Applications of Machine Learning and Deep Learning on Biological Data
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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. Comprehensively programming bioinformatical algorithms was previously 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 environment.
- Artificial Intelligence in treating and diagnosing schizophrenia
- An analysis of ML’s and DL’s 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 on biological data
Table of Contents
1. Deep Learning Approaches, Algorithms, and Applications in Bioinformatics
Saahira Banu Ahamed, Shermin Shamsudheen, and Awatef Salem Balobaid
2. Role of Artificial Intelligence and Machine Learning in Schizophrenia: A Survey
Bhawana Paliwal and Khandakar Faridar Rahman.
3. Understanding Financial Impact of Machine Learning and Deep Learning in Healthcare: An Analysis
Khurshid Ali Ganai and Bilal Ahmad Pandow
4. Face Mask Detection Alert System for Covid Prevention Using Deep Learning
Parth Agarwal , Dhruv Rastogi and Aman Sharma
5. An XGBoost-based Classification Method to Classify Breast Cancer
Kishwar Sadaf, Jabeen Sultana, and Nazia Ahmad
6. Prediction of Squamous Diseases Using Machine Learning
Syed Nisar Hussain Bukhari, Faheem Masoodi, Muneer Ahmad Dar, Nisar IqbalWani, Adfar Sajad and Gousiya Hussain
7. Grouping of Mushroom 5.8s rRNA Sequences by Implementing Hierarchical Clustering Algorithm
P. Sudhasini and B. Ashadevi
8. Applications of Machine Learning and Deep Learning in Genomics and Proteomics
Qurat-ul-ain and Uzma Hameed
9. Artificial Intelligence for Biological Data
Ifra Altaf, Muheet Ahmed Butt, Majid Zaman
10. Application of Machine Learning and Deep Learning on Biological Data
Tawseef Ahmed Teli, Faheem Syed Masoodi and Zubair Masoodi
11. Deep Learning for Bioinformatics
Tawseef Ahmed Teli and Rameez Yousuf
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.