# Multi-Task Topic Analysis Framework for Hallmarks of Cancer with Weak Supervision
###### Abstract
The distributed representations, known as word embedding [14], are often used as the first layer in DL models. Distributed representations are mainly learned through context and the learned word vectors can capture general syntactic and semantic information. Those word vectors have proven to be efficient in capturing context and semantic similarity, analogies, and due to its smaller dimensionality are fast and efficient in text mining tasks [15]. Typically, word embeddings are pre-trained by optimizing an auxiliary objective in a large unlabeled corpus and used for other downstream tasks. A topic model is a probabilistic model, which is used to find the statistics of topics from a large amount of corpus. Traditional topic models such as latent Dirichlet allocation (LDA) [16] and probabilistic latent semantic analysis (PLSA) [17] have been successfully employed in various text corpora. It maps a high dimensional frequency space to a lower-dimensional topic space. Moreover, the topic model can capture semantic information, which can reveal the latent relations among documents. It also can effectively solve the polysemy, synonym, and other problems, which has a vital significance in document feature extraction and content analysis. However, using a topic model to analyze the HoC has not been reported. Therefore, the purpose of this paper is to focus on the topic analysis for the HoC, design an efficient classification model, capture semantic information from a large amount of documents for extracting complexity of cancer, present a scalable framework to analyze trend and topics for the HoC, and improve the cancer hallmark classification performance and topic modeling result for each hallmark category. The main contributions are as follows. 1. We present a multi-task topic analysis (MTTA) framework to analyze cancer hallmark-specific topics in a multi-task manner by leveraging large amounts of unlabeled documents. 2. We leverage a large number of unlabeled documents related to lung cancer according to the HoC. Experiments on the unlabeled documents have demonstrated that the MTTA framework is potentially valuable for the HoC analysis with an impressive superiority. 3. We highlight the importance of the latent topic models on weak-labeled documents that are produced by the CNN model. The experimental results show that the CNN model can classify cancer hallmarks better than other approaches and conventional topic models can discover topics efficiently. 4. We produce semantic representations for each hallmark category using the CNN model and capture this semantic information for each hallmark category using conventional topic models. The rest of this paper is organized as follows. Section 2 summarizes related works on cancer hallmarks, topic modeling, and semantic learning. Section 3 presents the MTTA framework and its sub-tasks: cancer hallmark learning (CHL), weak label propagation (WLP), and topic modeling (ToM) in detail. Section 4 outlines the experimental setup and Section 5 shows experimental results of cancer hallmarks classification on manually labeled data and analysis of topic modeling on weak-labeled data related to lung cancer and discusses the effectiveness of the MTTA framework. Finally, we make a conclusion to this paper and discuss the future work in Section 6.