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E-raamat: Leveraging Biomedical and Healthcare Data: Semantics, Analytics and Knowledge

Edited by (Assistant Professor, Department Psychiatry, College of Medicine, University of Florida, USA), Edited by , Edited by , Edited by (Associate Professor, Psychiatry and Neuroscience, College of Medicine, University of Florida, USA and Executive Director, Center for Neuropr)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 23-Nov-2018
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128095614
  • Formaat - EPUB+DRM
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 23-Nov-2018
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128095614

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Bioinformatics and Biomedical Data Sciences: Semantics, Analytics and Knowledge provides an overview of the approaches used in semantic systems biology, introduces novel areas of its application and describes step-wise protocols for transforming heterogeneous data into useful knowledge that can influence healthcare and biomedical research. Given the astronomical increase in the number of published reports, papers, and datasets over the last few decades, the ability to curate this data has become a new field of biomedical and healthcare research. However, this kind of data cannot be handled by the same approaches traditionally used by biomedical scientists and epidemiologist, and it requires high-throughput approaches of semantic and text analysis to curate, understand and disseminate the knowledge hidden in this big data.

This book discusses big data text-based mining in order to understand molecular architecture of diseases and to guide health care decision. Additionally, it presents actual research examples to be used as a practical tutorial.

The book is a valuable resource for bioinformaticians and members of several areas of biomedical field who are interested in understanding more about how to process and apply great amount of data to improve their research.

  • Includes at each section resource pages containing a list of available curated raw and processed data that can be used by researchers in the field
  • Provides demonstrative and relevant examples that serve as a general tutorial
  • Presents a list of algorithm names and computational tools available for basic and clinical researchers

Muu info

An overview of the approaches used in semantic systems biology with step-wise protocols for transforming heterogeneous data into useful knowledge
Contributors xi
Foreword I xv
Foreword II xvii
Preface xxi
Acknowledgments xxiii
1 Comprehensive Workflow for Integrative Transcriptomics Meta-analysis
1(16)
Ali Nehme
Frederic Mazurier
Kazem Zibara
1 Introduction
1(1)
2 Data preparation
1(9)
3 Crossplatform integration
10(2)
4 Conclusion
12(1)
Acknowledgment
13(1)
References
13(4)
2 Proteomics and Protein Interaction in Molecular Cell Signaling Pathways
17(18)
Hassan Pezeshgi Modarres
Mohammad R.K. Mofrad
1 Introduction
17(1)
2 Experimental techniques
17(2)
3 Computational methods
19(12)
4 Conclusion
31(1)
References
31(4)
3 Understanding Specialized Ribosomal Protein Functions and Associated Ribosomopathies by Navigating Across Sequence, Literature, and Phenotype Information Resources
35(18)
K.A. Kyritsis
L. Angelis
Christos Ouzounis
Ioannis Vizirianakis
1 Introduction
35(1)
2 RPs and diseases
36(2)
3 Specialized functions of RPs
38(2)
4 Exploring RP roles in health and disease by navigating bioinformatics resources
40(7)
5 Conclusions and future directions
47(6)
References
47(6)
4 Big Data, Artificial Intelligence, and Machine Learning in Neurotrauma
53(24)
Denes V. Agoston
1 Introduction
53(1)
2 Big data: Characteristics, definitions, and examples
53(3)
3 Traumatic brain injury (TBI)
56(1)
4 Big data in TBI
57(3)
5 Machine learning
60(1)
6 Artificial intelligence
60(1)
7 Text mining
61(1)
8 Examples of using BD approaches in TBI research
62(1)
9 Imaging
62(2)
10 Biochemical markers
64(2)
11 Legacy data
66(2)
12 Future of big data in TBI
68(9)
References
69(8)
5 Artificial Intelligence Integration for Neurodegenerative Disorders
77(14)
Rajat Vashistha
Dinesh Yadav
Deepak Chhabra
Pratyoosh Shukla
1 Introduction
77(3)
2 Wearables and ML-based therapeutics
80(1)
3 Neurodegenerative therapeutics through Al
80(5)
4 Al-based clinical decision-making
85(1)
5 Limitations and future perspectives
86(5)
References
86(5)
6 Robust Detection of Epilepsy Using Weighted-Permutation Entropy: Methods and Analysis
91(16)
Bilal Fadlallah
Ali Fadlallah
Mahdi Razafsha
Nabil Karnib
Kevin Wang
Firas Kobeissy
1 Introduction
91(1)
2 Computational details
92(3)
3 Experiments and results
95(10)
4 Conclusion
105(1)
References
105(1)
Further reading
106(1)
7 Biological Knowledge Graph Construction, Search, and Navigation
107(14)
Chandana Tennakoon
Nazar Zaki
Hiba Arnaout
Shady Elbassuoni
Wassim El-Hajj
Alanoud Al Jaberi
1 Resource description format (RDF) knowledge graphs
107(3)
2 Our proposed approach to create, search, and visualize biological knowledge graphs
110(8)
3 Extending the knowledge graph using machine-learning techniques
118(1)
4 Conclusion
119(2)
References
119(2)
8 Healthcare Decision-Making Support Based on the Application of Big Data to Electronic Medical Records: A Knowledge Management Cycle
121(12)
Javier Carnicero
David Rojas
1 Introduction
121(1)
2 Knowledge management cycle in healthcare
121(6)
3 Key points for an EMR-based big data approach to clinical knowledge management
127(3)
4 Conclusions
130(1)
References
130(3)
9 Computational Modeling in Global Infectious Disease Epidemiology
133(10)
Ali Alawieh
Zahraa Sabra
Fadi A. Zaraket
1 Introduction
133(1)
2 Prediction of progression of bacterial resistance
134(3)
3 Poliovirus prediction
137(3)
4 Conclusions
140(1)
References
141(2)
10 Semiautomatic Annotator for Medical NLP Applications: About the Tool
143(8)
Mohamed Sabra
Ali Alawieh
1 Introduction
143(3)
2 The semiautomatic annotator
146(4)
References
150(1)
11 Intractome Curation and Analysis for Stroke and Spinal Cord Injury Using Semiautomatic Annotations
151(16)
Mohamed Sabra
Ali Alawieh
Fadi A. Zaraket
1 Gene and protein accession and mapping process
151(1)
2 Article selection process
152(2)
3 Cooccurrence relation extraction process
154(1)
4 Systems biology relational extraction process
154(3)
5 The analysis process
157(2)
6 Rich-club analysis
159(1)
7 Stroke case study
160(1)
8 Stroke intractome network analysis results
160(2)
9 Spinal cord injury intractome construction and analysis
162(2)
10 Conclusion
164(1)
References
165(1)
Further reading
165(2)
12 Deep Genomics and Proteomics: Language Model-Based Embedding of Biological Sequences and Their Applications in Bioinformatics
167(16)
Ehsaneddin Asgari
Mohammad R. Mofrad
1 Introduction
167(4)
2 Material and methods
171(4)
3 Results
175(3)
4 Conclusion
178(2)
References
180(3)
13 In Silico Transcription Factor Discovery via Bioinformatics Approach: Application on iPSC Reprogramming Resistant Genes
183(12)
Natalia Polouliakh
1 Introduction
183(1)
2 Regulation of reprogramming resistant genes (RRG) by iPSC reprogramming factors Oct4, Sox2, KLF4, NANOG, and c-Myc
184(5)
3 Comparative genomic analysis iPSC transcription regulation with SHOE
189(1)
4 Conclusions
189(2)
References
191(4)
Index 195
Dr. Firas Kobeissy obtained his PhD from the University of Florida in the area of Neuroscience. His post doc at the Department of Psychiatry focused on neuroproteomics studies of brain injury as well as drug abuse neurotoxicity. His current research is in the area of biochemistry and neuroscience focusing on identifying biomarkers of traumatic brain injury neuroproteomics.Dr. Kobeissy is the author of more than 75 articles, reviews and book chapters along with two patents. He is a member of the Center of Neuroproteomics and Biomarker Research (CNBR) at the McKnight Brain Institute at the University of Florida. He is the editor of three books (Humana Press and Taylor and Francis); the books deal with biomarker identification and proteomics research. Dr. Kobeissy has published extensively in the areas of systems biology pertaining to the areas of deciphering biomarker and pathways of pathogenesis in brain studies obtained from high throughput proteomics data. Dr. Kevin Wang is internationally recognized for his original contributions to the fields of traumatic brain injury (TBI)-linked proteolytic enzymes, therapeutic targets, neuroproteomics/systems biology, biomarker discovery and validation. Dr. Wang has published in the areas of systems biology/bioinformatics of biomarkers identification in with the main application on neural injury. In his quest for brain injury therapeutics, his omics/systems biology work has led to the identification of clinical diagnostic utility for two brain injury protein biomarkers during the acute phase of brain injury which have now been confirmed tested in clinical samples and are now moving forward to FDA-approval seeking pivotal clinical study. His current research directions include studying mechanisms for CNS injury, neuroproteomics, systems biology, and substance abuse-induced brain perturbation using systems biology approach. He has published more than 200 peer-reviewed papers, reviews and book chapters and co-authored eight US patents and co-edited four books. Dr. Wang is also past President (2011-12) and current Councilor (2013 - present) of the National Neurotrauma Society (USA). Dr. Fadi Zaraket obtained his PhD in the ECE from UT Austin. Dr. Zaraket worked in the industry for a dozen years at IBM, Sun Microsystems, and Santa Cruz Operations. His current research spans the (1) automated correctness and (2) information extraction areas. He teaches programming and computer engineering courses. His current research involves extracting entities and relational associations from related text documents, aggregating the extracted entities into graph entities using cross-document analysis techniques, and analyzing the graph entities for domain specific insight. The application of this approach to medical publications in the area of brain diseases with focus on stroke, brain injury and spinal cord injury led to several discoveries. The approach was also applied to Arabic documents coupled with computational linguistic based features to extract information from several corpora such as temporal entities from news documents, narrator chain from literature documents, and family relations from biblical and Islamic tradition documents. Dr. Fadi published in the areas of verification of logic systems and information extraction in several renowned peer reviewed conferences and journals. Ali Alawiehs research is focused on developing translational and site-targeted immunotherapeutics for stroke and brain injury. His research involves a complex mix of in-silico, in-vitro and in-vivo analyses of dysregulated protein and gene-regulatory networks after CNS injury, and how these networks could be fine-tuned to reduce injury and allow recovery. Ali has contributed to novel tools and approaches in data mining, computational modeling and network analysis including among others an automated and intelligent extraction system (SAMNA) to curate protein dysregulation data from scientific literature, a machine learning-based approach for prediction and analysis of bacterial resistance progression in Europe, and a novel approach for constructing and leveraging disease-related protein interaction networks. Ali has published his work in several peer-reviewed journals and wrote several book chapters and reviews in the fields of systems biology, data mining, and computational modelling.