Tuning Multi-Objective Evolutionary Algorithms on Different Sized Problem Sets

Masoudi‐Nejad

2020

BMC Bioinformatics

Background: Drug repurposing aims to detect the new therapeutic benefits of the existing drugs and reduce the spent time and cost of the drug development projects. The synthetic repurposing of drugs may prove to be more useful than the single repurposing in terms of reducing toxicity and enhancing efficacy. However, the researchers have not given it serious consideration. To address the issue, a novel datamining method is introduced and applied to repositioning of drugs for hypertension (HT) which is a serious medical condition and needs some improved treatment plans to help treat it. Results: A novel two-step data mining method, which is based on the If-Then association rules as well as a novel discrete optimization algorithm, was introduced and applied to the synthetic repurposing of drugs for HT. The required data were also extracted from DrugBank, KEGG, and DrugR+ databases. The findings indicated that based on the different statistical criteria, the proposed method outperformed the other state-of-the-art approaches. In contrast to the previously proposed methods which had failed to discover a list on some datasets, our method could find a combination list for all of them. Conclusion: Since the proposed synthetic method uses medications in small dosages, it might revive some failed drug development projects and put forward a suitable plan for treating different diseases such as COVID-19 and HT. It is also worth noting that applying efficient computational methods helps to produce better results.

Section: Resultsmentioning

confidence: 99%

Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm

Masoudi‐Nejad

2020

BMC Bioinformatics

“…Therefore, they must be executed at least 30 times, and their performances should be evaluated based on the produced data [44]. An algorithm, which yields very similar results in disparate runs, is more stable than the others and consequently, its generated outcomes should be better than the other ones.…”

Section: -Resultsmentioning

confidence: 99%

Synthetic Repurposing of Drugs in Hypertension: a Datamining Method Based on Association Rules and a Novel Discrete Algorithm

Masoudi‐Nejad²

2020

Preprint

Background Drug repurposing aims to detect the new benefits of the existing drugs and to reduce the spent time and cost of the drug development projects. Although synthetic repurposing of drugs may be more useful than single repurposing in terms of reducing toxicity and enhancing efficacy, the researchers have not taken it into account. To address the issue, a novel datamining method is introduced and applied to the repositioning of drugs in hypertension (HT), which is a serious medical condition and therefore needs to be dealt with effectively through making some improved treatment plans to help cure it. Results a novel two-step data mining method, which is based on the If-Then association rules and a novel discrete optimization algorithm, is proposed and applied to the synthetic repurposing of drugs in HT. The required data are extracted from DruhBank, KEGG, and DrugR+ databases. The outcomes presented that the proposed method outperforms the other state-of-the-art approaches in terms of different statistical criteria. In contrast to the previously proposed methods which failed to discover a list for some datasets, our method managed to suggest a combination of drugs for all the datasets. Conclusion The proposed synthetic method may revive some failed drug development projects and be a suitable plan for curing orphan and rare diseases due to using a low dosage of medicines. It is also essential to use some efficient computational methods to produce better results.

“…Therefore, they must be executed at least 30 times, and their performances are evaluated based on the produced data (47). An algorithm, whose results in disparate runs are close together, is more stable than others.…”

Section: -Resultsmentioning

confidence: 99%

Synthetic Repurposing of Drugs in Hypertension: a Datamining Method Based on Association Rules and a Novel Discrete Algorithm

Masoudi‐Nejad²

2020

Preprint

Background: Drug repurposing aims to detect new benefits of the existing drugs and to reduce the time and cost of drug development projects. Although synthetic repurposing of drugs may be more useful than single repurposing in terms of reducing toxicity and enhancing efficacy, the researchers have not taken it into account. To address the issue, a novel datamining method is introduced and applied to the repositioning of drugs in hypertension (HT). This disease is a complex one and needs to efficient treatment plans to cure it better.Methods: A novel two-step data mining method, which is based on the If-Then association rules and a novel discrete optimization algorithm, is proposed and applied to the synthetic repurposing of drugs in HT. The required data are extracted from DruhBank, KEGG, and DrugR+ databases. Results: The outcomes presented that the proposed method outperforms other state-of-the-art approaches in terms of different statistical criteria. Since different methods failed to discover the list for some datasets, our method could suggest a combination of drugs for all the datasets. Conclusion: Due to using a minimum dosage of medicines, the synthetic method may revive some failed drug development projects and maybe a suitable plan for curing orphan and rare diseases. Also, to achieve better outcomes, it is essential to use efficient computational methods.