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Cancer Drug Resistance

Overview of attention for book
Cover of 'Cancer Drug Resistance'

Table of Contents

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    Book Overview
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    Chapter 1 Cancer Drug Resistance: A Brief Overview from a Genetic Viewpoint
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    Chapter 2 Classical and Targeted Anticancer Drugs: An Appraisal of Mechanisms of Multidrug Resistance.
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    Chapter 3 In Vitro Methods for Studying the Mechanisms of Resistance to DNA-Damaging Therapeutic Drugs.
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    Chapter 4 In Vitro Approaches to Study Regulation of Hepatic Cytochrome P450 (CYP) 3A Expression by Paclitaxel and Rifampicin.
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    Chapter 5 Uptake and Permeability Studies to Delineate the Role of Efflux Transporters.
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    Chapter 6 Dynamics of Expression of Drug Transporters: Methods for Appraisal.
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    Chapter 7 Fluorimetric Methods for Analysis of Permeability, Drug Transport Kinetics, and Inhibition of the ABCB1 Membrane Transporter
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    Chapter 8 Resistance to Targeted Therapies in Breast Cancer.
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    Chapter 9 MicroRNAs and Cancer Drug Resistance
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    Chapter 10 The Role of MicroRNAs in Resistance to Current Pancreatic Cancer Treatment: Translational Studies and Basic Protocols for Extraction and PCR Analysis
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    Chapter 11 Methods for Studying MicroRNA Expression and Their Targets in Formalin-Fixed, Paraffin-Embedded (FFPE) Breast Cancer Tissues
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    Chapter 12 The Regulatory Role of Long Noncoding RNAs in Cancer Drug Resistance
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    Chapter 13 Cancer Exosomes as Mediators of Drug Resistance.
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    Chapter 14 Isolation and Characterization of Cancer Stem Cells from Primary Head and Neck Squamous Cell Carcinoma Tumors
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    Chapter 15 Clinical and Molecular Methods in Drug Development: Neoadjuvant Systemic Therapy in Breast Cancer as a Model.
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    Chapter 16 Proteomics in the Assessment of the Therapeutic Response of Antineoplastic Drugs: Strategies and Practical Applications.
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    Chapter 17 Managing Drug Resistance in Cancer: Role of Cancer Informatics
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    Chapter 18 Erratum to: In Vitro Methods for Studying the Mechanisms of Resistance to DNA-Damaging Therapeutic Drugs
Attention for Chapter 17: Managing Drug Resistance in Cancer: Role of Cancer Informatics
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  • Good Attention Score compared to outputs of the same age and source (74th percentile)

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2 X users

Citations

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Summary X Dimensions citations
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Chapter title
Managing Drug Resistance in Cancer: Role of Cancer Informatics
Chapter number 17
Book title
Cancer Drug Resistance
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3347-1_17
Pubmed ID
26910081
Book ISBNs
978-1-4939-3345-7, 978-1-4939-3347-1
Authors

Ankur Gautam, Kumardeep Chaudhary, Rahul Kumar, Sudheer Gupta, Harinder Singh, Gajendra P. S. Raghava

Editors

José Rueff, António Sebastião Rodrigues

Abstract

Understanding and managing cancer drug resistance is the main goal of the modern oncology programs worldwide. One of the major factors contributing to drug resistance in cancer cells is the acquired mutations in drug targets. Advances in sequencing technologies and high-throughput screening assays have generated huge information related to pharmaco-profiling of anticancer drugs and revealed the mutational spectrum of different cancers. Systematic meta-analysis of this complex data is very essential to make useful conclusions in order to manage cancer drug resistance. Bioinformatics can play a significant role to interpret this complex data into useful conclusions. In this chapter, the use of bioinformatics platforms, particularly CancerDR, in understanding the cancer drug resistance is described.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
 Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 4%
Ukraine 1 4%
Unknown 26 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 32%
Student > Master 6 21%
Student > Ph. D. Student 4 14%
Student > Bachelor 2 7%
Professor 1 4%
Other 2 7%
Unknown 4 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 29%
Agricultural and Biological Sciences 6 21%
Medicine and Dentistry 2 7%
Social Sciences 2 7%
Computer Science 2 7%
Other 3 11%
Unknown 5 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 July 2016.
All research outputs
#13,460,530
of 22,852,911 outputs
Outputs from Methods in molecular biology
#3,622
of 13,128 outputs
Outputs of similar age
#189,613
of 393,602 outputs
Outputs of similar age from Methods in molecular biology
#349
of 1,470 outputs
Altmetric has tracked 22,852,911 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,128 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 393,602 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 1,470 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.

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