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New Approaches of Protein Function Prediction from Protein Interaction Networks contains the critical aspects of PPI network based protein function prediction, including semantically assessing the reliability of PPI data, measuring the functional similarity between proteins, dynamically selecting prediction domains, predicting functions, and establishing corresponding prediction frameworks.
Functional annotation of proteins is vital to biological and clinical research and other applications due to the important roles proteins play in various biological processes. Although the functions of some proteins have been annotated via biological experiments, there are still many proteins whose functions are yet to be annotated due to the limitations of existing methods and the high cost of experiments. To overcome experimental limitations, this book helps users understand the computational approaches that have been rapidly developed for protein function prediction.
- Provides innovative approaches and new developments targeting key issues in protein function prediction
- Presents heuristic ideas for further research in this challenging area
Researchers in computational biology or bioinformatics, experimental biologists in universities, research institutes and commercial organizations
- Chapter 1: Introduction
- 1.1 Gene Ontology (GO) Scheme
- 1.2 FunCat Scheme
- 1.3 Approaches Based on Amino Acid Sequences
- 1.4 Approaches Based on Protein Structure
- 1.5 Approaches Based on Genome Sequences
- 1.6 Approaches Based on Phylogenetic Data
- 1.7 Approaches Based on Microarray Expression Data
- 1.8 Approaches Based on Protein Interaction Networks
- 1.9 Approaches Based on Biomedical Literature
- 1.10 Approaches Based on the Combination of Multiple Data Types
- Chapter 2: Reliability of Protein Interactions
- 2.1 Background
- 2.2 Semantic Interaction Reliability 1 (R1)
- 2.3 Semantic Interaction Reliability 2 (R2)
- 2.4 Semantic Reliability (SR) Assessment
- 2.5 Discussions
- 2.6 Evaluation of Reliability Assessment
- Chapter 3: Clustering-Based Protein Function Prediction
- 3.1 Background
- 3.2 Semantic Protein Similarity
- 3.3 Layered Protein Function Prediction
- 3.4 Cluster Feature Function Selection
- 3.5 Dynamic Clustering-Based Function Prediction
- 3.6 Discussions
- Chapter 4: Iterative Approaches of Protein Function Prediction
- 4.1 Local Iterative Function Prediction Method
- 4.2 Semi-local Iterative Function Prediction Method
- 4.3 Global Iterative Function Prediction Method
- 4.4 Discussions
- Chapter 5: Functional Aggregation for Protein Function Prediction
- 5.1 Introduction
- 5.2 Functional Aggregation-Based Prediction
- 5.3 Discussions
- Chapter 6: Searching for Domains for Protein Function Prediction
- 6.1 Introduction
- 6.2 Prediction Algorithm
- 6.3 Discussions
- Chapter 7: Protein Function Prediction from Functional Connectivity
- 7.1 Introduction
- 7.2 Function Prediction Algorithm
- 7.3 Discussions
- Chapter 8: Conclusions
- 8.1 Summary
- 8.2 Future Directions
- No. of pages:
- © Academic Press 2017
- 11th January 2017
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Dr. Jingyu Hou has been undertaking research in bioinformatics, especially in protein function prediction from protein-protein interaction (PPI) networks for many years. Dr Hou is part of a strong bioinformatics research team from Deakin University in Australia and has published many high quality journal and international conference papers. Particularly for protein function prediction, Jingyu’s research team has proposed and published various innovative algorithms to dynamically predict protein functions from PPI networks. These algorithms refer to many critical aspects of protein function prediction, including semantic similarities of proteins, dynamic selection of prediction domains, iterative function predictions and so on which are advanced and interesting in this area.
School of Information Technology, Deakin University, Australia
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