TY  - BOOK
AU  - Masoodi,Faheem
AU  - Quasim,Mohammad
AU  - Bukhari,Syed
AU  - Dixit,Sarvottam
AU  - ʻĀlam,Shādāb
TI  - Applications of Machine Learning and Deep Learning on Biological Data
T2  - Advances in Computational Collective Intelligence Ser
SN  - 9781000833768
AV  - QH324.25 
U1  - 570.285 23/eng/20230314
PY  - 2023///
CY  - Milton
PB  - Auerbach Publishers, Incorporated
KW  - Bioinformatics
KW  - Machine learning
KW  - Deep learning (Machine learning)
KW  - Biology
KW  - Data processing
KW  - Artificial intelligence
KW  - Biological applications
KW  - COMPUTERS / Artificial Intelligence
KW  - bisacsh
KW  - COMPUTERS / Neural Networks
N1  - Description based upon print version of record; Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Contributors -- About the Authors -- 1. Deep Learning Approaches, Algorithms, and Applications in Bioinformatics -- 2. Role of Artificial Intelligence and Machine Learning in Schizophrenia-A Survey -- 3. Understanding Financial Impact of Machine and Deep Learning in Healthcare: An Analysis -- 4. Face Mask Detection Alert System for COVID Prevention Using Deep Learning -- 5. An XGBoost-Based Classification Method to Classify Breast Cancer -- 6. Prediction of Erythemato-Squamous Diseases Using Machine Learning; 7. Grouping of Mushroom 5.8s rRNA Sequences by Implementing Hierarchical Clustering Algorithm -- 8. Applications of Machine Learning and Deep Learning in Genomics and Proteomics -- 9. Artificial Intelligence: For Biological Data -- 10. Application of ML and DL on Biological Data -- 11. Deep Learning for Bioinformatics -- Index
N2  - This book provides readers a comprehensive understanding of the application of machine Learning and deep Learning in proteomics, genomics, microarrays, text mining and related fields. The key objective is to provide machine learning applications to biological science problems, focusing on problems related to bioinformatics
UR  - https://www.taylorfrancis.com/books/9781003328780
UR  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ER  -