Buch, Englisch, 344 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 865 g
Reihe: Methods in Molecular Biology
Buch, Englisch, 344 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 865 g
Reihe: Methods in Molecular Biology
ISBN: 978-1-0716-4565-9
Verlag: Springer US
This second volume covers state-of-the-art cancer-related methods and tools for data analysis and interpretation. Chapters detail methods on cancer-related software repositories, databases, cloud computing resources, genomic alterations caused by cancer, methods on evaluate findings from liquid biopsies, and prognostic tools for immunotherapies. Written in the highly successful series format, the chapters include brief introductions tothe material, lists of necessary materials and reagents, step-by-step, readilyreproducible laboratory protocols, and a Notes section which highlightstips on troubleshooting and avoiding known pitfalls.
Authoritative and cutting-edge, aims to be comprehensive guide forresearchers in the field.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Bioconductor’s Computational Ecosystem for Genomic Data Science in Cancer.- Informatics Workflows for scalable data analysis: an RNA sequencing tutorial.- Using the Cancer Epitope Database and Analysis Resource (CEDAR).- Quantifying the Prevalence of Non-B DNA Motifs as a Marker of Non-B Burden in Cancer using NBBC.- Starfish: deciphering complex genomic rearrangement signatures across human cancers.- Using FFPEsig to remove formalin-induced artefacts and characterise mutational signatures in cancer.- Inferring phenotypes of copy number clones in cancer populations using TreeAlign.- Inference of genetic ancestry from cancer-derived molecular data with RAIDS.- Pruning-assisted modeling of network graph connectivity from spatial transcriptomic data.- Inferring metabolic flux from gene-expression data using METAFlux.- Functional Pathway Inference Analysis (FPIA).- NGP: a tool to detect noncoding RNA-gene regulatory pairs from expression data.- MODIG: An Attention Mechanism-based Approach for Cancer Driver Gene Identification.- Predictive modeling of anti-cancer drug sensitivity using REFINED CNN.- Anti-cancer monotherapy and polytherapy drug response prediction using deep learning: guidelines and best practices.- Identification of somatic variants in cancer genomes from tissue and liquid biopsy samples.- SUMMER: a practical tool for identifying factors and biomarkers associated with pan-cancer survival.- Predicting tumor antigens using the LENS workflow through RAFT.
