Intelligent strategies for pathway mining: model and pattern identification
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Datensatz im Suchindex
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| adam_text |
INTELLIGENT STRATEGIES FOR PATHWAY MINING
/
: 2014
TABLE OF CONTENTS / INHALTSVERZEICHNIS
INTRODUCTION
DATA RESOURCES AND APPLICATIONS.-DETECTING INCONSISTENCY IN BIOLOGICAL
MOLECULAR DATABASES USING ONTOLOGIES.-EXPLORATION OF POSITIVE FREQUENT
PATTERNS FOR AMP-ACTIVATED PROTEIN KINASE REGULATION.-MINING PROTEIN
KINASE REGULATION USING GRAPHICAL MODELS
MINING INHIBITION PATHWAYS FOR PROTEIN KINASES ON SKELETAL
MUSCLE.-MODELING CONSERVED STRUCTURE PATTERNS FOR FUNCTIONAL NONCODING
RNA.-INTERVAL BASED SIMILARITY FOR FUNCTION CLASSIFICATION OF RNA
PSEUDOKNOTS
DISCOVERY OF STRUCTURAL AND FUNCTIONAL FEATURES BIND TO RNA
PSEUDOKNOTS.-MINING FEATURED PATTERNS OF MIRNA INTERACTION BASED ON
SEQUENCE AND STRUCTURE SIMILARITY
DISCOVERING CONSERVED AND DIVERGED PATTERNS OF MIRNA
FAMILIES.-BIOINFORMATICS-BASED DRUG DISCOVERY FOR PROTEIN KINASES.-
CONCLUSION AND FUTUREWORKS
REFERENCES
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
INTELLIGENT STRATEGIES FOR PATHWAY MINING
/
: 2014
ABSTRACT / INHALTSTEXT
THIS BOOK IS ORGANIZED INTO THIRTEEN CHAPTERS THAT RANGE OVER THE
RELEVANT APPROACHES AND TOOLS IN DATA INTEGRATION, MODELING, ANALYSIS
AND KNOWLEDGE DISCOVERY FOR SIGNALING PATHWAYS. HAVING IN MIND THAT THE
BOOK IS ALSO ADDRESSED FOR STUDENTS, THE CONTRIBUTORS PRESENT THE MAIN
RESULTS AND TECHNIQUES IN AN EASILY ACCESSED AND UNDERSTOOD WAY TOGETHER
WITH MANY REFERENCES AND INSTANCES. CHAPTER 1 PRESENTS AN INTRODUCTION
TO SIGNALING PATHWAY, INCLUDING MOTIVATIONS, BACKGROUND KNOWLEDGE AND
RELEVANT DATA MINING TECHNIQUES FOR PATHWAY DATA ANALYSIS. CHAPTER 2
PRESENTS A VARIETY OF DATA SOURCES AND DATA ANALYSIS WITH RESPECT TO
SIGNALING PATHWAY, INCLUDING DATA INTEGRATION AND RELEVANT DATA MINING
APPLICATIONS. CHAPTER 3 PRESENTS A FRAMEWORK TO MEASURE THE
INCONSISTENCY BETWEEN HETEROGENOUS BIOLOGICAL DATABASES. A GO-BASED
(GENOME ONTOLOGY) STRATEGY IS PROPOSED TO ASSOCIATE DIFFERENT DATA
SOURCES.CHAPTER 4 PRESENTS IDENTIFICATION OF POSITIVE REGULATION OF
KINASE PATHWAYS IN TERMS OF ASSOCIATION RULE MINING. THE RESULTS DERIVED
FROM THIS PROJECT COULD BE USED WHEN PREDICTING ESSENTIAL RELATIONSHIPS
AND ENABLE A COMPREHENSIVE UNDERSTANDING OF KINASE PATHWAY INTERACTION.
CHAPTER 5 PRESENTS GRAPHICAL MODEL-BASED METHODS TO IDENTIFY REGULATORY
NETWORK OF PROTEIN KINASES. A FRAMEWORK USING NEGATIVE ASSOCIATION RULE
MINING IS INTRODUCED IN CHAPTER 6 TO DISCOVER FEATURED INHIBITORY
REGULATION PATTERNS AND THE RELATIONSHIPS BETWEEN INVOLVED REGULATION
FACTORS. IT IS NECESSARY TO NOT ONLY DETECT THE OBJECTS THAT EXHIBIT A
POSITIVE REGULATORY ROLE IN A KINASE PATHWAY BUT ALSO TO DISCOVER THOSE
OBJECTS THAT INHIBIT THE REGULATION. CHAPTER 7 PRESENTS METHODS TO MODEL
NCRNA SECONDARY STRUCTURE DATA IN TERMS OF STEMS, LOOPS AND MARKED
LABELS, AND ILLUSTRATES HOW TO FIND MATCHED STRUCTURE PATTERNS FOR A
GIVEN QUERY.CHAPTER 8 SHOWS AN INTERVAL-BASED DISTANCE METRIC FOR
COMPUTING THE DISTANCE BETWEEN CONSERVED RNA SECONDARY STRUCTURES.
CHAPTER 9 PRESENTS A FRAMEWORK TO EXPLORE STRUCTURAL AND FUNCTIONAL
PATTERNS OF RNA PSEUDOKNOT STRUCTURE ACCORDING TO PROBABILITY MATRIX.
CHAPTER 10 PRESENTS METHODS TO MODEL MIRNA DATA AND IDENTIFY MIRNA
INTERACTION OF CROSS-SPECIES AND WITHIN-SPECIES. CHAPTER 11 PRESENTS AN
APPROACH TO MEASURE THE IMPORTANCE OF MIRNA SITE AND THE ADJACENT BASE
BY USING INFORMATION REDUNDANCY AND DEVELOPS A NOVEL MEASURE TO IDENTIFY
STRONGLY CORRELATED INFREQUENT ITEMSETS. THE DISCOVER ASSOCIATION RULES
NOT ONLY PRESENT IMPORTANT STRUCTURAL FEATURES IN MIRNAS, BUT ALSO
PROMOTE A COMPREHENSIVE UNDERSTANDING OF REGULATORY ROLES OF MIRNAS.
CHAPTER 12 PRESENTS BIOINFORMATICS TECHNIQUES FOR PROTEIN KINASE DATA
MANAGEMENT AND ANALYSIS, KINASE PATHWAYS AND DRUG TARGETS, AND DESCRIBES
THEIR POTENTIAL APPLICATION IN PHARMACEUTICAL INDUSTRY.CHAPTER 13
PRESENTS A SUMMARY OF THE CHAPTERS AND GIVE A BRIEF DISCUSSION TO SOME
EMERGING ISSUES
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT. |
| any_adam_object | 1 |
| author | Chen, Qingfeng Chen, Baoshan Zhang, Chengqi |
| author_GND | (DE-588)1046717618 (DE-588)136277748 |
| author_facet | Chen, Qingfeng Chen, Baoshan Zhang, Chengqi |
| author_role | aut aut aut |
| author_sort | Chen, Qingfeng |
| author_variant | q c qc b c bc c z cz |
| building | Verbundindex |
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| ctrlnum | (OCoLC)867052389 (DE-599)BVBBV041658369 |
| discipline | Biologie Informatik |
| doi_str_mv | 10.1007/978-3-319-04172-8 |
| format | Electronic eBook |
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| indexdate | 2025-01-11T19:06:42Z |
| institution | BVB |
| isbn | 9783319041728 |
| language | English |
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| publisher | Springer |
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| series | Lecture notes in computer science |
| series2 | Lecture notes in computer science |
| spellingShingle | Chen, Qingfeng Chen, Baoshan Zhang, Chengqi Intelligent strategies for pathway mining model and pattern identification Lecture notes in computer science |
| title | Intelligent strategies for pathway mining model and pattern identification |
| title_auth | Intelligent strategies for pathway mining model and pattern identification |
| title_exact_search | Intelligent strategies for pathway mining model and pattern identification |
| title_full | Intelligent strategies for pathway mining model and pattern identification Qingfeng Chen ; Baoshan Chen ; Chengqi Zhang |
| title_fullStr | Intelligent strategies for pathway mining model and pattern identification Qingfeng Chen ; Baoshan Chen ; Chengqi Zhang |
| title_full_unstemmed | Intelligent strategies for pathway mining model and pattern identification Qingfeng Chen ; Baoshan Chen ; Chengqi Zhang |
| title_short | Intelligent strategies for pathway mining |
| title_sort | intelligent strategies for pathway mining model and pattern identification |
| title_sub | model and pattern identification |
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