## 3 Materials and Methods
In order to predict the accuracy of the proposed framework, it is compared with the existing topic modelling techniques such as latent Dirichlet allocation (LDA), latent Dirichlet allocation with affinity propagation (LDA with AP) and latent semantic analysis (LSA). The performance of our framework is evaluated using the following standard methodologies [42; 43]. Figure 4: Action of TB drugs against other pathogens or infections. _Precision_ is the measure of how much information returned by the system is correct. It is given in Equation (7). It is the ratio of the number of correctly predicted observations to the total predicted observations in the resultant set. \[Precision\%\ =\ \frac{number\ of\ correct\ answers\ given\ by\ the\ system}{number\ of\ answers\ given\ by\ the\ system}\times 100 \tag{7}\]

Recall, as given in Equation (8), the measure of relevant information extracted by the system. It is the ratio of correctly predicted observations to all the observations in the class. \[\text{Recall}\%\ =\ \frac{number\ of\ correct\ answers\ given\ by\ the\ system}{total\ number\ of\ possible\ correct\ answers\ in\ text}\times 100 \tag{8}\]

F-Measure is the harmonic mean of precision and recall and is computed as given in Equation (9). It balances both the precision and recall with a single score. \[\text{F}-\text{Measure}\%\ =\ \frac{2\ \times\ Precision\ \times\ Recall}{Precision\ +\ Recall}\ \times\ 100 \tag{9}\]

The above mentioned standard methodologies are calculated for various algorithms. The graphical comparisons of these measures for four different drug datasets, mooxifloxacin, linezolid, streptomycin and rifampicin are depicted in Figure 6a-c. From Figure 6c, we can observe that the TREASURE model gives high accuracy (71.85%) when compared to LDA, LDA with AP and LSA algorithms. The benchmark words for comparing the results of each algorithm were collected from renowned information sources such as WHO, Healthline, MEDLINE and NIH. Hence, it is evident that this way of data extraction has provided appropriate results in the identification of various TB drug-pathogen relationships. Figure 5: A Venn diagram summarization to depict the inter-relationships between the TB and HIV treatments. ## 6 Conclusions

Tuberculosis is a potentially serious infectious disease caused by _Mycobacterium tuberculosis_ that usually attacks the lungs. This paper is a novel attempt to propose a framework named TREASURE to analyze various TB drugs from PubMed literature and identify the action of these drugs against other pathogens. Lack of effective analysis tools to discover different drug-pathogen relationships necessitated the proposal of TREASURE model. We analyzed eight different TB drugs namely pyrazinamide, moxifloxacin, ethambutol, isoniazid, rifampicin, linezolid, streptomycin and amikacin, and found out various other pathogens or infections that these drugs are effective against, in addition to TB. We generated relation records from the datasets using affinity analysis and filtered these records using maximum co-occurrence value and set intersection property to obtain the results. This method provides inferences based on various drug-pathogen relationships which can help the medical experts to speed up their background research work and thereby saves time and manpower. In future, it can also be used to find remedies for some incurable diseases. In this application, we use only the text datasets. As a future research, it is intended to combine this model with image processing and analyze its performance. Conceptualization, P.S. and N.S.S. ; methodology, P.S. and N.S.S. ; validation, V.S. and Y.L. ; formal analysis, V.S. ; investigation, Y.L. ; resources, Y.L. ; data curation, P.S. and N.S.S. ; writing, P.S. and N.S.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.