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
