object difference under the guidance of questions. With the learned relationships, the input image can be represented as an object graph encoded with structural dependencies between objects. In addition, existing graph-based models leverage the pre-extracted object boxes by the object detection model as node features for convenience, but they are suffering from the redundancy problem. To reduce the redundant objects, we introduce a soft-attention mechanism to magnify the question-related objects. Moreover, we incorporate our object-difference based graph learner into the soft-attention based Graph Convolutional Networks to capture question-specific objects and their interactions for answer prediction. Our experimental results on the VQA 2.0 dataset demonstrate that our model gives significantly better performance than baseline methods. [ABSTRACT FROM AUTHOR]

Abstract Distant supervised relation extraction is an important task in the field of natural language processing. There are two main shortcomings for most state-of-the-art methods. One is that they take all sentences of an entity pair as input, which would result in a large computational cost. But in fact, few of most relevant sentences are enough to recognize the relation of an entity pair. To tackle these problems, we propose a novel hierarchical selective attention network for relation extraction under distant supervision. Our model first selects most relevant sentences by taking coarse sentence-level attention on all sentences of an entity pair and then employs word-level attention to construct sentence representations and fine sentence-level attention to aggregate these sentence representations. Experimental results on a widely used dataset demonstrate that our method performs significantly better than most of existing methods. [ABSTRACT FROM AUTHOR]