## 2 Literature Review  
The gene-disease relationships have a great significance in diagnosis, treatment and prevention of diseases. Though these associations are deeply investigated by the researchers, much of their underpinnings are yet to be explained. Text mining was performed on documents from the PubMed database to predict the gene-disease relationships based on the cosine similarity between the gene vectors and disease vectors [13]. This method integrates the MeSH database, co-occurrence methods and term weight to predict gene-disease relationships. Chemical and drug information is accumulated in all sorts of text documents like industry reports, patents and scientific articles. This has led to the development of many text mining applications. The PubMed biomedical literature database is a valuable source of information. An R package was developed by combining the advantages of existing text mining algorithms, to analyze the PubMed abstracts [14]. A review on the tools, methods and applications of text mining for chemical compounds, was presented to determine their structures and identify relationships between chemicals and other entities [15].  
Latent Dirichlet Allocation is a probabilistic topic model that aims to give a topic perspective solution. Researchers have proposed various topic models based on LDA. An algorithm called Bio-LDA was introduced to identify latent topics using biological terms and uncover putative relations among bio-terms and topics [16]. A survey was presented to discover trends, research development and intellectual structures of topic modelling based on LDA [17]. To extract research topics from [PERSON]'s disease-related papers, the LDAP framework was proposed, which combined LDA with an affinity propagation model algorithm [18]. Various analyses, like research trends analysis, were performed on the results. Another model, named latent semantic analysis, was used to semantically interface PubMed abstracts to gene ontology [19]. The PubMed abstracts for this model were obtained based on the semantic similarity between the user query and the abstracts.  
Three keyword extraction models were proposed in which the first one was based on LSA and right singular matrix, and the other two models were based on Shannon entropy [20]. The proposed models are not length dominating, and they have a low redundancy. For the analysis of patent data, an intelligent system based on principle component analysis and logistics was proposed [21]. This system extracts the features from the patents database and classifies them into categories such as software, biological, business and chemical.  
The prediction of TB survivability has been a challenging problem for many years. A study was presented on various data mining approaches that have been utilized for Tuberculosis diagnosis and prognosis, and a best prediction model for TB survivability was developed accordingly [22]. A regulatory network was proposed to detect the action of genes required for _Mycobacterium tuberculosis_ (_M.tb_) persistence, using text mining models [23]. Its purpose was to suggest candidates for new drug targets and to provide fresh insights on the persistence mechanism of _M.tb_. A method to find close association rules within TB data, was proposed and applied on the dataset containing the real medical records of TB patients [24]. This method determined the association of one symptom to another. Different types of media content analysis are carried out for different application.  
Taking all these as a motivation, this paper proposes a model to deal with PubMed datasets in the identification of the various pathogens the TB drugs are active against, in addition to _M.tb_. The analyzed TB drugs can be effective against some other diseases, caused by the pathogens that these drugs work against.