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Systems with both quantitative and qualitative responses are widely encountered in many applications. Design of experiment methods are needed when experiments are conducted to study such systems. Classic experimental design methods are unsuitable here because they often focus on one type of response. In this paper, we develop a Bayesian D-optimal design method for experiments with one continuous and one binary response. Both noninformative and conjugate informative prior distributions on the unknown parameters are considered. The proposed design criterion has meaningful interpretations regarding the D-optimality for the models for both types of responses. An efficient point-exchange search algorithm is developed to construct the local D-optimal designs for given parameter values. Global D-optimal designs are obtained by accumulating the frequencies of the design points in local D-optimal designs, where the parameters are sampled from the prior distributions. The performances of the proposed methods are evaluated through two examples.
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This content will become publicly available on March 26, 2026
MINE: a new way to design genetics experiments for discovery
The Maximally Informative Next Experiment or MINE is a new experimental design approach for experiments, such as those in omics, in which the number of effects or parameters p greatly exceeds the number of samples n (p > n). Classical experimental design presumes n > p for inference about parameters and its application to p > n can lead to over-fitting. To overcome p > n, MINE is an ensemble method, which makes predictions about future experiments from an existing ensemble of models consistent with available data in order to select the most informative next experiment. Its advantages are in exploration of the data for new relationships with n < p and being able to integrate smaller and more tractable experiments to replace adaptively one large classic experiment as discoveries are made. Thus, using MINE is model-guided and adaptive over time in a large omics study. Here, MINE is illustrated on two distinct multi-year experiments, one involving genetic networks in Neurospora crassa and a second one involving a Genome Wide Association Study or GWAS in Sorghum bicolor as a comparison to classic experimental design in an agricultural setting.
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- Award ID(s):
- 2041546
- PAR ID:
- 10579041
- Editor(s):
- Ma, S
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Briefings in bioinformatics
- ISSN:
- 1467-5463
- Subject(s) / Keyword(s):
- ensemble methods MINE mixed linear models genetic networks biological clock
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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