Machine learning and statistical methods for preclinical omics data analysis:
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis Book |
| Language: | English |
| Published: |
München
Verlag Dr. Hut
2015
|
| Edition: | 1. Auflage |
| Series: | Informatik
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xiv, 182 Seiten Illustrationen, Diagramme |
| ISBN: | 9783843923514 |
Staff View
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| 245 | 1 | 0 | |a Machine learning and statistical methods for preclinical omics data analysis |c Johannes Eichner |
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Record in the Search Index
| _version_ | 1804175845889146880 |
|---|---|
| adam_text | CONTENTS
1 INTRODUCTION 1
1.1 CONTRIBUTIONS OF THIS THESIS 2
1.2 THESIS ORGANIZATION 5
2 MONITORING GENE EXPRESSION ACROSS MULTIPLE MOLECULAR LAYERS 7
2.1 MOLECULAR MECHANISMS FOR THE REGULATION OF GENE EXPRESSION 7
2.1.1 TRANSCRIPTION FACTORS 8
2.1.2 DNA METHYLATION 10
2.1.3 MICRORNA INTERFERENCE 11
2.1.4 POST-TRANSLATIONAL MODIFICATIONS 13
2.1.5 INTERPLAY OF MOLECULAR LAYERS 16
2.2 MICIOARRAY PLATFORMS FOR MOLECULAR PROFILING 16
2.2.1 GENE EXPRESSION ARRAYS 16
2.2.2 DNA METHYLATION ARRAYS 18
2.2.3 REVERSE-PHASE PROTEIN ARRAYS 19
2.3 GENERIC METRICS
FOR MICROANAY QUALITY CONTROL 20
2.4 PRE-PROCESSING OF MICROARRAY DATASETS 21
2.4.1 AFFYMETRIX MRNA EXPRESSION ARRAYS 23
2.4.2 AGILENT MIRNA EXPRESSION ARRAYS 23
2.4.3 NIMBLEGEN DNA METHYLATION ARRAYS 24
2.4.4 ZEPTOSENS REVERSE-PHASE PROTEIN
ARRAYS 25
2.5 CONCEPTS AND ALGORITHMS
FOR MICROARRAY DATA ANALYSIS 25
2.5.1 DETECTION OF DIFFERENTIAL REGULATION 26
2.5.2 CLUSTER ANALYSIS 28
2.5.3 GENE SET ENRICHMENT AND OVERREPRESENTATION ANALYSIS 28
3 INTRODUCTION INTO STATISTICAL METHODS AND MACHINE LEARNING 31
3.1 STATISTICAL APPROACHES 31
3.1.1 GENERAL CONCEPTS OF HYPOTHESIS
TESTS 31
3.2 STATISTICS FOR MICROARRAY DATA
ANALYSIS 32
3.2.1 STATISTICS FOR DETECTING DEREGULATED MOLECULAR FEATURES 32
3.2.2 STATISTICS FOR THE
FUNCTIONAL ANALYSIS OF MOLECULAR FEATURES ... 33
3.2.3 STATISTICS FOR THE SELECTION
OF INFORMATIVE MOLECULAR FEATURES . . 34
XI
HTTP://D-NB.INFO/1079487883
CONTENTS
3.3 STATISTICS AND ALGORITHMS
FOR BIOLOGICAL SEQUENCE
ANALYSIS 34
3.3.1 ALIGNMENT-BASED ANALYSIS OF PROTEIN
SEQUENCE SIMILARITY .... 35
3.3.2 KERNEL-BASED COMPARISON OF PROTEIN SEQUENCES 35
3.3.3 FEATURE REPRESENTATIONS FOR PROTEIN SEQUENCES 37
3.3.4 STATISTICS FOR DNA MOTIF
COMPARISON 38
3.3.3 ALGORITHMIC CONSTRUCTION OF CONSENSUS MOTIFS 39
3.4 MACHINE LEARNING ALGORITHMS 40
3.4.1 SUPERVISED CLASSIFICATION 40
3.4.2 REGRESSION ANALYSIS 43
3.5 MODEL OPTIMIZATION AND VALIDATION 44
3.3.1 FEATURE SELECTION 44
3.3.2 MODEL SELECTION AND VALIDATION 43
3.5.3 SCORING METRICS 46
4 BIOINFORMATICS TOOLS FOR OMICS DATA ANALYSIS 49
4.1 EXISTING SOFTWARE FOR
THE ANALYSIS OF OMICS DATA 30
4.2 A PIPELINE FOR THE
ANALYSIS OF RPPA DATA 31
4.2.1 PIPELINE STRUCTURE AND
INTEGRATED TOOLS 53
4.2.2 SPECIAL FEATURES FOR PROTEIN ARRAY DATA ANALYSIS 54
4.2.3 APPLICATION TO EXPRESSION DATA FROM
COLON CARCINOMA SPHEROIDS 58
4.2.4 SUMMARY OF RPPAPIPE SOFTWARE 59
4.3 PATHWAY-CENTERED ANALYSIS OF CROSS-OMICS
DATASETS 59
4.3.1 TOOLS FOR THE
INTEGRATED VISUALIZATION OF CROSS-OMICS DATA .... 60
4.3.2 WORKFLOW FOR INTEGRATED OMICS DATA ANALYSIS 62
4.3.3 IMPORTING CROSS-OMICS DATASETS
INTO THE INCROMAP SOFTWARE . . 64
4.3.4 PATHWAY ENRICHMENT ANALYSIS
METHODS FOR
CROSS-OMICS DATASETS . 65
4.3.5 VISUALIZING ALTERATIONS IN METABOLIC
AND SIGNALING NETWORKS
... 65
4.3.6 ANALYSIS OF MULTI-LEVEL OMICS DATA FROM MOUSE LIVER
TUMORS WITH
INCROMAP 67
4.3.7 SUMMARY OF INCROMAP
SOFTWARE 69
4.4 SUMMARY AND FUTURE DIRECTIONS 69
5 SEQUENCE-BASED PREDICTION AND ANNOTATION OF TRANSCRIPTION FACTORS 71
5.1 COMPUTATIONAL ANNOTATION OF PROTEIN SEQUENCES 72
3.2 BLAST SCORE PERCENTILE
FEATURES 73
3.3 PIPELINE FOR
THE STRUCTURAL
AND FUNCTIONAL ANNOTATION
OF TRANSCRIPTION FACTORS 75
5.4 COMPILING A VALIDATION
SET OF ANNOTATED TRANSCRIPTION FACTOR SEQUENCES . 76
5.5 DISCRIMINATION OF TRANSCRIPTION FACTORS
FROM OTHER PROTEINS 78
5.6 STRUCTURAL CLASSIFICATION OF TRANSCRIPTION FACTORS 80
5.7 PERFORMANCE COMPARISON
WITH PUBLISHED METHODS
FOR TRANSCRIPTION
FACTOR
CLASSIFICATION 82
3.8 DETECTION OF THE DNA-BINDING DOMAINS
OF TRANSCRIPTION FACTORS 84
XII
CONTENTS
5.9 INFERENCE OF THE DNA MOTIFS
OF TRANSCRIPTION FACTORS 85
5.9.1 AN ALGORITHM FOR THE ESTIMATION OF TRANSCRIPTION FACTOR BINDING
PROFILES 85
5.9.2 PERFORMANCE
AND TRANSFER RATE OF PFM
PREDICTION MODELS
DEPEND
ING ON THE
INCORPORATED FEATURES 88
5.10 IMPLEMENTATION OF AN AUTOMATABLE TRANSCRIPTION FACTOR
ANNOTATION PIPELINE 90
5.10.1 IDENTIFICATION AND STRUCTURAL ANNOTATION OF TRANSCRIPTION FACTORS
WITH TFPREDICT 91
5.10.2 PREDICTING THE DNA-BINDING SPECIFICITY OF TRANSCRIPTION FACTORS
WITH SABINE 92
5.11 SUMMARY AND FUTURE DIRECTIONS 94
6 TOXICOGENOMICS APPROACHES FOR ASSESSING THE CARCINOGENIC RISK OF DRUGS
97
6.1 STATE-OF-THE ART METHODS FOR ASSESSING PRECLINICAL CARCINOGENICITY
.... 97
6.1.1 EARLIER STUDIES IN THE FIELD OF TOXICOGENOMICS 98
6.1.2 TOXICOGENOMICS DATABASES 99
6.2 ENSEMBLE FEATURE
SELECTION-BASED INFERENCE OF ROBUST BIOMARKER
SIGNATURES
100
6.3 A STATISTICAL APPROACH
FOR EXTRACTING
CHARACTERISTIC MOLECULAR SIGNATURES
. 104
6.4 CLASSIFICATION OF CARCINOGENS BASED ON EXPRESSION DATA FROM A MOUSE
IN
VIVO STUDY 105
6.4.1 ACCURACY AND STABILITY OF SIGNATURES INFERRED FROM MOUSE LIVER
EXPRESSION DATA 106
6.4.2 TOXICOGENOMICS-BASED RECLASSIFICATION OF COMPOUNDS WITH UN
DEFINED CARCINOGENIC
CLASS 110
6.4.3 GENES AND PATHWAYS RELATED TO MOUSE LIVER CARCINOGENESIS ... 112
6.4.4 COMPARISON WITH PUBLISHED MRNA SIGNATURES FOR THE DETECTION
OF DRUG-INDUCED CARCINOGENESIS IN MOUSE LIVER 113
6.5 EVALUATION OF TOXICOGENOMICS APPROACHES ON PUBLISHED RAT LIVER
EXPRES
SION DATA 114
6.5.1 PERFORMANCE ASSESSMENT OF TOXICOGENOMICS MODELS ON PUBLIC
DATA FROM TG-GATES 115
6.5.2 ACCURACY AND ROBUSTNESS OF INDIVIDUAL
GENE SELECTION METHODS . 117
6.5.3 PERFORMANCE COMPARISON WITH PUBLISHED MRNA SIGNATURES FOR
RAT LIVER CARCINOGENESIS 119
6.5.4 GENES AND PATHWAYS RELATED TO RAT LIVER CARCINOGENESIS 122
6.5.5 GENE EXPRESSION ANALYSIS
OF CARCINOGENS SHOWING MIXED
CHARAC
TERISTICS OF BOTH GENOTOXIC
AND NONGENOTOXIC AGENTS 124
6.6 SUMMARY AND CONCLUSIONS 126
7 TOXICOGENOMICS APPROACHES FOR THE INTEGRATED ANALYSIS OF CROSS-OMICS
DATA 129
7.1 NOVEL FEATURE REPRESENTATIONS FOR CROSS-PLATFORM TOXICOGENOMICS ....
130
7.1.1 MOLECULAR INTERACTION FEATURES 130
XIII
CONTENTS
7.1.2 PATHWAY ENRICHMENT FEATURES 132
7.2 WORKFLOW FOR TOXICOGENOMICS-BASED ANALYSIS
OF MULTI-LEVEL OMICS DATA . 133
7.3 PERFORMANCE OF TOXICOGENOMICS
MODELS TRAINED
ON DIFFERENT
FEATURE COM
POSITIONS 134
7.4
INFORMATIVE,
MOLECULAR
PROFILES FOR THE CONSTRUCTION OF TOXICOGENOMICS
MODELS 136
7.5 RECLASSIFICATION OF UNDEFINED COMPOUNDS BASED ON MOLECULAR
SIGNATURES 141
7.6 SUMMARY AND CONCLUSIONS 143
8 DISCUSSION AND CONCLUDING REMARKS 145
A SUPPLEMENTARY TABLES 151
ABBREVIATIONS 157
BIBLIOGRAPHY 161
XIV
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| id | DE-604.BV043309222 |
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| indexdate | 2024-07-10T07:22:46Z |
| institution | BVB |
| isbn | 9783843923514 |
| language | English |
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| physical | xiv, 182 Seiten Illustrationen, Diagramme |
| publishDate | 2015 |
| publishDateSearch | 2015 |
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| publisher | Verlag Dr. Hut |
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| series2 | Informatik |
| spelling | Eichner, Johannes Verfasser (DE-588)1079224602 aut Machine learning and statistical methods for preclinical omics data analysis Johannes Eichner 1. Auflage München Verlag Dr. Hut 2015 xiv, 182 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Informatik Dissertation Eberhard-Karls-Universität Tübingen 2015 Diagnostik (DE-588)4113303-1 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Diagnostik (DE-588)4113303-1 s Maschinelles Lernen (DE-588)4193754-5 s Datenanalyse (DE-588)4123037-1 s DE-604 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028729914&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Eichner, Johannes Machine learning and statistical methods for preclinical omics data analysis Diagnostik (DE-588)4113303-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4113303-1 (DE-588)4193754-5 (DE-588)4123037-1 (DE-588)4113937-9 |
| title | Machine learning and statistical methods for preclinical omics data analysis |
| title_auth | Machine learning and statistical methods for preclinical omics data analysis |
| title_exact_search | Machine learning and statistical methods for preclinical omics data analysis |
| title_full | Machine learning and statistical methods for preclinical omics data analysis Johannes Eichner |
| title_fullStr | Machine learning and statistical methods for preclinical omics data analysis Johannes Eichner |
| title_full_unstemmed | Machine learning and statistical methods for preclinical omics data analysis Johannes Eichner |
| title_short | Machine learning and statistical methods for preclinical omics data analysis |
| title_sort | machine learning and statistical methods for preclinical omics data analysis |
| topic | Diagnostik (DE-588)4113303-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Diagnostik Maschinelles Lernen Datenanalyse Hochschulschrift |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028729914&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT eichnerjohannes machinelearningandstatisticalmethodsforpreclinicalomicsdataanalysis |