2018/07/12 by Ariana Broumand, Broumand, Ariana, Siamak Zamani Dadaneh +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #FOS: Electrical engineering #Genomics and Phylogenetic Studies #Machine Learning and Algorithms #Methodology (stat.ME) #Signal Processing (eess.SP) #eess.SP #electronic engineering #information engineering #stat.ME
paper · pdf · doi:10.48550/arxiv.1807.05920
6 pages, 4 figures, accepted in Asilomar Conference on Signals, Systems, and Computers 2018
arxiv created 2018/07/12 · openalex publication_date 2018/07/12 · arxiv updated 2018/07/17 · openalex created_date 2022/08/04 · openalex updated_date 2026/07/28
High throughput technologies have become the practice of choice for comparative studies in biomedical applications. Limited number of sample points due to sequencing cost or access to organisms of interest necessitates the development of efficient sample collections to maximize the power of downstream statistical analyses. We propose a method for sequentially choosing training samples under the Optimal Bayesian Classification framework. Specifically designed for RNA sequencing count data, the proposed method takes advantage of efficient Gibbs sampling procedure with closed-form updates. Our results shows enhanced classification accuracy, when compared to random sampling.