# Multi-Task Topic Analysis Framework for Hallmarks of Cancer with Weak Supervision
###### Abstract  
The hallmarks of cancer represent an essential concept for discovering novel knowledge about cancer and for extracting the complexity of cancer. Due to the lack of topic analysis frameworks optimized specifically for cancer data, the studies on topic modeling in cancer research still have a strong challenge. Recently, deep learning (DL) based approaches were successfully employed to learn semantic and contextual information from scientific documents using word embeddings according to the hallmarks of cancer (HoC). However, those are only applicable to labeled data. There is a comparatively small number of documents that are labeled by experts. In the real world, there is a massive number of unlabeled documents that are available online. In this paper, we present a multi-task topic analysis (MTTA) framework to analyze cancer hallmark-specific topics from documents. The MTTA framework consists of three main subtasks: (1) cancer hallmark learning (CHL)--used to learn cancer hallmarks on existing labeled documents; (2) weak label propagation (WLP)--used to classify a large number of unlabeled documents with the pre-trained model in the CHL task; and (3) topic modeling (ToM)--used to discover topics for each hallmark category. In the CHL task, we employed a convolutional neural network (CNN) with pre-trained word embedding that represents semantic meanings obtained from an unlabeled large corpus. In the ToM task, we employed a latent topic model such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) model to catch the semantic information learned by the CNN model for topic analysis. To evaluate the MTTA framework, we collected a large number of documents related to lung cancer in a case study. We also conducted a comprehensive performance evaluation for the MTTA framework, comparing it with several approaches. multi-task learning; topic analysis; semantic learning; convolutional neural network; latent semantic learning; biomedical domain; cancer hallmark; lung cancer +
Footnote †: journal: Medical  
(EGSs), resisting cell death (RCD), enabling replicative immortality (ERI), inducing angiogenesis (IA), activating invasion and metastasis (AIM), genome instability and mutation (GIM), tumor-promoting inflammation (TPI), deregulating cellular energetics (DCE), and avoiding immune destruction (AID). Most of the cancer research results have been published in the biomedical literature. The famous biomedical literature database is PubMed, which has indexed approximately 30 million citations including around 4 million cancer-related literature by 2019. As biomedical literature on servers grows exponentially, biomedical text mining has been intensively investigated to find information in a more accurate and efficient manner [7; 8]. A large amount of biomedical literature in that database provides a great opportunity to extract useful knowledge for cancer research. Researchers use a keyword-based query to collect relevant literature from that database. Due to the complexity of cancer, a large number of keywords, their synonyms, and combinations are required, which is a very time-consuming task to retrieve relevant literature by using only a keyword. Most of the previous studies have used the comparatively small dataset annotated by the experts for the hallmarks of cancer. The dataset is small and the number of labeled documents, in particular, is very limited. To address this issue, we leverage a large number of documents related to lung cancer from PubMed in a semi-supervised manner. Recently, deep learning (DL) based approaches have achieved state-of-the-art performance and are increasingly applied to most natural language processing (NLP) tasks [9; 10; 11]. NLP has benefited greatly from the convolutional neural network (CNN) [12] and recurrent neural network (RNN) [13] due to their high performance without any feature engineering. Generally, CNNs are hierarchical and RNNs are sequential architectures.