Consequential Innovations in Nature-Inspired Intelligent Computing Techniques for Biomarkers and Potential Therapeutics Identification

Abstract

Computational biology has changed how healthcare systems and biomedical engineering work. Nature-inspired intelligent computing (NIIC) approaches in predicting potential biomarkers and drug targets could be an astonishing bridge between biology/nature with today’s advanced and sophisticated areas like artificial intelligence, deep learning, computer vision, and others. Analyzing disease biomarkers is an emerging field of interest. Several molecular evaluations have been developed to detect biomarkers that indicate disease response to specific therapies. Recognition of these molecules and understanding their molecular mechanisms is critical for disease prognosis and late-stage therapeutics development. Breakthroughs in genomics and transcriptional analyses have significantly increased our understanding of the poorly understood genomic matter or dark matter. The systematic identification of disease-associated lncRNAs has expanded our understanding of the underlying molecular mechanisms of complex diseases, but it has also been shown to have an inherent advantage over protein-coding genes in disease diagnosis, prognosis, and treatment. Given the lower efficiency and increased time and cost of biological experiments, computer-aided inference of disease-associated RNAs using nature-inspired intelligent computing methods has emerged as a promising approach for expediting the study of lncRNA functions and providing complementary value for experimental analyses. In this chapter, we have discussed the fundamentals of NIIC techniques, their role in the diagnosis of various diseases, and their futuristic role in the healthcare industry.