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.
The automated learning of machines characterizes machine learning (ML). It focuses on making data-driven predictions using programmed algorithms. ML has several applications, including bioinformatics, which is a discipline of study and practice that deals with applying computational derivations to obtain biological data. It involves the collection, retrieval, storage, manipulation, and modeling of data for analysis or prediction made using customized software. Previously, comprehensive programming of bioinformatical algorithms was an extremely laborious task for such applications as predicting protein structures. Now, algorithms using ML and deep learning (DL) have increased the speed and efficacy of programming such algorithms.
Applications of Machine Learning and Deep Learning on Biological Data is an examination of applying ML and DL to such areas as proteomics, genomics, microarrays, text mining, and systems biology. The key objective is to cover ML applications to biological science problems, focusing on problems related to bioinformatics. The book looks at cutting-edge research topics and methodologies in ML applied to the rapidly advancing discipline of bioinformatics.
ML and DL applied to biological and neuroimaging data can open new frontiers for biomedical engineering, such as refining the understanding of complex diseases, including cancer and neurodegenerative and psychiatric disorders. Advances in this field could eventually lead to the development of precision medicine and automated diagnostic tools capable of tailoring medical treatments to individual lifestyles, variability, and the environment.
Highlights include:
- Artificial Intelligence in treating and diagnosing schizophrenia
- An analysis of MLs and DLs financial effect on healthcare
- An XGBoost-based classification method for breast cancer classification
- Using ML to predict squamous diseases
- ML and DL applications in genomics and proteomics
- Applying ML and DL to biological data
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
Learning 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.
Dr. Faheem Syeed Masoodi is Assistant Professor in the Department of Computer Science, University of Kashmir, India.
Dr. Mohammad Tabrez Quasim is Assistant Professor at University of Bisha, Saudi Arabia.
Dr. Syed Nisar Hussain Bukhari is a Scientist-C at the National Institute of Electronics and Information Technology (NIELIT) J&K, Srinagar, India.
Prof. Dr. Sarvottam Dixit holds the post of Advisor to The Chairperson, Mewar University, Chittorgarh, India.
Dr. Shadab Alam is currently Assistant Professor in the Department of Computer Science, Jazan University, Jazan, Saudi Arabia.
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