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IEEE Transactions on Bio-Medical Engineering|August 16, 2000
Neuro-computing versus linear statistical techniques applied to liver transplant monitoring: a comparative studyD G Melvin, M Niranjan, R W Prager, et al.
Indian Journal of Nephrology|May 12, 2025
A Rare Case of Tuberculosis Masquerading as Collapsing GlomerulopathyVishnu Keerthana Kotha, Vijay Chander Bukka, M Niranjan, et al.
Liver Transplantation : Official Publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society|July 10, 2001
Clinical validation of an artificial neural network trained to identify acute allograft rejection in liver transplant recipientsV F Hughes, D G Melvin, M Niranjan, et al.
Nepal Medical College Journal : NMCJ|December 9, 2009
Quality assurance in the management of peritonitis: a prospective studyC S Agrawal, M Niranjan, S Adhikary, et al.
British Journal of Anaesthesia|July 28, 2004
Estimation of errors in determining intrathoracic blood volume using thermal dilution in pigs with acute lung injury and haemorrhageM Nirmalan, M Niranjan, T Willard, et al.
Scientific Reports|October 7, 2025
Deep intelligence: a four-stage deep network for accurate brain tumor segmentationNirmala Paramanandham, Kishore Rajendiran, L K Pavithra, et al.
Plos One|March 3, 2021
Analysis of (co) variance components and estimation of breeding value of growth and production traits in Dahlem Red chicken using pedigree relationship in an animal modelU Rajkumar, L Leslie Leo Prince, K S Rajaravindra, et al.
Animal Biotechnology|April 19, 2011
Identification of haplotypes in promoter of prolactin gene and their effect on egg production and quality traits in layer chickenT K Bhattacharya, R N Chatterjee, R P Sharma, et al.
Biochemical Genetics|January 22, 2011
Polymorphism in the prolactin promoter and its association with growth traits in chickensT K Bhattacharya, R N Chatterjee, R P Sharma, et al.
International Journal of Medical Informatics|November 28, 1997
Design, construction and evaluation of systems to predict risk in obstetricsD R Lovell, B Rosario, M Niranjan, et al.
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