A Neuro-Fuzzy Framework for Finding Clinical Trials in the Drug Discovery Process

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dc.contributor.authorOladele, Tinuke Omolewa
dc.contributor.authorWilliams, F. E.
dc.contributor.authorSadiku, J. S.
dc.date.accessioned2023-05-11T14:04:28Z
dc.date.available2023-05-11T14:04:28Z
dc.date.issued2015
dc.description.abstractThe drug discovery process is complex, time consuming and very expensive. Typically, the time to develop a candidate drug is about 5 years, while the clinical phases leading, possibly, to the commercial availability of the drug are even longer (>7 years) for a total cost of more than 700 Million dollars. Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting Phase II and III trials. It is therefore crucial to ensure that these phases of drug development are conducted more efficiently and cost-effectively, and that attrition rates are reduced.In this paper, a neuro-fuzzy framework is presented for finding clinical trials in the drug discovery process. The proposed framework was developed based on the combination of two artificial intelligence techniques known as artificial neural networks and fuzzy logic.en_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/10127
dc.language.isoenen_US
dc.publisherComputing, Information Systems, Development Informatics & Allied Research Journal (CISDIAR)en_US
dc.subjectClinical Trial, Drug Discovery, Preclinical, Drug Target, Placeboen_US
dc.titleA Neuro-Fuzzy Framework for Finding Clinical Trials in the Drug Discovery Processen_US
dc.typeArticleen_US

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