Computational Methods with Applications in Bioinformatics Analysis
Nonfiction, Science & Nature, Technology, Engineering, Computers
| Author: | Jeffrey J P Tsai, Ka-Lok Ng | ISBN: | 9789813207998 |
| Publisher: | World Scientific Publishing Company | Publication: | June 9, 2017 |
| Imprint: | WSPC | Language: | English |
| Author: | Jeffrey J P Tsai, Ka-Lok Ng |
| ISBN: | 9789813207998 |
| Publisher: | World Scientific Publishing Company |
| Publication: | June 9, 2017 |
| Imprint: | WSPC |
| Language: | English |
This compendium contains 10 chapters written by world renowned researchers with expertise in semantic computing, genome sequence analysis, biomolecular interaction, time-series microarray analysis, and machine learning algorithms.
The salient feature of this book is that it highlights eight types of computational techniques to tackle different biomedical applications. These techniques include unsupervised learning algorithms, principal component analysis, fuzzy integral, graph-based ensemble clustering method, semantic analysis, interolog approach, molecular simulations and enzyme kinetics.
The unique volume will be a useful reference material and an inspirational read for advanced undergraduate and graduate students, computer scientists, computational biologists, bioinformatics and biomedical professionals.
Contents:
- Unsupervised Clustering of Time Series Gene Expression Data Based on Spectrum Processing and Autoregressive Modeling
- Gene Ontology-Based Analysis of Time Series Gene Expression Data Using Support Vector Machines
- A Comparative Review of Graph-Based Ensemble Clustering as Transformation Methods for Microarray Data Classification
- Semantic Analytics of Biomedical Data
- Investigating Interactions Between Proteins and Nucleic Acids by Computational Approaches
- Bioinformatics Analysis of MicroRNA and Protein-Protein Interaction in Plant Host-Pathogen Interaction System
- Computational Modelling of the Alu-Carrying RNA Network in Th17-Mediated Autoimmune Diseases
- Principal Component Analysis Based Unsupervised Feature Extraction Applied to Bioinformatics Analysis
- Choquet Integral Algorithm for T-Cell Epitope Prediction Using Support Vector Machine
- Unsupervised Clustering Algorithms for Flow/Mass Cytometry Data
Readership: Researchers, academics, professionals, advanced undergraduate and graduate students, computer scientists, computational biologists, bioinformatics and biomedical professionals.
This compendium contains 10 chapters written by world renowned researchers with expertise in semantic computing, genome sequence analysis, biomolecular interaction, time-series microarray analysis, and machine learning algorithms.
The salient feature of this book is that it highlights eight types of computational techniques to tackle different biomedical applications. These techniques include unsupervised learning algorithms, principal component analysis, fuzzy integral, graph-based ensemble clustering method, semantic analysis, interolog approach, molecular simulations and enzyme kinetics.
The unique volume will be a useful reference material and an inspirational read for advanced undergraduate and graduate students, computer scientists, computational biologists, bioinformatics and biomedical professionals.
Contents:
- Unsupervised Clustering of Time Series Gene Expression Data Based on Spectrum Processing and Autoregressive Modeling
- Gene Ontology-Based Analysis of Time Series Gene Expression Data Using Support Vector Machines
- A Comparative Review of Graph-Based Ensemble Clustering as Transformation Methods for Microarray Data Classification
- Semantic Analytics of Biomedical Data
- Investigating Interactions Between Proteins and Nucleic Acids by Computational Approaches
- Bioinformatics Analysis of MicroRNA and Protein-Protein Interaction in Plant Host-Pathogen Interaction System
- Computational Modelling of the Alu-Carrying RNA Network in Th17-Mediated Autoimmune Diseases
- Principal Component Analysis Based Unsupervised Feature Extraction Applied to Bioinformatics Analysis
- Choquet Integral Algorithm for T-Cell Epitope Prediction Using Support Vector Machine
- Unsupervised Clustering Algorithms for Flow/Mass Cytometry Data
Readership: Researchers, academics, professionals, advanced undergraduate and graduate students, computer scientists, computational biologists, bioinformatics and biomedical professionals.
