Description

Book Synopsis

 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.

Cancer Bioinformatics

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    A Hardback by Alexander Krasnitz

    £143.99

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      Book details

      Publisher Humana
      Published 10 July 2025
      ISBN-13 9781071645659
      978-1071645659
      ISBN-10 107164565X
      Also in:
      Oncology

      Description

      Book Synopsis

       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.

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