LEADER 00000cam a2200673Ii 4500 
001    on1227386126 
003    OCoLC 
005    20220114043859.0 
006    m     o  d         
007    cr cn||||||||| 
008    201219s2021    enk     o     000 0 eng d 
019    1226782912 
020    9781788019965|q(electronic book) 
020    1788019962|q(electronic book) 
020    9781788019958|q(PDF) 
020    1788019954|q(PDF) 
020    |z9781788017619 
020    |z1788017617 
035    (OCoLC)1227386126|z(OCoLC)1226782912 
040    EBLCP|beng|cEBLCP|dN$T|dOCLCO|dEBLCP|dYDX|dUKRSC|dUIU
       |dOCLCF|dOCLCO 
049    RIDW 
050  4 RC71.3 
082 04 616.075|223 
090    RC71.3 
245 00 Detection methods in precision medicine /|cEditors: Mengsu
       (Michael) Yang, Michael Thompson. 
264  1 Cambridge :|bRoyal Society of Chemistry,|c2021. 
300    1 online resource (379 pages). 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
340    |gpolychrome|2rdacc 
347    text file|2rdaft 
490 1  Detection science ;|v18 
505 0  Intro -- Halftitle -- Detection Science Series -- Title --
       Copyright -- Preface -- Contents -- Part 1 Biomarkers for 
       Precision Medicine -- Chapter 1 Genome-wide Discovery of 
       MicroRNA Biomarkers for Cancer Precision Medicine -- 1.1 
       Introduction -- 1.2 A Review of miRNA-based Cancer 
       Biomarkers in Various Clinical Applications -- 1.2.1 
       Diagnosis -- 1.2.2 Prognosis -- 1.2.3 Prediction of 
       Therapeutic Response -- 1.2.4 Disease Status Monitoring --
       1.3 A Data-driven Methodology for miRNA Biomarker 
       Development -- 1.3.1 Data Collection -- 1.3.2 In Silico 
       Discovery and Validation 
505 8  1.3.3 Clinical Validation -- 1.4 Advantages and Challenges
       -- Abbreviations -- References -- Chapter 2 Extracellular 
       Vesicles in Precision Medicine -- 2.1 Introduction to 
       Extracellular Vesicles -- 2.1.1 Classification and 
       Biogenesis -- 2.1.2 Composition -- 2.1.3 Isolation and 
       Characterisation -- 2.2 Physiological Roles of EVs -- 2.3 
       Pathological Roles of EVs -- 2.4 Diagnostic and Monitoring
       Potential of EVs -- 2.4.1 EV Levels and Size -- 2.4.2 EV 
       DNA -- 2.4.3 EV RNA -- 2.4.4 EV Proteins -- 2.4.5 EV 
       Lipids and Metabolites -- 2.5 Therapeutic Potential of EVs
       -- 2.6 Conclusions and Future Directions 
505 8  Abbreviations -- References -- Chapter 3 Proteomics in 
       Precision Medicine -- 3.1 Introduction -- 3.2 Sample 
       Preparation for Proteomics -- 3.3 LC-MS Analysis of 
       Digested Peptides -- 3.3.1 LC Separation -- 3.3.2 MS 
       Instrumentation -- 3.3.3 Data Acquisition Modes -- 3.4 
       Quantitative Proteomics -- 3.4.1 Label-based 
       Quantification -- 3.4.2 Label-free Quantification -- 3.5 
       Proteomics Data Analysis -- 3.5.1 Peptide Identification -
       - 3.5.2 Protein Assembly -- 3.5.3 Protein Quantification -
       - 3.6 Recent Applications of Proteomics in Precision 
       Medicine 
505 8  3.6.1 Proteomics for Biomarker Discovery and Verification 
       -- 3.6.2 Proteomics for Identification of Therapeutic 
       Targets -- 3.6.3 Proteomics for Diagnosis -- Abbreviations
       -- References -- Chapter 4 Computational Prediction of 
       Tumor Neoantigen for Precision Oncology -- 4.1 Background 
       Introduction -- 4.2 Computation Prediction of Neoantigen 
       from High-throughput Sequencing Data -- 4.2.1 Preparing 
       Input Data: Reference Genome Alignment, Gene-calling and 
       Annotation -- 4.2.2 Isoform Structural Comparison to 
       Visualize Alternative Splicing Events 
505 8  4.2.3 Neoantigen Prediction Based on Peptide Sequence 
       Alignment -- 4.2.4 Neoantigen Binding Affinity by Epitope 
       Prediction Software -- 4.3 PacBio Long-read Sequencing 
       Dataset to Test Computational Tool -- 4.4 Identification 
       of Neoantigen Candidates for Melanoma Immunotherapy Study 
       -- 4.5 Discussion -- References -- Chapter 5 Big Data and 
       Its Emerging Role in Precision Medicine and Therapeutic 
       Response -- 5.1 Introduction -- 5.2 Big Data: Advantages 
       and Milestones -- 5.2.1 Genome -- 5.2.2 Epigenome 
       (Methylome) -- 5.2.3 Transcriptome 
505 8  5.3 Big Data and Its Integration in Therapeutic Response 
       Modeling. 
520    This book will be among the first to cover the detection 
       methods for precision medicine that are set to transform 
       health care in the future. 
588 0  Print version record. 
590    eBooks on EBSCOhost|bEBSCO eBook Subscription Academic 
       Collection - North America 
650  0 Diagnosis|0https://id.loc.gov/authorities/subjects/
       sh85037489|xTechnological innovations.|0https://id.loc.gov
       /authorities/subjects/sh2001009095 
650  0 Biochemical markers.|0https://id.loc.gov/authorities/
       subjects/sh85014122 
650  7 Diagnosis.|2fast|0https://id.worldcat.org/fast/892273 
650  7 Technological innovations.|2fast|0https://id.worldcat.org/
       fast/1145002 
650  7 Biochemical markers.|2fast|0https://id.worldcat.org/fast/
       831950 
655  4 Electronic books. 
700 1  Yang, Mengsu,|q(Michael)|0https://id.loc.gov/authorities/
       names/no2021077895 
700 1  Thompson, Michael.|0https://id.loc.gov/authorities/names/
       n81111812 
776 08 |iPrint version:|aYang, Mengsu (Michael)|tDetection 
       Methods in Precision Medicine|dCambridge : Royal Society 
       of Chemistry,c2020|z9781788017619 
830  0 RSC detection science series ;|0https://id.loc.gov/
       authorities/names/no2014003495|vno. 18. 
856 40 |uhttps://rider.idm.oclc.org/login?url=https://
       search.ebscohost.com/login.aspx?direct=true&scope=site&
       db=nlebk&AN=2705684|zOnline ebook via EBSCO. Access 
       restricted to current Rider University students, faculty, 
       and staff. 
856 42 |3Instructions for reading/downloading the EBSCO version 
       of this ebook|uhttp://guides.rider.edu/ebooks/ebsco 
901    MARCIVE 20231220 
948    |d20220127|cEBSCO|tEBSCOebooksacademic NEW 6019|lridw 
994    92|bRID