Cutting edge and novel approaches of Bioinformatics ( OMIC technologies); packed with enhanced AI is a blessing in healthcare
Keywords:
Omics data analysis, Bioinformatic, Genomics, Cutting edgeAbstract
Background: Cellular processes are one of the most complex and sophisticated operations in living biological systems. They are studied piece by piece and with enhanced understanding of synchronization among these pieces, help of advanced AI software’s play a key role. For this reason, Bioinformatics is growing and gaining new more improved A.I tools to solve this puzzle of pathologies that we see now a days.
Introduction: OMIC technologies help us to understand cellular processes in a more meaningful way then previously thought. Big and traditional OMIC approaches usually have been consisting of Genomics, Epigenomics, Transcriptomic and Proteomics. These protocols of understanding cellular biological processes through machine learning and AI agents has been tremendously successful in data science, preferential diagnostics and personalized treatment. But very advanced upgrades have been put forward by scientists to develop new cures for human pathologies.
Method: Data gathered and analyzed from Google scholar, PubMed and Science direct.
Result: As scientists dwelled deep into the fascinating world of intra cellular interactions; they have reported and developed something new in Bioinformatics . That is the concept of cutting edge next generation OMICS like epiomics (Epigenomics, epitranscriptomics and epiproteomics), Interactomics ( DNA-RNA interactomics, DNA-protein interactomics,RNA-RNA interactomics , RNA -protein interactomics, protein-protein interactomics, protein metabolites interactomics ) and Immunomics ( immune genomics, immune transcriptomics, immune proteomics and immune metabolomics). All these newly found interactive behaviors and approaches in bioinformatics coupled with advanced AI software’s for integrative data analysis pave the way for more development of learned ,therapeutic and targeted personalized medicines.
Conclusion: More improved AI tailored personalized medicines are in the making.