Artificial intelligence for genomics: a look into it

The latest progress in genomics and artificial intelligence (AI) sees both disciplines work together to improve results in the relatively new medical area called precision medicine. This chapter aims to provide readers with a review of AI techniques ingesting genomics data to extract patterns and high-level information. Many new and sophisticated AI architectures have been introduced in the scientific community since the release of the famous Human Genome Project. The latter was delivered in 2003 and allowed sequencing and mapping of all the genes of our species (Homo sapiens). Genomics has paved the way for deeper insights into correlations between changes in DNA sequences and diseases. As described throughout the sections in the manuscript, deoxyribonucleic acid (DNA) sequences are big-sized. This feature makes them suitable for investigation through both machine and deep learning (DL) methods. Moreover, the disruptive advent of DL in the scientific community pushed the bar for achievable accuracy rates in many tasks. Genomics makes no exception. AI methods have been primarily employed to tackle some tasks for biomedical image analysis: detection, classification and segmentation of suspicious regions from MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography) and CT (Computer Tomography), to mention some, have been broadly addressed using machine learning (ML) and DL approaches. Over the last few years, there has been an exponential spike in the number of DL techniques for genomics. Nowadays, genomics and AI are closely twisted in the attempt to achieve ambitious objectives, such as predicting treatment outcomes to deliver patient-tailored therapies, biomarker discoveries, radiotherapy responses and predicting drug effectiveness from cancer genomic signature. The main goal here is to check through the current state-of-the-art AI methods for genomics spanning the most challenging aspects of today's landscape