Machine Learning in Biological Sciences: Updates and Future Prospects

✍ Scribed by Shyamasree Ghosh, Rathi Dasgupta

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 337
• Category: Library

✦ Synopsis

This book gives an overview of applications of Machine Learning (ML) in diverse fields of biological sciences, including healthcare, animal sciences, agriculture, and plant sciences. Machine learning has major applications in process modelling, computer vision, signal processing, speech recognition, and language understanding and processing and life, and health sciences. It is increasingly used in understanding DNA patterns and in precision medicine. This book is divided into eight major sections, each containing chapters that describe the application of ML in a certain field. The book begins by giving an introduction to ML and the various ML methods. It then covers interesting and timely aspects such as applications in genetics, cell biology, the study of plant-pathogen interactions, and animal behavior. The book discusses computational methods for toxicity prediction of environmental chemicals and drugs, which forms a major domain of research in the field of biology.

It is of relevance to post-graduate students and researchers interested in exploring the interdisciplinary areas of use of machine learning and deep learning in life sciences.

✦ Table of Contents

Preface
Acknowledgments
Contents
About the Authors
1: A Brief Overview of Applications of Machine Learning in Life Sciences
1.1 Introduction
1.2 Complexity of the Biological World and the Origin of Machine Learning: A Journey Through History
1.3 Artificial Intelligence, Machine Learning and Deep Learning
1.4 Languages Used in Machine Learning
1.5 A Linear Regression Model and Machine Learning Algorithm
1.5.1 Linear Assumption
1.5.2 Remove Noise
1.5.3 Remove Collinearity
1.5.4 Gaussian Distributions
1.5.5 Rescale Inputs
1.6 A Brief Overview of Applications of Machine Learning in Life Sciences
1.6.1 Studies in the Domain of Cell Biology
1.6.2 Application in Study of Toxicity
1.6.3 Developmental Biology
1.6.4 Biology of Health and Disease
1.6.5 Plant and Agricultural Sciences
1.6.6 Animal Behavior
1.6.7 Nanorobots
1.7 Discussions
References
2: Introduction to Artificial Intelligence (AI) Methods in Biology
2.1 Introduction
2.2 Artificial Intelligence in Medicine
2.3 AI in the Disease Biology of Infection
2.4 AI in Genomics and Diagnostics
2.5 Discussion
References
3: Machine Learning Methods
3.1 Introduction
3.2 Associations
3.2.1 Support
3.2.2 Confidence
3.2.3 Lift
3.3 Classification
3.3.1 Unsupervised Learning
3.3.2 Clustering
3.3.3 Supervised Learning
3.3.4 Linear Regression
3.3.5 Spearman´s Coefficient
3.4 Pearson´s Correlation Coefficient
3.5 Graphical Representation of a Linear Relationship
3.6 Classification Techniques
3.7 Logistic Regression
3.7.1 Linear Discriminant Analysis (LDA)
3.7.2 K-Nearest Neighbors (KNN)
3.7.3 Neural Networks (Fig. 3.6)
3.7.4 Support Vector Machine
3.7.5 Trees
3.7.6 Reinforcement Learning Method
3.8 Discussion
Reference
4: Introduction to the Machine Learning Models
4.1 Introduction
4.2 The Classification of Machine Learning Algorithms/Learning Algorithm
4.3 Machine Learning Models in the Science of Biology
4.4 Regression Algorithm
4.5 Regularized Regression Methods
4.6 Decision Trees
4.7 K-Means Clustering
4.8 Naïve Bayes Classifier
4.9 Discussion
References
5: Model Selection for Machine Learning
5.1 Introduction
5.2 Model Selection Process and Criteria for Model Selection
5.3 Model Selection Techniques
5.4 Resampling Methods
5.5 K-Fold Cross Validation
Further Reading
6: Multivariate Methods in Machine Learning in the Context of Biological Data
6.1 Introduction
6.2 Multivariate Analysis
6.3 Multivariate Data
6.4 Parameter Estimation
6.5 Estimation and Handling of Missing Values
6.6 Multivariate Normal Distribution
6.7 Multivariate Classification
6.8 Tuning Complexity
References
Further Reading
7: Dimensionality Reduction Methods in Machine Learning
7.1 Introduction
7.2 Dimensionality Reduction and Biological Data
7.3 Dimensionality Reduction Techniques
7.3.1 Feature Selection Methods
7.3.2 Feature Extraction Method
7.4 Discussion
References
Further Reading
8: Hidden Markov Method
8.1 Introduction: Markov Chain
8.2 The Hidden Markov Model (HMM)
8.3 Likelihood Computation and the Forward-Backward Procedure
8.4 The Forward-Backward Procedure
8.5 Discussion
References
Further Reading
9: Neural Network and Deep Learning
9.1 Introduction
9.2 Perceptron
9.3 Sigmoid Neurons
9.4 Structure of Neural Networks
9.5 Cost Function and Back Propagation
9.6 Discussion
Further Reading
10: Ethics in Machine Learning and Artificial Intelligence
10.1 Introduction
10.2 Moral Choice
10.3 Fairness and Concept of Consequentialism
10.4 Tackling Biases
10.5 Business
10.6 Discussion
11: Machine Learning and Life Sciences
11.1 Introduction
11.2 Machine Learning and Life Sciences
11.2.1 Applications in Health
11.2.1.1 Applications in Drug Design
11.2.1.2 Epigenetics and Disease Biology
11.2.1.3 Cancer Biology
11.2.1.4 Infectious Disease Biology
11.2.1.5 Immunological Disorders
Autoimmune Disorders
Hypersensitivity Disorders
11.2.2 Agriculture
11.2.3 Animal Sciences
11.3 Discussions
References
12: Machine Learning and Neglected Tropical Diseases
12.1 Introduction
12.2 Neglected Tropical Diseases
12.3 Machine Learning in Study of Neglected Tropical Diseases
12.4 Discussion
References
13: Machine Learning in Cardiovascular Disorders
13.1 Introduction
13.2 Machine Learning and CVD
13.3 Discussions
References
14: Machine Learning and Diabetes
14.1 Introduction
14.2 Machine Learning and Diabetes
14.3 Discussion
References
15: Machine Learning and Epilepsy
15.1 Introduction
15.2 Epilepsy and Study by Conventional Methods
15.2.1 Cause, Diagnosis and Prognosis
15.2.2 Conventional Treatment
15.2.3 Genetics
15.2.3.1 GABA Receptor Mutation
15.2.3.2 NMDA (N-Methyl-d-Aspartate) Receptor (NMDAR) Mutation
15.2.3.3 Potassium Channels
15.2.3.4 G-Protein Coupled Receptors (GCPRs)
15.2.3.5 Mammalian Target of Rapamycin Pathway (mTOR) Pathway
15.2.3.6 Chromatin Remodeling
15.2.4 Autoimmune Epilepsies
15.2.5 Comorbidity
15.3 Machine Learning and Epilepsy
15.4 Discussions
References
16: The IT Industry and Applications in Biology
16.1 I. Introduction
16.2 Google
16.2.1 Artificial Intelligence Applications
16.2.2 In Search of Biomedical Literature
16.2.3 Google Trends and Disease Forecast
16.2.3.1 Trends in Disease Biology
Disease Surveillance
16.2.3.2 Prediction of Disease Incidence and Trend
Lyme Disease
Cellulitis
Disease Related to Pollution
Tick-Borne Encephalitis (TBE)
Vesicular Stomatitis (VS)
Measles
COVID-19
Zika Virus (ZIKV)
Autoimmune Disease
Cancer
16.3 Amazon
16.4 Microsoft
16.5 Facebook
16.6 Pytorch
16.7 Discussion
References
17: Applications and Software of Machine Learning and Artificial Intelligence (AI) in Medical Knowledge and Health
17.1 Introduction
17.2 Big Data and Applications in Public Health
17.3 Public Health Surveillance Using Tools of Machine Learning
17.4 Machine Learning and Big Data and Human Health Science
17.5 Machine Learning and Bigdata: Applications in Human Health
17.5.1 Study in Diabetes
17.5.2 Predicting Cancer
17.5.3 Interpreting Echocardiography (ECG)
17.5.4 Detection of Glaucoma Through Machine Learning Algorithm
17.6 Machine Learning and Health Policy
17.7 Novel Corona (COVID-19) Virus Infection and Machine Learning: A Recent Most Application
17.8 Big Data and Future Direction of Public Health Through Machine
17.9 Discussions
References
18: Cloud Computing Infrastructure in Healthcare Industry
18.1 Introduction
18.2 Cloud Transformation Models
18.3 Cloud Service Models
18.4 Cloud Computing and Healthcare
18.5 Challenges in Cloud Technology in Healthcare
18.6 Discussion
References
19: Artificial Intelligence Industry and the Domain of Life Sciences
19.1 Introduction
19.2 Artificial Intelligence Industry
19.2.1 Chatbots
19.2.2 Voice Conversational Agents
19.2.3 High Performance Computing (HPC)
19.3 Industry and Biological Sciences
19.4 Discussion
References
20: Toxicity: An Introduction
20.1 Introduction
20.2 Types of Toxicity
20.2.1 Acute Toxicity
20.2.2 Subchronic Toxicity
20.2.3 Chronic Toxicity
20.2.4 Carcinogenicity
20.2.5 Developmental Toxicity
20.2.6 Genetic Toxicity
20.3 Routes of Exposure of Toxicants
20.4 Drug Induced Toxicity
20.4.1 Pathophysiology
20.4.2 Molecular Basis
20.5 Discussion
References
21: Machine Learning (ML) and Toxicity Studies
21.1 Introduction
21.2 Machine Learning and Application in Detection of Toxicity
21.3 Discussion
References
22: Applications of Machine Learning in Study of Cell Biology
22.1 Introduction
22.2 Study of Cell Biology
22.3 Machine Learning in Study of Cell Biology
22.3.1 Education and Research
22.3.2 Cell Structure and Networks
22.3.3 Cell Health
22.3.4 Stem Cells
22.3.5 Cell Segmentation and Trafficking
22.3.6 Cancer Cells
22.4 Discussion
References
23: Genomics and Machine Learning
23.1 Introduction
23.2 Machine Learning Applications in Genomics
23.3 k-Nearest-Neighbors (kNN) and Application in Genomic Based Studies
23.4 Discussions
References
24: Cell Fate Analysis and Machine Learning
24.1 Introduction
24.2 Cell Fate and Fate Maps
24.3 Cell Fate and Machine Learning
24.4 Discussion
References
25: Study of Biomarker and Machine Learning
25.1 Introduction
25.2 Biomarkers and their Importance in Disease Biology
25.3 Machine Learning Applications in Biomarkers
25.4 Discussions
References
26: Animal Behavior: An Introduction
26.1 Introduction
26.2 Diversity of Animal Behavior and the Factors Involved
26.2.1 Movement/Locomotion
26.2.2 Courtship
26.2.3 Chemical Mimicry
26.2.4 Social Behavior
26.2.5 Bat flight
26.2.6 Ethology
26.2.7 Hormones
26.2.8 Behavioral Schedules and Biological Clock
26.2.9 Bird Song
26.2.10 The Behavior of Worker Bee
26.2.11 Predator Animal Behavior
26.3 Traditional Ways of Study of Animal Behavior
26.4 Computational Studies of Animal Behavior
26.5 Discussions
References
27: Study of Animal Behavior and Machine Learning
27.1 Introduction
27.2 Machine Learning and Study of Animal Behavior
27.2.1 Unsupervised Machine Learning
27.2.2 Supervised Learning
27.2.3 Semi-Supervised Learning
27.3 Recording Behavior
27.4 Tracking Animal Position and Pose
27.5 Automation and Behavioral Analysis
27.6 Automatic Classification of Behavior
27.7 Behavioral Analysis from Audio Based Methods
27.8 Discussion
References
28: Machine Learning and Precision Farming
28.1 Introduction
28.1.1 Precision Agriculture
28.1.2 Ecological Monitoring
28.1.3 Precision Mariculture and Aquaculture
28.1.4 Precision Livestock Farming (PLF)
28.2 IOT Platforms and Precision Agriculture
28.3 The Big Data and Livestock Application
28.4 Precision Animal Breeding (PAB)
28.5 Precision Livestock Management
28.6 Discussion
References
29: Machine Learning in the Study of Animal Health and Veterinary Sciences
29.1 Introduction
29.2 Machine Learning Applications in Disease
29.2.1 Infection
29.2.1.1 Cattle
29.2.1.2 Canines
29.2.1.3 Felines
29.2.1.4 Birds
29.2.2 Health
29.2.2.1 Cattle
29.2.2.2 Felines
29.2.2.3 Zebrafish
29.2.2.4 Giardia Duodenalis
29.2.2.5 Sea Lions
29.2.2.6 Sharks
29.2.2.7 Horses
29.2.2.8 Canines
29.2.3 Disease Surveillance and Health
29.3 Discussions
References
30: Machine Learning and Animal Reservoirs
30.1 Introduction: Zoonotic Infection
30.2 Animals as Reserviors
30.3 Machine Learning and Animal Reservoirs
30.3.1 Influenza
30.3.2 Nipah
30.3.3 Rift Valley Fever (RVF)
30.3.4 Studies on Hosts and Reservoirs
30.4 Discussion
References
31: Challenging Problems in Plant Biology
31.1 Introduction
31.2 Some of the Interesting Areas and Challenges in Study in Plant Sciences
31.3 Discussion
References
32: Machine Learning and Plant Sciences
32.1 Introduction
32.2 Application of Machine Learning
32.2.1 Plant Immunity and Infection
32.2.2 Plant Genomics
32.2.3 Stress Biology
32.2.4 Plant Growth, Genome Selection, and Phenotyping
32.2.5 Airborne Pollen Release
32.3 Discussion
References
33: Machine Learning in Understanding of Plant-Pathogen Interactions
33.1 Introduction
33.2 Plant Disease
33.3 A Brief History and the Advancement in the Study in Plant Pathology
33.4 Machine Learning and Plant Pathogen Interaction
33.5 STAR RNA Sequencing
33.6 Discussions
References
34: Machine Learning in Plant Disease Research
34.1 Introduction
34.2 Plant Diseases
34.3 Detection of Plant Diseases Using Deep Learning Algorithm
34.3.1 An Approach in Creating a Deep Learning Code to Identify Plant Disease
34.4 Discussion
References
35: Biorobots
35.1 Introduction
35.1.1 Biorobotics
35.1.2 Biomechanics
35.1.3 Molecular Robots
35.2 Biorobots and Biological Application
35.2.1 Application in Biology
35.2.2 Application in Detection Systems and in Detection of Infectious Disorders
35.2.3 Study of Immune Responses to Infection
35.2.4 Brain and Neurological Function
35.2.5 In the Study of Animal Locomotion
35.2.6 Other Major Biological Applications
35.3 Molecular Robotics and its Application in Science and Technology
35.4 Machine Learning and Biorobots
35.5 Discussions
References
36: The Challenges to Application of Machine Learning in Biological Sciences
36.1 Introduction
36.2 Machine Learning: Challenges in Recent Times
36.2.1 Search for Better Memory Networks
36.2.2 Improved Natural Language Processing (NLP)
36.2.3 Involvement of Attention to Integrate Data
36.2.4 Understand Deep Nets Training
36.2.5 Approaches of One-Shot Learning
36.2.6 Deep Reinforcement Learning and Control of Robots
36.2.7 Semantic Segmentation
36.2.8 Video Training Data
36.2.9 Detection of Object
36.2.10 Application of AI to Big Data
36.3 Challenges Faced by Machine Learning in Biology
36.4 Discussions
References
37: The Future of Machine Learning
37.1 Introduction
37.2 Machine Learning and the Future
37.3 Discussion