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1. Genome-wide association study (GWAS) for identification and characterization of causal genes and novel biomarkers for coronary heart disease.
Performing GWAS for several cardiovascular disease phenotypes, this is in collaboration with Erik Ingelsson research group.
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2. Network enrichment analysis.
Although high-throughput genomic experiments may have different objectives, e.g., class comparison, class prediction and class discovery, the results of these analyses are usually similar, i.e., lists of interesting genes. After discovering these genes, the next challenge is to interpret them biologically.
Alexeyenko et al. (2012) proposed a network enrichment analysis (NEA) method, which systematically implements the network approach to describe novel gene sets with biologically meaningful functional categories
NeaGUI is a user friendly package developed to implement a method of network enrichment analysis (NEA) that extends the overlap statistics in GEA to network links between genes in the experimental set and those in the functional categories. NeaGUI can be downloaded freely from Bioconductor: http://www.bioconductor.org/packages/2.13/bioc/html/neaGUI.html
3. Statistical Methods in Microarray and Next Generation Sequencing Analysis (NGS) Data Analysis.
Statistical methods for high-throughput data analyses particularly in DNA, RNA sequencing, Protein array and RNA microarray expression studies in cancer (breast cancer and prostate cancer), malaria, and cardiovascular diseases.
– Microarray data analysis. Statistical analysis, e.g., Classification and Survival analysis, related to sub type discovery in prostate cancer, malaria, etc.
– NGS filtering procedure. NGS data are known to produce errors due to many factors, e.g., base-calling and alignment errors. These sequencing errors lead to numerous false positives or in case of variant detection, it can cause to many false discovered variants. Several filtering procedures are proposed to improve true variants detection.
– RNA-seq. Classification and clustering of cancer subtypes.
– Exome-seq in Breast cancer, discovering driver genes.
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