Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome

Erscheinungsjahr 2018
ISBN: 978-0-12-816356-6
Verlag: Elsevier Science

Buch, Englisch, 200 Seiten, Format (B × H): 231 mm x 216 mm, Gewicht: 290 g

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.

Zielgruppe

<p>Bioinformaticians, Cancer Researchers, researchers interested in applying Systems Biology approaches to their studies</p><b> <p></b>Geneticists, Bioengineers, researchers interested in Machine learning, Data Mining, Bioinformatics</p>

Autoren/Hrsg.

Mishra, Shruti

Mishra, Debahuti

Satapathy, Sandeep Kumar

Fachgebiete

• Naturwissenschaften Biowissenschaften Biowissenschaften DNA und Transgene Organismen
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Klinische und Innere Medizin Onkologie, Krebsforschung
• Naturwissenschaften Biowissenschaften Biowissenschaften Genetik und Genomik (nichtmedizinisch)

Inhaltsverzeichnis

1. Literature Review2. SVM-BT-RFE: An Improved Gene Selection Framework Using Bayesian T-Test Embedded in Support Vector Machine (Recursive Feature Elimination) Algorithm3. Enhanced Gene Ranking Approaches Using Modified Trace Ratio Algorithm for Gene Expression Data4. SNR-TR Gene Ranking Method: A Signal-to-Noise Ratio Based Gene Selection Algorithm Using Trace Ratio for Gene Expression Data5. Visualization of Interactive Gene Regulatory Network Using Gene Selection Techniques from Expression Data6. Conclusion and Future Work

Über Autor(innen)

Satapathy, Sandeep Kumar
Dr. Sandeep Kumar Satapathy is currently working as an Associate Professor in the Department of Computer Science & Engineering and is also the Head of the Department of Information Technology at Vignana Bharathi Institute of Technology. Dr. Satapathy did his doctorate in the field of Data Mining & Machine Learning, and his thesis included a detailed classification of brain EEG signals using machine learning techniques. He has been member to various academic committees within the institution. Also, he has been an active reviewer in various peer reviewed journals and presented papers in prestigious conferences. He has also reviewed many research articles and books for Elsevier for possible publication. Prof. Satapathy is highly engrossed into the area of deep learning and image processing. He has many research publications to his credit, that is more than 25 research articles, book chapters and has guided more than 10 master thesis. Dr. Satapathy has also authored a book entitled Frequent Pattern Discovery from Gene Expression Data: An Experimental Approach. He is currently member of many professional organizations and societies. His research interest includes Bioinformatics and computational approaches to biomedical field.

Mishra, Shruti
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.