Secure CheckoutPersonal information is secured with SSL technology.
Free ShippingFree global shipping
No minimum order.
Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome helps readers identify and select the specific genes causing oncogenes. The book also addresses the validation of the selected genes using various classification techniques and performance metrics, making it a valuable source for cancer researchers, bioinformaticians, and researchers from diverse fields interested in applying systems biology approaches to their studies.
- Provides well described techniques for the purpose of gene selection/feature selection for the generation of gene subsets
- Presents and analyzes three different types of gene selection algorithms: Support Vector Machine-Bayesian T-Test-Recursive Feature Elimination (SVM-BT-RFE), Canonical Correlation Analysis-Trace Ratio (CCA-TR), and Signal-To-Noise Ratio-Trace Ratio (SNRTR)
- Consolidates fundamental knowledge on gene datasets and current techniques on gene regulatory networks into a single resource
Bioinformaticians, Cancer Researchers, researchers interested in applying Systems Biology approaches to their studies; Geneticists, Bioengineers, researchers interested in Machine learning, Data Mining, Bioinformatics
1. Literature Review
2. SVM-BT-RFE: An Improved Gene Selection Framework Using Bayesian T-Test Embedded in Support Vector Machine (Recursive Feature Elimination) Algorithm
3. Enhanced Gene Ranking Approaches Using Modified Trace Ratio Algorithm for Gene Expression Data
4. SNR-TR Gene Ranking Method: A Signal-to-Noise Ratio Based Gene Selection Algorithm Using Trace Ratio for Gene Expression Data
5. Visualization of Interactive Gene Regulatory Network Using Gene Selection Techniques from Expression Data
6. Conclusion and Future Work
- No. of pages:
- © Academic Press 2018
- 9th May 2018
- Academic Press
- Paperback ISBN:
- eBook ISBN:
Dr. Mishra is currently working as an Associate Professor in the Department of Computer Science & Engineering, Vignana Bharathi Institute of Technology, Hyderabad and as former Head of the Department for the same institution. She was earlier working as an Assistant Professor, Department of Computer Science & Engineering, Institute of Technical Education & Research, Siksha O Anusandhan (Deemed-to-be University), Bhubaneswar, Odisha, India. She has guided 5 M.Tech thesis and more than 30 B.Tech students. Dr. Mishra has around 22 publications in various peer-reviewed journals and conference, 3 book chapters and 1 book to her credit. Her area of research is basically in Data Mining, Bioinformatics and Machine Learning. She is currently into the field of Geoinformatics and Deep Learning.
Associate Professor, Department of Computer Science & Engineering, Vignana Bharathi Institute of Technology (VBIT), Hyderabad, India
Department of Computer Science and Engineering, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan, Bhubaneswar, Odisha, India
Department of Computer Science and Engineering, Vignana Bharathi Institute of Technology (VBIT), Hyderabad, India
Elsevier.com visitor survey
We are always looking for ways to improve customer experience on Elsevier.com.
We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.
If you decide to participate, a new browser tab will open so you can complete the survey after you have completed your visit to this website.
Thanks in advance for your time.