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Open Access

Peer-reviewed

Research Article

Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation

Kalluri Thishya,

Roles Data curation, Formal analysis, Investigation, Methodology

Affiliation Departments of Clinical Pharmacology and Therapeutics, Nizam's Institute of Medical Sciences Hyderabad, Telangana, India
⨯
Kiran Kumar Vattam,

Roles Formal analysis, Methodology, Validation

Affiliation Sandor Lifesciences Pvt Ltd, Hyderabad, Telangana, India
⨯
Shaik Mohammad Naushad,

Roles Conceptualization, Formal analysis, Writing – review & editing

Affiliation Sandor Lifesciences Pvt Ltd, Hyderabad, Telangana, India
⨯
Shree Bhushan Raju,

Roles Conceptualization, Supervision, Visualization

Affiliation Department of Nephrology, Nizam's Institute of Medical Sciences, Hyderabad, Telanagana, India
⨯
Vijay Kumar Kutala

Roles Conceptualization, Investigation, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: vijaykutala@gmail.com

Affiliation Departments of Clinical Pharmacology and Therapeutics, Nizam's Institute of Medical Sciences Hyderabad, Telangana, India

http://orcid.org/0000-0002-7253-8999

⨯

Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation

Kalluri Thishya,
Kiran Kumar Vattam,
Shaik Mohammad Naushad,
Shree Bhushan Raju,
Vijay Kumar Kutala

x

Published: April 5, 2018
https://doi.org/10.1371/journal.pone.0191921

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Dose prediction methods
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Artificial neural networks
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Renal transplantation
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Diabetes mellitus
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Variant genotypes
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Alleles
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Medical risk factors
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Transplant rejection
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