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Integrating heterogeneous biological databases for unveiling the new intra-molecular and inter-molecular
attributes, behaviors, and relationships in the human cellular system has always been a focused research area of
computational biology. In this context, a lot of biological data integration systems have been deployed in the last couple of
decades. One of the prime and common objectives of all these systems is to better facilitate the end-users for exploring,
exploiting, and analyzing the integrated biological data for knowledge extraction. With the advent of especially highthroughput data generation technologies, biological data is growing and dispersing continuously, exponentially,
heterogeneously, and geographically. Due to this, biological data integration systems are too facing data integration and
data organization-related current and future challenges. The objective of this review is to quantitatively evaluate and
compare some of the recent warehouse-based multi-omics data integration systems to check their compliance with the
current and future data integration needs. For this, we identified some of the major data integration design characteristics
that should be in the multi-omics data integration model to comprehensively address the current and future data
integration challenges. Based on these design characteristics and the evaluation criteria, we evaluated some of the recent
data warehouse systems and showed categorical and comparative analysis results. Results show that most of the systems
exhibit no or partial compliance with the required data integration design characteristics. So, these systems need design
improvements to adequately address the current and future data integration challenges while keeping their service level
commitments in place.
Using schema transforation pathways for biological data integration
