Abstract
Tuberculosis (TB) is one of the top causes of death in the world. Though TB is known as the world’s most infectious killer, it can be treated with a combination of TB drugs. Some of these drugs can be active against other infective agents, in addition to TB. We propose a framework called TREASURE (Text mining algoRithm basEd on Affinity analysis and Set intersection to find the action of tUberculosis dRugs against other pathogEns), which particularly focuses on the extraction of various drug–pathogen relationships in eight different TB drugs, namely pyrazinamide, moxifloxacin, ethambutol, isoniazid, rifampicin, linezolid, streptomycin and amikacin. More than 1500 research papers from PubMed are collected for each drug. The data collected for this purpose are first preprocessed, and various relation records are generated for each drug using affinity analysis. These records are then filtered based on the maximum co-occurrence value and set intersection property to obtain the required inferences. The inferences produced by this framework can help the medical researchers in finding cures for other bacterial diseases. Additionally, the analysis presented in this model can be utilized by the medical experts in their disease and drug experiments.
Highlights
According to the World Health Organization (WHO) report, the number of TB cases went from 2.2 million to 2.8 million in the year 2015 [1]
In the year 2019, around 2.64 million TB cases were reported by the WHO in India, and estimates show that 40% of the Indian population was affected by TB bacteria
The proposed framework, TREASURE, finds various relations among the preprocessed data, with the help of affinity analysis, and it uses set intersection property to filter out these patterns among the relation sets based upon their occurring frequency
Summary
According to the World Health Organization (WHO) report, the number of TB cases went from 2.2 million to 2.8 million in the year 2015 [1]. There are no reports of any inferences drawn on the activity of drugs against a spectrum of pathogens, in addition to the pathogen responsible for an infectious disease This motivated the need to design a technology-based solution by using TB drugs-based PubMed abstracts as the source dataset. The proposed framework, TREASURE, finds various relations among the preprocessed data, with the help of affinity analysis, and it uses set intersection property to filter out these patterns among the relation sets based upon their occurring frequency This framework is applied on various TB drugs datasets to determine the various other infections these drugs are effective against in addition to TB. This method might help various researchers in the field of drug discovery and drug interaction studies, doctors and the pharmaceutical companies
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