Biological data mining and its applications in healthcare /:
Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to di...
Gespeichert in:
| Weitere Verfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New Jersey :
World Scientific,
2013.
|
| Schriftenreihe: | Science, engineering, and biology informatics ;
v. 8. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains. |
| Beschreibung: | 1 online resource |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 9789814551014 9814551015 |
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| 505 | 0 | |a Contents -- Preface -- Part I: Sequence Analysis -- Mining the Sequence Databases for Homology Detection: Application to Recognition of Functions of Trypanosoma brucei brucei Proteins and Drug Targets -- 1. Introduction -- 2. Remote homology driven approaches for protein function annotation -- 2.1. Sequence-based approaches for remote homology detection -- 2.1.1. Iterated searches using PSI-BLAST -- 2.1.2. Multi-profiles approach to improve sensitivity -- 2.1.3. Cascade PSI-BLAST -- 2.1.4. Hidden Markov Models -- 2.1.5. Profile-profile matching algorithms | |
| 505 | 8 | |a 2.2. Assessment of significant sequence alignments3. Trypanosoma brucei: A case study -- 3.1. Overview on structural and functional domain assignments in T. brucei proteome -- 3.2. Fold assignments -- 3.3. Metabolic proteins in Trypanosoma brucei -- 3.3.1. Domain composition of metabolic proteins -- 3.3.2. Predicting drug targets based on remote homology approaches -- 4. Conclusions -- Acknowledgments -- References -- Identification of Genes and their Regulatory Regions Based on Multiple Physical and Structural Properties of a DNA Sequence -- 1. Introduction | |
| 505 | 8 | |a 2. Gene prediction methods2.1. Background -- 2.2. Exon prediction based on the AR model and multifeature spectral analysis -- 3. Regulatory region (promoter) prediction methods -- 3.1. Background -- 3.2. Cascade AdaBoost algorithm -- 3.3. Hierarchical promoter prediction system based on signal, context and structural properties -- 3.4. Prediction of eukaryotic core promoters based on Isomap and support vector machine -- 3.5. Computational identification of disease-related genes and regulatory regions -- 4. Summary -- Acknowledgement -- References | |
| 505 | 8 | |a Mining Genomic Sequence Data for Related Sequences Using Pairwise Statistical Significance1. Introduction -- 1.1. Biological sequence -- 1.2. Homology and similarity -- 1.3. Sequence alignment -- 2. Statistical significance -- 2.1. Why statistical significance? -- 2.2. P-value in statistical significance -- 2.3. Modeling statistical for local sequence alignment -- 2.3.1. Coin-Toss model -- 2.3.2. Assessing the statistical significance using alignment scores -- 2.4. Gumbel extreme value distribution -- 3. Pairwise statistical significance | |
| 505 | 8 | |a 3.1. The definition of pairwise statistical significance3.2. Parameters fitting of pairwise statistical significance -- 3.3. Evaluation of pairwise statistical significance -- 4. HPC solutions for accelerating pairwise statistical significance estimation -- 4.1. Parallel paradigms of HPC techniques -- 4.2. Implementations -- 4.3. Summary -- Acknowledgement -- References -- Part II: Biological Network Mining -- Indexing for Similarity Queries on Biological Networks -- 1. Introduction -- 2. Preliminaries -- 2.1. Definitions -- 2.2. Problem Formulation | |
| 520 | |a Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains. | ||
| 650 | 0 | |a Medical informatics. |0 http://id.loc.gov/authorities/subjects/sh89005069 | |
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| contents | Contents -- Preface -- Part I: Sequence Analysis -- Mining the Sequence Databases for Homology Detection: Application to Recognition of Functions of Trypanosoma brucei brucei Proteins and Drug Targets -- 1. Introduction -- 2. Remote homology driven approaches for protein function annotation -- 2.1. Sequence-based approaches for remote homology detection -- 2.1.1. Iterated searches using PSI-BLAST -- 2.1.2. Multi-profiles approach to improve sensitivity -- 2.1.3. Cascade PSI-BLAST -- 2.1.4. Hidden Markov Models -- 2.1.5. Profile-profile matching algorithms 2.2. Assessment of significant sequence alignments3. Trypanosoma brucei: A case study -- 3.1. Overview on structural and functional domain assignments in T. brucei proteome -- 3.2. Fold assignments -- 3.3. Metabolic proteins in Trypanosoma brucei -- 3.3.1. Domain composition of metabolic proteins -- 3.3.2. Predicting drug targets based on remote homology approaches -- 4. Conclusions -- Acknowledgments -- References -- Identification of Genes and their Regulatory Regions Based on Multiple Physical and Structural Properties of a DNA Sequence -- 1. Introduction 2. Gene prediction methods2.1. Background -- 2.2. Exon prediction based on the AR model and multifeature spectral analysis -- 3. Regulatory region (promoter) prediction methods -- 3.1. Background -- 3.2. Cascade AdaBoost algorithm -- 3.3. Hierarchical promoter prediction system based on signal, context and structural properties -- 3.4. Prediction of eukaryotic core promoters based on Isomap and support vector machine -- 3.5. Computational identification of disease-related genes and regulatory regions -- 4. Summary -- Acknowledgement -- References Mining Genomic Sequence Data for Related Sequences Using Pairwise Statistical Significance1. Introduction -- 1.1. Biological sequence -- 1.2. Homology and similarity -- 1.3. Sequence alignment -- 2. Statistical significance -- 2.1. Why statistical significance? -- 2.2. P-value in statistical significance -- 2.3. Modeling statistical for local sequence alignment -- 2.3.1. Coin-Toss model -- 2.3.2. Assessing the statistical significance using alignment scores -- 2.4. Gumbel extreme value distribution -- 3. Pairwise statistical significance 3.1. The definition of pairwise statistical significance3.2. Parameters fitting of pairwise statistical significance -- 3.3. Evaluation of pairwise statistical significance -- 4. HPC solutions for accelerating pairwise statistical significance estimation -- 4.1. Parallel paradigms of HPC techniques -- 4.2. Implementations -- 4.3. Summary -- Acknowledgement -- References -- Part II: Biological Network Mining -- Indexing for Similarity Queries on Biological Networks -- 1. Introduction -- 2. Preliminaries -- 2.1. Definitions -- 2.2. Problem Formulation |
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| id | ZDB-4-EBA-ocn869281569 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:25:46Z |
| institution | BVB |
| isbn | 9789814551014 9814551015 |
| language | English |
| oclc_num | 869281569 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource |
| psigel | ZDB-4-EBA |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | World Scientific, |
| record_format | marc |
| series | Science, engineering, and biology informatics ; |
| series2 | Science, engineering, and biology informatics ; |
| spelling | Biological data mining and its applications in healthcare / [edited by] Xiaoli Li (A*STAR, Singapore & Nanyang Technological University, Singapore), See-Kiong Ng (A*STAR, Singapore), & Jason T.L. Wang (New Jersey Institute of Technology, USA). New Jersey : World Scientific, 2013. 1 online resource text txt rdacontent computer c rdamedia online resource cr rdacarrier Science, engineering, and biology informatics ; volume 8 Includes bibliographical references and index. Print version record. Contents -- Preface -- Part I: Sequence Analysis -- Mining the Sequence Databases for Homology Detection: Application to Recognition of Functions of Trypanosoma brucei brucei Proteins and Drug Targets -- 1. Introduction -- 2. Remote homology driven approaches for protein function annotation -- 2.1. Sequence-based approaches for remote homology detection -- 2.1.1. Iterated searches using PSI-BLAST -- 2.1.2. Multi-profiles approach to improve sensitivity -- 2.1.3. Cascade PSI-BLAST -- 2.1.4. Hidden Markov Models -- 2.1.5. Profile-profile matching algorithms 2.2. Assessment of significant sequence alignments3. Trypanosoma brucei: A case study -- 3.1. Overview on structural and functional domain assignments in T. brucei proteome -- 3.2. Fold assignments -- 3.3. Metabolic proteins in Trypanosoma brucei -- 3.3.1. Domain composition of metabolic proteins -- 3.3.2. Predicting drug targets based on remote homology approaches -- 4. Conclusions -- Acknowledgments -- References -- Identification of Genes and their Regulatory Regions Based on Multiple Physical and Structural Properties of a DNA Sequence -- 1. Introduction 2. Gene prediction methods2.1. Background -- 2.2. Exon prediction based on the AR model and multifeature spectral analysis -- 3. Regulatory region (promoter) prediction methods -- 3.1. Background -- 3.2. Cascade AdaBoost algorithm -- 3.3. Hierarchical promoter prediction system based on signal, context and structural properties -- 3.4. Prediction of eukaryotic core promoters based on Isomap and support vector machine -- 3.5. Computational identification of disease-related genes and regulatory regions -- 4. Summary -- Acknowledgement -- References Mining Genomic Sequence Data for Related Sequences Using Pairwise Statistical Significance1. Introduction -- 1.1. Biological sequence -- 1.2. Homology and similarity -- 1.3. Sequence alignment -- 2. Statistical significance -- 2.1. Why statistical significance? -- 2.2. P-value in statistical significance -- 2.3. Modeling statistical for local sequence alignment -- 2.3.1. Coin-Toss model -- 2.3.2. Assessing the statistical significance using alignment scores -- 2.4. Gumbel extreme value distribution -- 3. Pairwise statistical significance 3.1. The definition of pairwise statistical significance3.2. Parameters fitting of pairwise statistical significance -- 3.3. Evaluation of pairwise statistical significance -- 4. HPC solutions for accelerating pairwise statistical significance estimation -- 4.1. Parallel paradigms of HPC techniques -- 4.2. Implementations -- 4.3. Summary -- Acknowledgement -- References -- Part II: Biological Network Mining -- Indexing for Similarity Queries on Biological Networks -- 1. Introduction -- 2. Preliminaries -- 2.1. Definitions -- 2.2. Problem Formulation Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains. Medical informatics. http://id.loc.gov/authorities/subjects/sh89005069 Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Computational Biology methods Data Mining methods Genomics methods Natural Language Processing Imaging, Three-Dimensional methods Computational Biology https://id.nlm.nih.gov/mesh/D019295 Data Mining https://id.nlm.nih.gov/mesh/D057225 Médecine Informatique. Bio-informatique. Exploration de données (Informatique) Traitement automatique des langues naturelles. HEALTH & FITNESS Holism. bisacsh HEALTH & FITNESS Reference. bisacsh MEDICAL Alternative Medicine. bisacsh MEDICAL Atlases. bisacsh MEDICAL Essays. bisacsh MEDICAL Family & General Practice. bisacsh MEDICAL Holistic Medicine. bisacsh MEDICAL Osteopathy. bisacsh Natural language processing (Computer science) fast Bioinformatics fast Data mining fast Medical informatics fast Li, Xiao-Li, 1969- editor. https://id.oclc.org/worldcat/entity/E39PCjHmXKCFJJh8xWh4pHdwQ3 http://id.loc.gov/authorities/names/n2008184082 Ng, See-Kiong, editor. Wang, Jason T. L., editor. Print version: Biological data mining and its applications in healthcare 9789814551007 (DLC) 2013037382 (OCoLC)855583066 Science, engineering, and biology informatics ; v. 8. http://id.loc.gov/authorities/names/no2007052160 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=689749 Volltext |
| spellingShingle | Biological data mining and its applications in healthcare / Science, engineering, and biology informatics ; Contents -- Preface -- Part I: Sequence Analysis -- Mining the Sequence Databases for Homology Detection: Application to Recognition of Functions of Trypanosoma brucei brucei Proteins and Drug Targets -- 1. Introduction -- 2. Remote homology driven approaches for protein function annotation -- 2.1. Sequence-based approaches for remote homology detection -- 2.1.1. Iterated searches using PSI-BLAST -- 2.1.2. Multi-profiles approach to improve sensitivity -- 2.1.3. Cascade PSI-BLAST -- 2.1.4. Hidden Markov Models -- 2.1.5. Profile-profile matching algorithms 2.2. Assessment of significant sequence alignments3. Trypanosoma brucei: A case study -- 3.1. Overview on structural and functional domain assignments in T. brucei proteome -- 3.2. Fold assignments -- 3.3. Metabolic proteins in Trypanosoma brucei -- 3.3.1. Domain composition of metabolic proteins -- 3.3.2. Predicting drug targets based on remote homology approaches -- 4. Conclusions -- Acknowledgments -- References -- Identification of Genes and their Regulatory Regions Based on Multiple Physical and Structural Properties of a DNA Sequence -- 1. Introduction 2. Gene prediction methods2.1. Background -- 2.2. Exon prediction based on the AR model and multifeature spectral analysis -- 3. Regulatory region (promoter) prediction methods -- 3.1. Background -- 3.2. Cascade AdaBoost algorithm -- 3.3. Hierarchical promoter prediction system based on signal, context and structural properties -- 3.4. Prediction of eukaryotic core promoters based on Isomap and support vector machine -- 3.5. Computational identification of disease-related genes and regulatory regions -- 4. Summary -- Acknowledgement -- References Mining Genomic Sequence Data for Related Sequences Using Pairwise Statistical Significance1. Introduction -- 1.1. Biological sequence -- 1.2. Homology and similarity -- 1.3. Sequence alignment -- 2. Statistical significance -- 2.1. Why statistical significance? -- 2.2. P-value in statistical significance -- 2.3. Modeling statistical for local sequence alignment -- 2.3.1. Coin-Toss model -- 2.3.2. Assessing the statistical significance using alignment scores -- 2.4. Gumbel extreme value distribution -- 3. Pairwise statistical significance 3.1. The definition of pairwise statistical significance3.2. Parameters fitting of pairwise statistical significance -- 3.3. Evaluation of pairwise statistical significance -- 4. HPC solutions for accelerating pairwise statistical significance estimation -- 4.1. Parallel paradigms of HPC techniques -- 4.2. Implementations -- 4.3. Summary -- Acknowledgement -- References -- Part II: Biological Network Mining -- Indexing for Similarity Queries on Biological Networks -- 1. Introduction -- 2. Preliminaries -- 2.1. Definitions -- 2.2. Problem Formulation Medical informatics. http://id.loc.gov/authorities/subjects/sh89005069 Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Computational Biology methods Data Mining methods Genomics methods Natural Language Processing Imaging, Three-Dimensional methods Computational Biology https://id.nlm.nih.gov/mesh/D019295 Data Mining https://id.nlm.nih.gov/mesh/D057225 Médecine Informatique. Bio-informatique. Exploration de données (Informatique) Traitement automatique des langues naturelles. HEALTH & FITNESS Holism. bisacsh HEALTH & FITNESS Reference. bisacsh MEDICAL Alternative Medicine. bisacsh MEDICAL Atlases. bisacsh MEDICAL Essays. bisacsh MEDICAL Family & General Practice. bisacsh MEDICAL Holistic Medicine. bisacsh MEDICAL Osteopathy. bisacsh Natural language processing (Computer science) fast Bioinformatics fast Data mining fast Medical informatics fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh89005069 http://id.loc.gov/authorities/subjects/sh00003585 http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh88002425 https://id.nlm.nih.gov/mesh/D019295 https://id.nlm.nih.gov/mesh/D057225 |
| title | Biological data mining and its applications in healthcare / |
| title_auth | Biological data mining and its applications in healthcare / |
| title_exact_search | Biological data mining and its applications in healthcare / |
| title_full | Biological data mining and its applications in healthcare / [edited by] Xiaoli Li (A*STAR, Singapore & Nanyang Technological University, Singapore), See-Kiong Ng (A*STAR, Singapore), & Jason T.L. Wang (New Jersey Institute of Technology, USA). |
| title_fullStr | Biological data mining and its applications in healthcare / [edited by] Xiaoli Li (A*STAR, Singapore & Nanyang Technological University, Singapore), See-Kiong Ng (A*STAR, Singapore), & Jason T.L. Wang (New Jersey Institute of Technology, USA). |
| title_full_unstemmed | Biological data mining and its applications in healthcare / [edited by] Xiaoli Li (A*STAR, Singapore & Nanyang Technological University, Singapore), See-Kiong Ng (A*STAR, Singapore), & Jason T.L. Wang (New Jersey Institute of Technology, USA). |
| title_short | Biological data mining and its applications in healthcare / |
| title_sort | biological data mining and its applications in healthcare |
| topic | Medical informatics. http://id.loc.gov/authorities/subjects/sh89005069 Bioinformatics. http://id.loc.gov/authorities/subjects/sh00003585 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Computational Biology methods Data Mining methods Genomics methods Natural Language Processing Imaging, Three-Dimensional methods Computational Biology https://id.nlm.nih.gov/mesh/D019295 Data Mining https://id.nlm.nih.gov/mesh/D057225 Médecine Informatique. Bio-informatique. Exploration de données (Informatique) Traitement automatique des langues naturelles. HEALTH & FITNESS Holism. bisacsh HEALTH & FITNESS Reference. bisacsh MEDICAL Alternative Medicine. bisacsh MEDICAL Atlases. bisacsh MEDICAL Essays. bisacsh MEDICAL Family & General Practice. bisacsh MEDICAL Holistic Medicine. bisacsh MEDICAL Osteopathy. bisacsh Natural language processing (Computer science) fast Bioinformatics fast Data mining fast Medical informatics fast |
| topic_facet | Medical informatics. Bioinformatics. Data mining. Natural language processing (Computer science) Computational Biology methods Data Mining methods Genomics methods Natural Language Processing Imaging, Three-Dimensional methods Computational Biology Data Mining Médecine Informatique. Bio-informatique. Exploration de données (Informatique) Traitement automatique des langues naturelles. HEALTH & FITNESS Holism. HEALTH & FITNESS Reference. MEDICAL Alternative Medicine. MEDICAL Atlases. MEDICAL Essays. MEDICAL Family & General Practice. MEDICAL Holistic Medicine. MEDICAL Osteopathy. Bioinformatics Data mining Medical informatics |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=689749 |
| work_keys_str_mv | AT lixiaoli biologicaldatamininganditsapplicationsinhealthcare AT ngseekiong biologicaldatamininganditsapplicationsinhealthcare AT wangjasontl biologicaldatamininganditsapplicationsinhealthcare |