Genomics is an interdisciplinary field of molecular biology that focuses on
studying the structure and function of the gene as well as its relation to gene mapping
and tailoring of the genome. The techniques of genomics have led to a revolution in the
area of clinical medicine and public health. Genomics plays a critical role in health
science by identifying targeted genes, precision in treatment, and drug therapies for
patients. The tremendous progress in genomics in the healthcare field is due to the high
speed, low cost, increased accuracy, and improved sensitivity of its techniques. The
next generation of genomics is marked by recent advances in the field, which utilise
computational tools, like artificial intelligence and machine learning. Machine
Learning (ML) allows computers to learn, filter, and classify data without human
intervention. The Next Generation genomic science technology has major applications
in the fields of early detection of tumour gene analysis, non-invasive prenatal
screening, and genomic tests for childhood and rare disorders. In this article, we have
discussed some recent advances in Next-Generation Machine Learning techniques
(NGML) in healthcare and summarised their applications in some recent genomic
science techniques like Nanopore sequencing, CRISPR-Cas system, and Spatial
transcriptomes technique.
Keywords: Artificial neural network, CRISPR-Cas system, Deep learning, Machine learning, Medical image analysis, Spatial transcriptosome.
