Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.

A Drug Combination Prediction Framework Based on Graph Convolutional Network and Heterogeneous Information.

Combination therapy, which can improve therapeutic efficacy and reduce side effects, plays an important role in the treatment of complex diseases. Yet, a large number of possible combinations among candidate compounds limits our ability to identify effective combinations. Though many studies have focused on predicting potential drug combinations, the existing methods are not entirely satisfactory in terms of performance and scalability. In this study, we propose a new computational pipeline, called DCMGCN, which integrates diverse drug-related information, to predict novel drug combinations. Specifically, DCMGCN first learns low-dimensional representations of drugs from the drug attributes and similarity networks. Then, by quantifying the degree of the nodes in the known drug-drug network and the similarity between connected nodes, we found the drug-drug network has heterophily and sparseness, which may limit the effectiveness of the graph convolutional network (GCN). Therefore, we introduce two designs to modify GCN. Finally, the drug representations are optimized using modified GCN (MGCN) and used to predict drug combinations. The tests on multiple drug combination datasets show that DCMGCN achieved substantial improvements over state-of-the-art methods. Importantly, our model may embed the mechanism of ground-truth drug pairs into the low-dimensional representation of each drug, which may help to further clarify the understanding of mechanisms of drug action.

TADC: A Topic-Aware Dynamic Convolutional Neural Network for Aspect Extraction.

Aspect extraction is one of the key tasks in fine-grained sentiment analysis. This task aims to identify explicit opinion targets from user-generated documents. Currently, the mainstream methods for aspect extraction are built on recurrent neural networks (RNNs), which are difficult to parallelize. To accelerate the training/testing process, convolutional neural network (CNN)-based methods are introduced. However, such models usually utilize the same set of filters to convolve all input documents, and hence, the unique information inherent in each document may not be fully captured. To alleviate this issue, we propose a CNN-based model that employs a set of dynamic filters. Specifically, the proposed model extracts the aspects in a document using the filters generated from the aspect information intrinsic in the document. With the dynamically generated filters, our model is capable of learning more important features concerning aspects, thus promoting the effectiveness of aspect extraction. Furthermore, considering that aspects can be grouped into certain topics that conversely indicate the target words that need to be extracted, we naturally introduce a neural topic model (NTM) and integrate latent topics into the CNN-based module to help identify aspects. Experiments on two benchmark datasets demonstrate that the joint model is able to effectively identify aspects and produce interpretable topics.

TACN: A Topical Adversarial Capsule Network for textual network embedding.

Combining topological information and attributed information of nodes in networks effectively is a valuable task in network embedding. Nevertheless, many prior network embedding methods regarded attributed information of nodes as simple attribute sets or ignored them totally. In some scenarios, the hidden information contained in vertex attributes are essential to network embedding. For instance, networks that contain vertexes with text information play an increasingly important role in our life, including citation networks, social networks, and entry networks. In these textual networks, the latent topic relevance information of different vertexes contained in textual attributes information are valuable in the network analysis process. Shared latent topics of nodes in networks may influence the interaction between them, which is critical to network embedding. However, much prior work for textual network embedding only regarded the text information as simple word sets while ignored the embedded topic information. In this paper, we develop a model named Topical Adversarial Capsule Network (TACN) for textual network embedding, which extracts a low-dimensional latent space of the original network from node structures, vertex attributes, and topic information contained in text of nodes. The proposed TACN contains three parts. The first part is an embedding model, which extracts the embedding representation from the topological structure, vertex attributes, and document-topic distributions. To ensure a consistent training process by back-propagation, we generate document-topic distributions by the neural topic model with Gaussian Softmax constructions. The second part is a prediction model, which is used to exploit labels of vertices. In the third part, an adversarial capsule model is used to help distinguish the latent representations from node structure domain, vertex attribute domain, or document-topic distribution domain. The latent representations, which may come from the three domains, are the output of the embedding model. We incorporate the adversarial idea into the adversarial capsule model to combine the information from these three domains, rather than to distinguish the representations conventionally. Experiments on seven real-world datasets validate the effectiveness of our method.

Fast Supervised Topic Models for Short Text Emotion Detection.

With the development of social network platforms, discussion forums, and question answering websites, a huge number of short messages that typically contain a few words for an individual document are posted by online users. In these short messages, emotions are frequently embedded for communicating opinions, expressing friendship, and promoting influence. It is quite valuable to detect emotions from short messages, but the corresponding task suffers from the sparsity of feature space. In this article, we first generate term groups co-occurring in the same context to enrich the number of features. Then, two basic supervised topic models are proposed to associate emotions with topics accurately. To reduce the time cost of parameter estimation, we further propose an accelerated algorithm for our basic models. Extensive evaluations using three short corpora validate the efficiency and effectiveness of the accelerated models for predicting the emotions of unlabeled documents, in addition to generate the topic-level emotion lexicons.

Affective topic model for social emotion detection.

The rapid development of social media services has been a great boon for the communication of emotions through blogs, microblogs/tweets, instant-messaging tools, news portals, and so forth. This paper is concerned with the detection of emotions evoked in a reader by social media. Compared to classical sentiment analysis conducted from the writer's perspective, analysis from the reader's perspective can be more meaningful when applied to social media. We propose an affective topic model with the intention to bridge the gap between social media materials and a reader's emotions by introducing an intermediate layer. The proposed model can be used to classify the social emotions of unlabeled documents and to generate a social emotion lexicon. Extensive evaluations using real-world data validate the effectiveness of the proposed model for both these applications.

Community-aware user profile enrichment in folksonomy.

In the era of big data, collaborative tagging (a.k.a. folksonomy) systems have proliferated as a consequence of the growth of Web 2.0 communities. Constructing user profiles from folksonomy systems is useful for many applications such as personalized search and recommender systems. The identification of latent user communities is one way to better understand and meet user needs. The behavior of users is highly influenced by the behavior of their neighbors or community members, and this can be utilized in constructing user profiles. However, conventional user profiling techniques often encounter data sparsity problems as data from a single user is insufficient to build a powerful profile. Hence, in this paper we propose a method of enriching user profiles based on latent user communities in folksonomy data. Specifically, the proposed approach contains four sub-processes: (i) tag-based user profiles are extracted from a folksonomy tripartite graph; (ii) a multi-faceted folksonomy graph is constructed by integrating tag and image affinity subgraphs with the folksonomy tripartite graph; (iii) random walk distance is used to unify various relationships and measure user similarities; (iv) a novel prototype-based clustering method based on user similarities is used to identify user communities, which are further used to enrich the extracted user profiles. To evaluate the proposed method, we conducted experiments using a public dataset, the results of which show that our approach outperforms previous ones in user profile enrichment.