| Description |
1 online resource |
| Series |
SpringerBriefs in computer science |
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SpringerBriefs in computer science.
|
| Contents |
Preface; Acknowledgements; Contents; Part I Biological Networks; 1 Data Sources and Models; 1.1 Protein Data; 1.1.1 Protein -- Protein Interaction Networks; 1.2 Disease Data; 1.2.1 Genotype -- Phenotype Networks; 1.3 Co-expression Data; 1.3.1 Network Populations; References; 2 Problems and Techniques; 2.1 Network Alignment; 2.1.1 Techniques; 2.1.2 Querying; 2.2 Network Clustering; 2.2.1 Techniques; 2.3 Network Motif Extraction; 2.3.1 Techniques; References; Part II Pattern Mining; 3 Exceptional Pattern Discovery; 3.1 Frequent Pattern Mining; 3.2 Emerging Patterns and Contrast Sets |
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3.3 Pattern Discovery on Networks3.4 Pattern Discovery on Biological Data; References; 4 Discriminating Graph Pattern Mining from Gene Expression Data; 4.1 Motivations; 4.2 Network Model; 4.2.1 Strength of the Relationships; 4.2.2 Relevance of the Relationships; 4.2.3 Building Networks; 4.3 Statement of the Problem; 4.3.1 Discriminating Pattern; 4.3.2 Problem Definition; 4.4 Technique; 4.4.1 Algorithm; 4.4.2 Strength Computation Details; 4.4.3 Relevance Computation Details; References |
| Summary |
This work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example genes). Among these exceptional patterns, of particular interest are discriminative patterns, namely those which are able to discriminate between two input populations (for example healthy/unhealthy samples). In addition, the work includes a discussion on the most recent proposal on discovering discriminative patterns, in which there is a labeled network for each sample, resulting in a database of networks representing a sample set. This enables the analyst to achieve a much finer analysis than with traditional techniques, which are only able to consider an aggregated network of each population |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
| Subject |
Biological systems -- Simulation methods
|
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Pattern recognition systems.
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Pattern Recognition, Automated
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Pattern recognition.
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Data mining.
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Molecular biology.
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Medical genetics.
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Life sciences: general issues.
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NATURE -- Reference.
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SCIENCE -- Life Sciences -- Biology.
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SCIENCE -- Life Sciences -- General.
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Biological systems -- Simulation methods
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Pattern recognition systems
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| Form |
Electronic book
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| Author |
Rombo, Simona E., author
|
|
Serrao, Cristina, author
|
| ISBN |
9783319634777 |
|
3319634771 |
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