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BAM: A Block-Based Bayesian Method for Detecting Genome-Wide Associations with Multiple Diseases
Article proposes a novel Bayesian method, named BAM, for simultaneously partitioning Single Nucleotide Polymorphisms (SNPs) into Linkage Disequilibrium(LD)-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases. Experimental results on the simulated datasets demonstrate that BAM is powerful and efficient.
Date:
April 16, 2020
Creator:
Guo, Xuan; Wu, Guanying & Xu, Baohua
System:
The UNT Digital Library
Mining Potential Effects of HUMIRA in Twitter Posts Through Relational Similarity
Article investigating HUMIRA effects mentioned in Twitter posts using a relational similarity-based method. The authors were able to identify effects previously known as well as potentially unreported, which demonstrates the power of this method and its potential for studying effects of other medications shared by Twitter users.
Date:
June 16, 2020
Creator:
Feng, Shichao; Jiang, Keyuan; Huang, Liyuan; Chen, Tingyu & Bernard, Gordon R.
System:
The UNT Digital Library