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.
Highlights
Cancer is the second leading cause of death globally in 2018 and it is incredibly complicated [1].The characteristics used to distinguish cancer cells from normal cells are called the hallmarks of cancer (HoC) [2,3]
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
Chen et al [31] proposed an approach that employs an latent Dirichlet allocation (LDA) topic generative model to promoting ranking diversity for biomedical information retrieval. They showed that the proposed approach achieved an 8% improvement over the Aspect MAP reported in TREC 2007 Genomics track [32]
Summary
Cancer is the second leading cause of death globally in 2018 and it is incredibly complicated [1].The characteristics used to distinguish cancer cells from normal cells are called the hallmarks of cancer (HoC) [2,3]. The cancer hallmark is the fundamental principle of malignant transformation of a cancer cell and useful to understand tumor pathogenesis. It is becoming highly influential in cancer research [4,5,6]. There are common 10 hallmarks of normal cells required for malignant growth that have been proposed that provide an organizing principle to explain the variety of the biological processes leading to cancer [3]. These are sustaining proliferative signaling (SPS), evading growth suppressors
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