Table of Contents
Chapter 1: Building research data handling systems with open source tools
Chapter 2: Interactive predictive toxicology with Bioclipse and OpenTox
2.2 Basic Bioclipse-OpenTox interaction examples
2.3 Use Case 1: Removing toxicity without interfering with pharmacology
2.4 Use Case 2: Toxicity prediction on compound collections
Chapter 3: Utilizing open source software to facilitate communication of chemistry at RSC
3.2 Project Prospect and open ontologies
Chapter 4: Open source software for mass spectrometry and metabolomics
4.2 A short mass spectrometry primer
4.3 Metabolomics and metabonomics
4.5 Metabolomics data processing
4.6 Metabolomics data processing using the open source workflow engine, KNIME
4.7 Open source software for multivariate analysis
4.8 Performing PCA on metabolomics data in R/KNIME
4.9 Other open source packages
Chapter 5: Open source software for image processing and analysis: picture this with ImageJ
5.3 ImageJ macros: an overview
5.5 Industrial applications of image analysis
Chapter 6: Integrated data analysis with KNIME
6.3 Benefits of 'professional open source'
Chapter 7: Investigation-Study-Assay, a toolkit for standardizing data capture and sharing
7.1 The growing need for content curation in industry
7.2 The BioSharing initiative: cooperating standards needed
7.3 The ISA framework – principles for progress
8.5 Case study: a simple ChIP-seq pipeline
Chapter 9: Creating an in-house ’omics data portal using EBI Atlas software
9.2 Leveraging ’omics data for drug discovery
9.4 Deploying Atlas in the enterprise
Chapter 10: Setting up an ’omics platform in a small biotech
10.2 General changes over time
10.4 Maintenance of the system
Chapter 11: Squeezing big data into a small organisation
11.2 Our service and its goals
11.3 Manage the data: relieving the burden of data-handling
11.5 Standardising to your requirements
11.6 Analysing the data: helping users work with their own data
11.7 Helping biologists to stick to the rules
11.9 Helping the user to understand the details
Chapter 13: Free and open source software for web-based collaboration
13.2 Application of the FLOSS assessment framework
14.2 The need to make sense of large amounts of data
14.3 Open source search technologies
14.4 Creating the foundation layer
14.5 Visualisation technologies
14.6 Prefuse visualisation toolkit
14.9 Challenges and future developments
14.11 Thanks and Acknowledgements
15.1 Utopia Documents in industry
15.3 Sharing, while playing by the rules
15.4 History and future of Utopia Documents
16.2 Wiki-based Enterprise Encyclopaedia
16.4 Conclusion and future directions
Chapter 17: Building disease and target knowledge with Semantic MediaWiki
17.2 The Disease Knowledge Workbench (DKWB)
Chapter 18: Chem2Bio2RDF: a semantic resource for systems chemical biology and drug discovery
18.1 The need for integrated, semantic resources in drug discovery
18.2 The Semantic Web in drug discovery
18.3 Implementation challenges
18.4 Chem2Bio2RDF architecture
18.5 Tools and methodologies that use Chem2Bio2RDF
19.1 The challenge of Big Data
19.3 Semantic technologies overview
19.4 The design and features of TripleMap
19.5 TripleMap Generated Entity Master ('GEM') semantic data core
19.6 TripleMap semantic search interface
19.7 TripleMap collaborative, dynamic knowledge maps
19.8 Comparison and integration with third-party systems
Chapter 20: Extreme scale clinical analytics with open source software
20.5 Unified Medical Language System (UMLS)
20.8 Final architectural overview
Chapter 21: Validation and regulatory compliance of free/open source software
21.2 The need to validate open source applications
21.3 Who should validate open source software?
21.5 Risk management and open source software
21.6 Key validation activities
21.7 Ongoing validation and compliance
Chapter 22: The economics of free/open source software in industry
22.4 Open source software in the pharmaceutical industry
22.5 Open source as a catalyst for pre-competitive collaboration in the pharmaceutical industry