Computational Methods for Precision Oncology

lt;p>Alessandro Laganà is Assistant Professor of Oncological Sciences at the Icahn School of Medicine at Mount Sinai, NY. Dr. Laganà's main research interests are integrative cancer genomics, cancer network biology and precision oncology. His current research is focused on the treatment of Multiple Myeloma (malignancy of bone marrow plasma cells). He received both his M.Sc. and Ph.D. from the University of Catania in Catania, Italy. He is also the author of the Humana Press title MicroRNA Target Identification: Methods and Protocols (2019).

Part One: Basic Concepts in Precision Oncology.- Chapter 1. Design and Implementation of a precision oncology workflow.- Chapter 2. An overview of cancer genome sequencing.- Chapter 3. Software infrastructures for precision medicine workflows.- Part Two: Current Computational Methods and Tools in Precision Oncology.- Chapter 4. Somatic and germline variant calling from next-generation sequencing data.- Chapter 5. Identification of copy number alterations from next-generation sequencing data.- Chapter 6. Genome-wide discovery of large-scale structural variants from next-generation sequencing data.- Chapter 7. Assessment of microsatellite instability from next-generation sequencing data.- Chapter 8. Computational approaches for the investigation of intra-tumor heterogeneity and clonal evolution from bulk sequencing data.- Chapter 9. Analysis of the transcriptome for precision oncology applications.- Chapter 10. Computational methods for drug repurposing.- Chapter 11. Pathway for analysisfor precision oncology applications.- Chapter 12. Identification of fusion transcripts from RNA-seq.- Chapter 13. Computational resources for the interpretation of variations in cancer.- Chapter 14. Analysis of electronic health records for precision oncology.- Chapter 15. Network approaches for precision oncology.- Chapter 16. Experimental validation of genomic alterations.- Part Three: Advanced Concepts - The Future of Precision Oncology.- Chapter 17. Molecular profiling of liquid biopsies for precision oncology.- Chapter 18. Artificial intelligence for precision oncology.- Chapter 19. Single-cell technologies in precision oncology.- Chapter 20. Computational approaches for precision immuno-oncology.- Chapter 21. Multi-pmics profiling of the tumor microenvironment.- Chapter 22. Epigenetic and precision oncology.- Chapter 23. Next generation knowledge bases to support precision oncology.