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What is Feature Space

Handbook of Research on Computational Intelligence Applications in Bioinformatics
Space associated feature vector.
Published in Chapter:
Computational Methods for Prediction of Protein-Protein Interactions: PPI Prediction Methods
Sneha Rai (Netaji Subhas Institute of Technology, India) and Sonika Bhatnagar (Netaji Subhas Institute of Technology, India)
DOI: 10.4018/978-1-5225-0427-6.ch010
Abstract
The key signaling pathways in cellular processes involve protein-protein interactions (PPIs). A perturbation in the balance of PPIs occurs in various pathophysiological processes. There are a large numbers of experimental methods for detection of PPIs. However, experimental PPI determination is time consuming, expensive, error prone and does not effectively cover transient interactions. Therefore, overlaying and integration of predictive methods with experimental results provides statistical robustness and biological significance to the PPI data. In this chapter, the authors describe PPIs in terms of types, importance, and biological consequences. This chapter also provides a comprehensive description on various computational approaches for PPI prediction. Prediction of PPI can be done through: 1) Genomic information based methods 2) Structure based methods 3) Network topology based methods: 4) Literature and data mining based methods 5) Machine learning methods. For ease of use and convenience, a summary of various databases and software for PPI prediction has been provided.
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More Results
Ensemble of ANN for Traffic Sign Recognition
n-dimensional space where each example (pattern) is represented as a point. The dimension of this space is equal to the number of features used to describe the patterns.
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Evaluation of Genetic Algorithm as Learning System in Rigid Space Interpretation
In pattern recognition, a feature space (O ? ) is an abstract space where each pattern sample is represented as a point in n-dimensional space . Its dimension is determined by the number of features used to describe the patterns. Similar samples are grouped together, which allows the use of density estimation for finding patterns.
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