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Applications of Machine Learning and Deep Learning on Biological Data
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Applications of Machine Learning and Deep Learning on Biological Data

Book Details

Format Paperback / Softback
ISBN-10 1032358262
ISBN-13 9781032358260
Publisher Taylor & Francis Ltd
Imprint Auerbach
Country of Manufacture GB
Country of Publication GB
Publication Date Oct 7th, 2024
Print length 200 Pages
Weight 453 grams
Ksh 8,650.00
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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 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

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