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Cancer Bioinformatics Second Edition 2025 [Kõva köide]

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  • Formaat: Hardback, 290 pages, kõrgus x laius: 254x178 mm, 72 Illustrations, color; 8 Illustrations, black and white; X, 290 p. 80 illus., 72 illus. in color., 1 Hardback
  • Sari: Methods in Molecular Biology 2932
  • Ilmumisaeg: 08-Sep-2025
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 107164565X
  • ISBN-13: 9781071645659
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Cancer Bioinformatics Second Edition 2025Suurem pilt
  • Formaat: Hardback, 290 pages, kõrgus x laius: 254x178 mm, 72 Illustrations, color; 8 Illustrations, black and white; X, 290 p. 80 illus., 72 illus. in color., 1 Hardback
  • Sari: Methods in Molecular Biology 2932
  • Ilmumisaeg: 08-Sep-2025
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 107164565X
  • ISBN-13: 9781071645659
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781071645659.html
Märksõnad:

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 Methods in Molecular Biology series format, the chapters include brief introductions to the material, lists of necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and a Notes section which highlights tips on troubleshooting and avoiding known pitfalls.

Authoritative and cutting-edge, Cancer Bioinformatics, Second Edition aims to be comprehensive guide for researchers in the field.

 Bioconductors 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.