Deep Learning Applications in Translational Bioinformatics

Herausgeber: Barh, Debmalya; Ahmad, Naeem; Raza, Khalid; Singh, Deepak

• Broschiertes Buch

Produktbeschreibung

Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, and various applications of deep learning in translational bioinformatics, including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics foster future research and development.

Produktdetails

• Verlag: Elsevier Science Publishing Co Inc
• Seitenzahl: 298
• Erscheinungstermin: 7. März 2024
• Abmessung: 190mm x 236mm x 18mm
• Gewicht: 630g
• ISBN-13: 9780443222993
• ISBN-10: 0443222991
• Artikelnr.: 68743161

Inhaltsangabe

1. Deep Learning Ensembles in Translational Bioinformatics
2. Recursive Feature Elimination and Multi Support Vector Machine in
Healthcare Analytics
3. Sensors-enabled Biomedical Decision Support System using Deep Learning
and Fuzzy Logic
4. Prediction of Alzheimer's Disease Using Densely Convolutional Neural
Network
5. Brain Tumor Detection from MRI Images using Shallow CNN
6. Pandemic Disease Detection Using Diffusional Convolutional Neural
Network
7. Multiview Technique and Shallow CNN with Dropouts to Predict Antiviral
Peptides
8. Deep Learning Methods for Protein Classification
9. Biosensors-based Identification of Antibiotic Resistance in Bacteria
10. Deep Learning for Fervent Gene Expression Analysis
11. Machine Learning-enforced Bioinformatics Approaches for Drug Discovery
and Development
12. Role of Deep Learning in Predicting Drug Formulations and Delivery
Systems
13. Deep Learning in Computer-aided Drug Design: A Case Study
14. Protein Structure Prediction with RNN and CNN: A Case Study
15. Generative Adversarial Networks in Protein and Ligand Structure
Generation: A Case Study
16. Artificial Neural Networks for Prediction of Psychological Threats: A
Case Study