Data Science for Genomics

314 Pages·2022·13.627 MB·English

by  Amit Kumar Tyagi, Ajith Abraham

Data Science for Genomics
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Data Science for Genomics
Edited by
Amit Kumar Tyagi
Department of Fashion Technology,
National Institute of Fashion Technology,
New Delhi, India
Ajith Abraham
Director,
Machine Intelligence Research Labs,
United States
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Contents
3. Machine learning in genomics:
Contributors xi
Preface xiii identification and modeling of
Acknowledgment xv anticancer peptides
1. Genomics and neural networks in Girish Kumar Adari, Maheswari Raja
electrical load forecasting with and P. Vijaya
computational intelligence
1. Introduction 25
Prasannavenkatesan Theerthagiri 2. Materials and methods 26
2.1 Google Colaboratory 26
1. Introduction 1
2.2 Data sets 26
2. Methodology 2
2.3 Pfeature package 26
2.1 RNN 2
2.4 Feature extraction functions 28
2.2 Long short-term memory 4
2.5 Machine learning implementation 29
3. Experiment evaluation 6
2.6 Conclusion 66
3.1 Testing methods effectiveness for References 67
PGVCL data 6
3.2 Testing methods effectiveness for 4. Genetic factor analysis for an early
NYISO data 8 diagnosis of autism through
4. Conclusion 9 machine learning
References 9
A. Chaitanya Kumar, J. Andrew John,
2. Application of ensemble Maheswari Raja and P. Vijaya
learninge based classifiers for
1. Introduction 69
genetic expression data
2. Review of literature 70
classification
3. Methodology 71
Saumendra Kumar Mohapatra, Abhishek Das 3.1 Using KNIME software 71
and Mihir Narayan Mohanty 3.2 Data set analysis through ML
algorithms 72
1. Introduction 11
3.3 Naive Bayes learner 72
2. Ensemble learningebased classifiers for
3.4 Fuzzy rule learner 73
genetic data classification 12
3.5 Decision tree learner 73
2.1 Bagging 13
3.6 RProp MLP learner 74
2.2 Boosting 13
3.7 Random forest learner 74
2.3 Stacking 13
3.8 SVM learner 75
3. Stacked ensemble classifier for leukemia
3.9 K-nearest neighbors learner 75
classification 14
3.10 Gradient boosted trees learner 76
3.1 Proposed classification model 14
3.11 K-means clustering 76
3.2 Deep-stacked ensemble classifier 14 4. Results 77
3.3 SVM meta classifier 15
4.1 Graphs obtained 77
3.4 Gradient boosting meta classifier 16
4.2 Inference 82
4. Results and discussion 17 5. Conclusion 82
5. Conclusion 21 Appendix 83
References 21 References 83
v
vi Contents
5. Artificial intelligence and data 2. Materials and method 109
science in pharmacogenomics-
2.1 Target protein preparation 110
based drug discovery: future of
2.2 Ligand preparation 110
medicines
2.3 Binding site/catalytic site prediction 110
2.4 Structure minimization 110
Vikas Jhawat, Sumeet Gupta, Monika Gulia
2.5 Grid generation 110
and Anroop Nair
2.6 Molecular docking of proteineligand
1. Introduction 85 using Autodock software 111
2. Artificial intelligence 86 2.7 Hydrogen bond interaction using
3. Artificial intelligence in drug research 88 LigPlot software 111
4. Drug discovery 88 2.8 Screening of compounds for drug
4.1 Drug screening 88 likeness 111
4.2 Drug designing 89 2.9 Screening of compounds for
4.3 Drug repurposing 89 toxicity 111
4.4 ADME prediction 89 3. Results and discussion 111
4.5 Dosage form and delivery system 89 4. Conclusion 125
4.6 PK/PD correlation 89 Declaration 125
5. Pharmacogenomics 90 Nomenclature 125
6. Pharmacogenomics and AI 92 Acknowledgments 126
7. Integration of pharmacogenomics and AI 92 References 126
8. Pharmacogenomic-based clinical
8. Toward automated machine
evaluation and AI 95
learning for genomics: evaluation
9. Discussion 95
and comparison of state-of-the-art
10. Conclusion 95
AutoML approaches
Abbreviations 96
References 96 Akbar Ali Khan, Prakriti Dwivedi, Sareeta
6. Recent challenges, opportunities, Mugde, S.A. Sajidha, Garima Sharma and
and issues in various data analytics Gulshan Soni
1. Into the world of genomics 129
Kannadhasan Suriyan and Nagarajan
2. Need and purpose of analytics in
Ramalingam
genomics 129
1. Introduction 99 3. Literature review 129
2. Big data 99 4. Research design 131
3. Data analytics 100 4.1 Research design methodology 131
4. Challenges in data analytics 101 4.2 AutoML tools used: PyCaret and
5. Various sectors in data analytics 102 AutoViML 133
6. Conclusion 105 5. AutoML 133
References 105 5.1 Why AutoML and why it should be
democratized 133
7. In silico application of data science,
5.2 Architectural design of AutoML 134
genomics, and bioinformatics in
5.3 Democratization of AutoML and
screening drug candidates against
beyond 134
COVID-19
6. Research outcome 135
Rene Barbie Browne, Jai Narain Vishwakarma, 6.1 Exploratory data analysis 135
Vedant Vikrom Borah, Raj Kumar Pegu and 6.2 Analysis using PyCaret 137
Jayanti Datta Roy 6.3 Analysis using AutoViML 140
6.4 Model comparison: PyCaret and
1. Introduction 107
AutoViML 143
1.1 A brief overview of SARS-CoV-2 108
7. Business implications 148
1.2 Compounds reported with antiviral
8. Conclusion 151
activities 109
References 151
1.3 Herb extracts with antiviral property in
Further reading 152
India 109
Contents vii
9. Effective dimensionality reduction
2.3 PIN diodes uses and advantages 171
model with machine learning
2.4 PIN photodiode applications 171
classification for microarray gene 3. Results and simulations 171
expression data
3.1 Effect of light on a PIN photodiode 171
3.2 Procedure to design and observe the
Yakub Kayode Saheed
effect of light 171
1. Introduction 153 3.3 VeI characteristic of a PIN photodiode 174
2. Related work 154 4. Conclusion 176
3. Materials and methods 155 Appendix (Silvaco Code) 176
3.1 Feature selection 155 Effect of light on the characteristics of pin
3.2 Principal component diode code 176
analysis 155 Effect of light on the characteristics of SDD
3.3 Logistic regression 157 diode code 177
3.4 Extremely randomized trees References 177
classifier 157
11. One step to enhancement the per-
3.5 Ridge classifier 157
formance of XGBoost through GSK
3.6 Adaboost 157
for prediction ethanol, ethylene,
3.7 Linear discriminant analysis 157
ammonia, acetaldehyde, acetone,
3.8 Random forest 157
and toluene
3.9 Gradient boosting machine 157
3.10 K-nearest neighbors 158 Samaher Al-Janabi, Hadeer Majed and Saif
3.11 Data set used for analysis 158 Mahmood
4. Results and discussion 158
1. Introduction 179
4.1 Experimental analysis on 10-fold
2. Related work 180
cross-validation 158
3. Main tools 181
4.2 Experimental analysis on
3.1 Internet of Things (IoTs) 181
eightfold cross-validation 159
3.2 Optimization techniques 181
4.3 Comparison of our findingswith
3.3 Prediction techniques 184
some earlier studies 160
5. Conclusion and future work 160 4. Result of implementation 194
References 161 4.1 Description of dataset 194
4.2 Result of preprocessing 194
10. Analysis the structural, electronic
4.3 Checking missing values 195
and effect of light on PIN 5. Conclusions 201
photodiode achievement through References 202
SILVACO software: a case study
12. A predictive model for classifying
Samaher Al-Janabi, Ihab Al-Janabi and Noora colorectal cancer using principal
Al-Janabi component analysis
1. Introduction 165 Micheal Olaolu Arowolo, Happiness Eric
1.1 Photodiode 165 Aigbogun, Precious Eniola Michael, Marion
1.2 Effect of light on the IeV characteristics
Olubunmi Adebiyi and Amit Kumar Tyagi
of photodiodes 165
1.3 IeV characteristics of a photodiode 167 1. Introduction 205
2. Related works 206
1.4 Types of photodiodes 168
3. Methodology 207
1.5 Modes of operation of a photodiode 168
1.6 Effect of temperature on IeV char of 3.1 Experimental dataset 208
3.2 Dimensionality reduction tool 208
photodiodes 168
3.3 Classification 209
1.7 Signal-to-noise ratio in a photodiode 169
3.4 Research tool 210
1.8 Responsivity of a photodiode 169
3.5 Performance evaluation metrics 210
1.9 Responsivity versus wavelength 169
2. PIN photodiode 170 4. Results and discussions 210
5. Conclusion 215
2.1 Operation of PIN photodiode 170
References 215
2.2 Key PIN diode characteristics 170
viii Contents
13. Genomic data science systems 15. Genomic privacy: performance
of Prediction and prevention of analysis, open issues, and future
pneumonia from chest X-ray images research directions
using a two-channel
dual-stream convolutional neural M. Shamila, K. Vinuthna and
network Amit Kumar Tyagi
1. Introduction 249
Olalekan J. Awujoola, Francisca N. Ogwue-
1.1 Genome data 249
leka, Philip O. Odion, Abidemi E. Awujoola
1.2 Genomic dataversus other types of
and Olayinka R. Adelegan
data 250
1. Introduction 217 2. Related work 251
2. Review of literature 218 3. Motivation 252
2.1 Introduction 218 4. Importance of genomic data/privacyin
2.2 Convolutional neural networks (CNNs) 219 real life 252
3. Materials and methods 220 5. Techniques for protecting genetic
3.1 Dataset 220 privacy 254
3.2 The proposed architecture: 5.1 Controlled access 254
two-channel dual-stream CNN 5.2 Differential privacy preservation 254
(TCDSCNN) model 220 5.3 Cryptographic solutions 254
3.3 Performance matrix for classification 223 5.4 Otherapproaches 255
4. Result and discussion 224 5.5 Some useful suggestions for protecting
4.1 Visualizing the intermediate layer genomic data 255
output of CNN 224 6. Genomic privacy: use case 255
4.2 Model feature map 224 7. Challenges in protecting genomic data 256
4.3 Model accuracy 224 8. Opportunities in genomic data privacy 258
5. Conclusion and future work 224 9. Arguments about genetic privacy with
References 227 several other privacy areas 259
10. Conclusion with future scope 260
14. Predictive analytics of genetic
Appendix A 260
variation in the COVID-19 genome
Authors’ contributions 262
sequence: a data science
Acknowledgments 262
perspective
References 262
V. Kakulapati, S. Mahender Reddy, Sri Sai
16. Automated and intelligent systems
Deepthi Bhrugubanda and Sriman Naini
for next-generation-based smart
1. Introduction 229 applications
1.1 Objectives 231
2. Related work 231 H.R. Deekshetha and Amit Kumar Tyagi
3. The COVID-19 genomic sequence 232 1. Introduction 265
3.1 The relevance of genome sequences 2. Background work 265
to disease analyses 233 3. Intelligent systems for smart
3.2 Utilization of COVID-19 genome applications 266
sequencing for processing 233 4. Automated systems for smart
4. Methodology 235 applications 266
4.1 Implementation analysis 240 5. Automated and intelligent systems for
Lung epithelial similarity 241 smart applications 266
5. Discussion 243 6. Machine learning and AI technologies for
6. Conclusion 243 smart applications 267
7. Future outlook 245 7. Analytics for advancements 267
References 245 8. Cloud strategies: hybrid, containerization,
Further reading 247 serverless, microservices 267
Contents ix
9. Edge intelligence 268 20. Conclusion and future scope 274
10. Data governance and quality for smart Acknowledgments 274
applications 268 References 274
11. Digital Ops including DataOps, AIOps, Further reading 276
and CloudSecOps 269
17. Machine learning applications for
12. AI in healthcaredfrom data to
COVID-19: a state-of-the-art review
intelligence 270
13. Big data analytics in IoT-based smart
Firuz Kamalov, Aswani Kumar Cherukuri, Hana
applications 271 Sulieman, Fadi Thabtah and Akbar Hossain
14. Big data applications in a smart city 271
15. Big data intelligence for cyber-physical 1. Introduction 277
systems 272 2. Forecasting 278
16. Big data science solutions for real-life 3. Medical diagnostics 280
applications 272 4. Drug development 283
17. Big data analytics for cybersecurity and 5. Contact tracing 284
privacy 272 6. Conclusion 286
18. Data analytics for privacy-by-design in References 287
smart health 273
19. Case studies and innovative applications 273
19.1 Innovative bioceramics 273 Index 291