Data Analysis


 Research interests and main challenges include:


- Omics data storage, preprocessing and analysis
- Biological databases are utilized for presenting several data analysis pipelines in order to analyze big omics data
- Parallel, incremental, and multi-view machine learning methods can be scaled to handle big data using the distributed and parallel computing technologies
- Big data architectures and tools for fast analysis of massive DNA, RNA, and protein sequence data, and fast querying on incremental and heterogeneous disease networks
- String algorithms, such as data compression and compressed matching
- Variations in genes analysis (e.g., SNPs)
- Algorithm engineering, such as efficient implementations development
- Sequence analysis, such as DNA Compression Algorithms, motif extraction
- Databases data mining
- Next Generation Sequencing (NGS) data analysis