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Target Identification and Validation in Drug Discovery

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Cover of 'Target Identification and Validation in Drug Discovery'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 The Path to Oncology Drug Target Validation: An Industry Perspective
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    Chapter 2 Identification of Aptamers as Specific Binders and Modulators of Cell-Surface Receptor Activity
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    Chapter 3 The Design and Structure–Functional Properties of DNA-Based Immunomodulatory Sequences
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    Chapter 4 siRNA Design Principles and Off-Target Effects
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    Chapter 5 Western Blot Evaluation of siRNA Delivery by pH-Responsive Peptides
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    Chapter 6 High-Throughput RNAi Screening for the Identification of Novel Targets
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    Chapter 7 Integration of RNAi and Small Molecule Screens to Identify Targets for Drug Development
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    Chapter 8 CellProfiler and KNIME: Open Source Tools for High Content Screening.
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    Chapter 9 PARP inhibition as a prototype for synthetic lethal screens.
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    Chapter 10 Structure-Based Target Druggability Assessment
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    Chapter 11 Validating Pharmacological Disruption of Protein–Protein Interactions by Acceptor Photobleaching FRET Imaging
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    Chapter 12 Systematic Analysis of Complex Signal Transduction Pathways Using Protein Fragment Complementation Assays
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    Chapter 13 Reverse Phase Protein Microarrays and Their Utility in Drug Development
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    Chapter 14 A Cell Culture System That Mimics Chronic Lymphocytic Leukemia Cells Microenvironment for Drug Screening and Characterization
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    Chapter 15 Two-Dimensional vs. Three-Dimensional In Vitro Tumor Migration and Invasion Assays
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    Chapter 16 Tumor Spheroid-Based Migration Assays for Evaluation of Therapeutic Agents
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    Chapter 17 The Neurosphere Assay Applied to Neural Stem Cells and Cancer Stem Cells
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    Chapter 18 Genetically engineered animal models for in vivo target identification and validation in oncology.
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    Chapter 19 Target Validation in Mice by Constitutive and Conditional RNAi
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    Chapter 20 In Vivo Target Validation by Inducible RNAi in Human Xenograft Mouse Models
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    Chapter 21 Bright-Field In Situ Hybridization Methods to Discover Gene Amplifications and Rearrangements in Clinical Samples
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    Chapter 22 Combined MicroRNA In Situ Hybridization and Immunohistochemical Detection of Protein Markers
Attention for Chapter 8: CellProfiler and KNIME: Open Source Tools for High Content Screening.
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Chapter title
CellProfiler and KNIME: Open Source Tools for High Content Screening.
Chapter number 8
Book title
Target Identification and Validation in Drug Discovery
Published in
Methods in molecular biology, February 2013
DOI 10.1007/978-1-62703-311-4_8
Pubmed ID
Book ISBNs
978-1-62703-310-7, 978-1-62703-311-4
Authors

Stöter M, Niederlein A, Barsacchi R, Meyenhofer F, Brandl H, Bickle M, Martin Stöter, Antje Niederlein, Rico Barsacchi, Felix Meyenhofer, Holger Brandl, Marc Bickle, Stöter, Martin, Niederlein, Antje, Barsacchi, Rico, Meyenhofer, Felix, Brandl, Holger, Bickle, Marc

Abstract

High content screening (HCS) has established itself in the world of the pharmaceutical industry as an essential tool for drug discovery and drug development. HCS is currently starting to enter the academic world and might become a widely used technology. Given the diversity of problems tackled in academic research, HCS could experience some profound changes in the future, mainly with more imaging modalities and smart microscopes being developed. One of the limitations in the establishment of HCS in academia is flexibility and cost. Flexibility is important to be able to adapt the HCS setup to accommodate the multiple different assays typical of academia. Many cost factors cannot be avoided, but the costs of the software packages necessary to analyze large datasets can be reduced by using Open Source software. We present and discuss the Open Source software CellProfiler for image analysis and KNIME for data analysis and data mining that provide software solutions which increase flexibility and keep costs low.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 1 2%
Unknown 41 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 21%
Student > Ph. D. Student 8 19%
Student > Master 7 17%
Other 3 7%
Professor 3 7%
Other 5 12%
Unknown 7 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 31%
Biochemistry, Genetics and Molecular Biology 7 17%
Pharmacology, Toxicology and Pharmaceutical Science 4 10%
Computer Science 3 7%
Medicine and Dentistry 3 7%
Other 5 12%
Unknown 7 17%