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James Cussens

Showing results (1-10 of 5) with videos related to

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Theoretical Population Biology|August 10, 2014
Improved maximum likelihood reconstruction of complex multi-generational pedigreesNuala A Sheehan, Mark Bartlett, James Cussens
Risk Analysis : an Official Publication of the Society for Risk Analysis|February 17, 2025
Integrated testing strategies for cost-sensitive time-efficient hazard classification of new chemicals: The case of skin sensitizationMarko Raseta, Jon Pitchford, James Cussens, et al.
Mathematical Biosciences|May 28, 2017
Integrated testing strategies can be optimal for chemical risk classificationMarko Raseta, Jon Pitchford, James Cussens, et al.
Genetic Epidemiology|October 5, 2012
Maximum likelihood pedigree reconstruction using integer linear programmingJames Cussens, Mark Bartlett, Elinor M Jones, et al.
Journal of Affective Disorders|February 24, 2019
Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcareChristopher M Hatton, Lewis W Paton, Dean McMillan, et al.
Pageof 1

Showing results (1-10 of 5) with videos related to

Sort By:
Pageof 1
Theoretical Population Biology|August 10, 2014
Improved maximum likelihood reconstruction of complex multi-generational pedigreesNuala A Sheehan, Mark Bartlett, James Cussens
Risk Analysis : an Official Publication of the Society for Risk Analysis|February 17, 2025
Integrated testing strategies for cost-sensitive time-efficient hazard classification of new chemicals: The case of skin sensitizationMarko Raseta, Jon Pitchford, James Cussens, et al.
Mathematical Biosciences|May 28, 2017
Integrated testing strategies can be optimal for chemical risk classificationMarko Raseta, Jon Pitchford, James Cussens, et al.
Genetic Epidemiology|October 5, 2012
Maximum likelihood pedigree reconstruction using integer linear programmingJames Cussens, Mark Bartlett, Elinor M Jones, et al.
Journal of Affective Disorders|February 24, 2019
Predicting persistent depressive symptoms in older adults: A machine learning approach to personalised mental healthcareChristopher M Hatton, Lewis W Paton, Dean McMillan, et al.
Pageof 1