## 3 Materials and Methods
## 5 Discussion

The resultant sets are analyzed and an important inference is extracted about the drugs. It determines the pathogens that these drugs are active against, in addition to TB as given in Figure 4. The antibiotic drugs developed for a particular disease work against the pathogen causing that disease. It inhibits the growth of the microbial targets without harming the host. Each class of antibiotics has a unique mechanism of action. They can be the inhibitors of cell wall synthesis, inhibitors of protein synthesis, inhibitors of cell membrane function, inhibitors of nucleic acid synthesis or inhibitors of other metabolic processes [32]. It depends on the nature of their structure and their affinity to the target. A mechanism of action of a drug can work against many pathogens. Therefore, that drug can be used in the treatment of diseases caused by such pathogens. For example, moxifloxacin is also effective against _Helicobacter Pylori_, a bacterium which causes ulcer and might progress to stomach cancer [33; 34]. Thus in addition to TB, moxifloxacin can also be used in the treatment of these two diseases. A Gram-positive bacterium named Methicillin-Resistant _Staphylococcus aureus_ (MRSA) causes various infections such as skin infections, pneumonia and sepsis [35]. The drugs, moxifloxacin, amikacin and linezolid are effective against MRSA in addition to TB and thereby can be used in the treatment of the mentioned infections. Similarly the TB drugs can be effective against various other pathogens or infections. Thereby they can be used in the treatment of various diseases caused by such pathogens. This kind of TB drug-pathogen relationship is extracted through our framework. The results are compared with some important information sources such as doctors and medical research papers, and they

Figure 3: Resultant sets with co-occurrence value for: (**a**) amikacin; (**b**) ethambutol; (**c**) isoniazid; (**d**) linezolid; (**e**) moxifloxacin; (**f**) pyrazinamide; (**g**) rifampicin; (**h**) streptomycin. closely associate with the inferences obtained from these sources [36; 37; 38; 39]. This inference might help medical researchers in various drug analyses, in finding treatment for various other bacterial diseases, etc. Researchers who carry out in vitro testing can also benefit from this study by extracting this inference from about 1000 papers, which would have otherwise been extremely cumbersome under usual settings. Another result is also obtained through this framework is depicted in Figure 5. The resultant sets of the mentioned TB drugs in this figure, contained either antiretroviral or immunodeficiency virus in them. This inference states that some of the TB drugs are also used in antiretroviral therapy to treat Human Immunodeficiency Virus (HIV). This inference shows the inter-relationships between TB and HIV. People diagnosed with HIV have a high chance of getting infected with TB pathogens as HIV weakens the immune system [40; 41]. Therefore, the body cannot fight TB, thereby these pathogens can quickly progress into TB disease. TB has become the leading cause of death among the people affected by HIV. If a person is affected by TB, it is important for them to know about their HIV status. Similarly, various inferences can be extracted through this framework. It is evident that this way of data extraction has identified many different and important results. This tool when applied to other drugs, diseases or plants datasets can draw out various important inferences and thereby can help the medical researchers to speed up their background research work. This framework can be considered as a complementing tool for doctors and medical research experts in their drug discovery studies, drug interaction studies and bacteria analysis, and can pave a way in discovering treatment for some incurable diseases.