Research Area:  Machine Learning

Abstract:

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level,with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

Keywords:  
Bioinformatics
NGS Data
Biological
Detection
Oncological Clinical
next-generation sequencing
machine learning

Author(s) Name:  Serena Dotolo ,Riziero Esposito Abate ,Cristin Roma ,Davide Guido ,Alessia Preziosi ,Beatrice Tropea ,Fernando Palluzzi ,Luciano Giacò and Nicola Normanno

Publisher name:  MDPI

DOI:  10.3390/biomedicines10092074

Volume Information:  Volume 10, Issue 9

Paper Link:   https://www.mdpi.com/2227-9059/10/9/2074