# Multi-Task Topic Analysis Framework for Hallmarks of Cancer with Weak Supervision
## 2 Related Work
### Cancer Hallmarks Classification  
Machine learning (ML) and DL techniques have been explosively applied to research in the fields of cancer and NLP. However, few research efforts have addressed cancer hallmark classification with ML and DL.  
[PERSON] et al. [18] acquired a collection of PubMed abstracts using a set of search terms representative for each of the 10 hallmarks. They used text mining methodologies such as tokenization, part-of-speech tagging, lemmatization, dependency parsing, and named entity recognition for identifying relevant information in the literature and extracted seven feature types such as a lemmatized bag of words, noun bigrams, grammatical relations, verb classes, named entities, medical subject headings, and chemical lists. They developed 10 independent binary classifiers to predict whether an abstract belongs to each hallmark category and achieved an average F1-score of 77%. [PERSON] et al. [19] presented a joint learning method that learns distributed semantic representations at different types of granularity such as words, sentences, documents, and class using the Support Vector Machine (SVM) algorithm. They also showed a semi-supervised classification result using the same classifier. Their model achieved an F1-score of 76.4% on a corpus of 1580 PubMed abstracts. [PERSON] et al. [20] presented a CNN model to achieve better performance with conventional ML algorithms with manually engineered features. They compared the result with the SVM classifier, outperformed the performance in the seven cancer hallmark tasks. [PERSON] et al. [21] applied a hierarchical model for the multi-label classification task. They initiated the final hidden layer of a neural network that leverages label co-occurrence relations such as hypernymy. They achieved the F1-score of 75.3% on a document level and the accuracy of 89% on the sentence level. [PERSON] et al. [22] developed an automatic text mining methodology and tool to retrieve and organize millions of PubMed literature into the 37 cancer hallmarks by adding subclasses. They used the SVM classifier to predict a biomedical document and trained individual binary classifiers. Their models achieved the average F1-score of 52% and an accuracy of 97.9% on the sentence level classification for all cancer hallmarks.  
[PERSON] et al. [23] proposed a DL framework for multi-label classification of biomedical text without any manual feature engineering. They trained a single model for a large set of labels. The proposed model alleviates human effort for feature engineering and avoids training an individual model for each class label. Their proposed model achieved the F1-score of 81.5% on a corpus of 1580 abstracts. [PERSON] et al. [24] presented a literature-based discovery system with a particular focus on the molecular biology of cancer using NLP methods and ML. They trained a CNN model on 37 cancer hallmarks including the basic 10 hallmarks. The model supports the system to classify sentences to hallmark categories. [PERSON] et al. [25] evaluated and analyzed biomedical NLP representation models on five tasks with 10 datasets including cancer hallmark classification. They found that the BERT models pre-trained on PubMed abstracts achieved better performance than other models. Their best model achieved the F1-score of 87.3% on a corpus of 1580 PubMed abstracts.  
Most of the previous studies used the small dataset, which contains around 1500 documents. In this paper, we leveraged a large number of unlabeled documents related to lung cancer from PubMed.  
We analyzed topics on the unlabeled documents for each cancer hallmark category by employing text mining, deep learning, and topic models.