Applications of Machine Learning and Deep Learning on Biological Data

Faheem Masoodi, Mohammad Quasim, Syed Bukhari , Sarvottam Dixit

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English

Auerbach
07 October 2024

Computing: general; Mathematical theory of computation; Artificial intelligence; Machine learning; Neural networks & fuzzy systems

Series: Advances in Computational Collective Intelligence

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 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 to biological data

Edited by: Faheem Masoodi, Mohammad Quasim, Syed Bukhari, Sarvottam Dixit, Shadab Alam
Imprint: Auerbach
Country of Publication: United Kingdom
Dimensions: Height: 234mm, Width: 156mm,
ISBN: 9781032358260
ISBN 10: 1032358262
Series: Advances in Computational Collective Intelligence
Pages: 200
Publication Date: 07 October 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Forthcoming

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.