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Machine Learning in Biological Sciences

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Cover of 'Machine Learning in Biological Sciences'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 A Brief Overview of Applications of Machine Learning in Life Sciences
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    Chapter 2 Introduction to Artificial Intelligence (AI) Methods in Biology
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    Chapter 3 Machine Learning Methods
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    Chapter 4 Introduction to the Machine Learning Models
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    Chapter 5 Model Selection for Machine Learning
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    Chapter 6 Multivariate Methods in Machine Learning in the Context of Biological Data
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    Chapter 7 Dimensionality Reduction Methods in Machine Learning
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    Chapter 8 Hidden Markov Method
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    Chapter 9 Neural Network and Deep Learning
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    Chapter 10 Ethics in Machine Learning and Artificial Intelligence
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    Chapter 11 Machine Learning and Life Sciences
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    Chapter 12 Machine Learning and Neglected Tropical Diseases
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    Chapter 13 Machine Learning in Cardiovascular Disorders
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    Chapter 14 Machine Learning and Diabetes
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    Chapter 15 Machine Learning and Epilepsy
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    Chapter 16 The IT Industry and Applications in Biology
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    Chapter 17 Applications and Software of Machine Learning and Artificial Intelligence (AI) in Medical Knowledge and Health
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    Chapter 18 Cloud Computing Infrastructure in Healthcare Industry
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    Chapter 19 Artificial Intelligence Industry and the Domain of Life Sciences
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    Chapter 20 Toxicity: An Introduction
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    Chapter 21 Machine Learning (ML) and Toxicity Studies
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    Chapter 22 Applications of Machine Learning in Study of Cell Biology
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    Chapter 23 Genomics and Machine Learning
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    Chapter 24 Cell Fate Analysis and Machine Learning
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    Chapter 25 Study of Biomarker and Machine Learning
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    Chapter 26 Animal Behavior: An Introduction
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    Chapter 27 Study of Animal Behavior and Machine Learning
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    Chapter 28 Machine Learning and Precision Farming
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    Chapter 29 Machine Learning in the Study of Animal Health and Veterinary Sciences
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    Chapter 30 Machine Learning and Animal Reservoirs
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    Chapter 31 Challenging Problems in Plant Biology
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    Chapter 32 Machine Learning and Plant Sciences
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    Chapter 33 Machine Learning in Understanding of Plant–Pathogen Interactions
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    Chapter 34 Machine Learning in Plant Disease Research
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    Chapter 35 Biorobots
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    Chapter 36 The Challenges to Application of Machine Learning in Biological Sciences
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    Chapter 37 The Future of Machine Learning
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Title
Machine Learning in Biological Sciences
Published by
Springer Nature Singapore, February 2022
DOI 10.1007/978-981-16-8881-2
ISBNs
978-9-81-168880-5, 978-9-81-168881-2
Authors

Ghosh, Shyamasree, Dasgupta, Rathi

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 4 15%
Student > Master 3 12%
Student > Ph. D. Student 1 4%
Other 1 4%
Unspecified 1 4%
Other 0 0%
Unknown 16 62%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 12%
Agricultural and Biological Sciences 2 8%
Unspecified 1 4%
Nursing and Health Professions 1 4%
Medicine and Dentistry 1 4%
Other 2 8%
Unknown 16 62%