Bayesian variable selection for high-dimensional data with an ordinal response: identifying genes associated with prognostic risk group in acute myeloid leukemia

Bayesian variable selection for high-dimensional data with an ordinal response: identifying genes associated with prognostic risk group in acute myeloid leukemia

Background: Acute myeloid leukemia (AML) is a heterogeneous cancer of the blood, though specific recurring cytogenetic abnormalities in AML are strongly associated with attaining complete response after induction chemotherapy, remission duration, and survival. Therefore recurring cytogenetic abnorma...

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Bibliographic Details
Main Authors: Archer, K.J (Author), Zhang, Y. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
acute myeloid leukemia
Acute myeloid leukemia
Bayes factor
Bayes theorem
Bayes Theorem
Bayesian
Chemotherapy
Clustering algorithms
Diseases
Double exponential
European leukemianet
European LeukemiaNet
Gene expression
genetics
human
Humans
LASSO
Leukemia, Myeloid, Acute
Penalized model
Penalized models
prognosis
Prognosis
Response model
risk factor
Risk Factors
Risk groups
Spike-and-slab
treatment outcome
Treatment Outcome
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