Life science data mining:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Hackensack, NJ
World Scientific Publ.
2006
|
| Schriftenreihe: | Science, engineering, and biology informatics
2 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVIII, 370 S. Ill., graph. Darst., Tab. |
| ISBN: | 9789812700650 981270065X 9789812700643 9812700641 |
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| 650 | 4 | |a Exploration de données (Informatique) | |
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Datensatz im Suchindex
| _version_ | 1804136463428747264 |
|---|---|
| adam_text | CONTENTS
Preface v
Chapter 1 Survey of Early Warning Systems for Environmental
and Public Health Applications 1
1. Introduction 1
2. Disease Surveillance 3
3. Reference Architecture for Model Extraction 5
4. Problem Domain 9
5. Data Sources 10
6. Detection Methods 12
7. Summary and Conclusion 13
References 14
Chapter 2 Time Lapse Cell Cycle Quantitative Data Analysis
Using Gaussian Mixture Models 17
1. Introduction 18
2. Material and Feature Extraction 20
2.1. Material and cell feature extraction 20
2.2. Model the time lapse data using AR model 23
3. Problem Statement and Formulation 24
4. Classification Methods 26
4.1. Gaussian mixture models and the EM algorithm 26
4.2. K Nearest Neighbor (KNN) classifier 28
4.3. Neural networks 28
4.4. Decision tree 29
4.5. Fisher clustering 30
5. Experimental Results 30
5.1. Trace identification 31
5.2. Cell morphologic similarity analysis 33
5.3. Phase identification 35
5.4. Cluster analysis of time lapse data 37
xi
xii Contents
6. Conclusion 40
Appendix A 41
Appendix B 42
References 43
Chapter 3 Diversity and Accuracy of Data Mining Ensemble 47
1. Introduction 47
2. Ensemble and Diversity 49
2.1. Why needs diversity? 49
2.2. Diversity measures 51
3. Probability Analysis 52
4. Coincident Failure Diversity 52
5. Ensemble Accuracy 55
5.1. Relationship between random guess and accuracy of
lower bound single models 55
5.2. Relationship between accuracy A and the number
of models N 56
5.3. When model s accuracy 50% 57
6. Construction of Effective Ensembles 58
6.1. Strategies for increasing diversity 59
6.2. Ensembles of neural networks 60
6.3. Ensembles of decision trees 61
6.4. Hybrid ensembles 62
7. An Application: Osteoporosis Classification Problem 62
7.1. Osteoporosis problem 63
7.2. Results from the ensembles of neural nets 63
7.3. Results from ensembles of the decision trees 66
7.4. Results of hybrid ensembles 67
8. Discussion and Conclusions 68
References 70
Chapter 4 Integrated Clustering for Microarray Data 73
1. Introduction 73
2. Related Work 77
3. Data Preprocessing 81
Contents xiii
4. Integrated Clustering 83
4.1. Clustering algorithms 83
4.2. Integration methodology 88
5. Experimental Evaluation 89
5.1. Evaluation methodology 89
5.2. Results 91
5.3. Discussion 93
6. Conclusions 94
References 94
Chapter 5 Complexity and Synchronization of EEG with
Parametric Modeling 99
1. Introduction 100
1.1. Brief review of EEG recording analysis 100
1.2. AR modeling based EEG analysis 101
2. TVAR Modeling 104
3. Complexity Measure 105
4. Synchronization Measure 109
5. Conclusions 113
References 114
Chapter 6 Bayesian Fusion of Syndromic Surveillance with
Sensor Data for Disease Outbreak Classification 119
1. Introduction 120
2. Approach 122
2.1. Bayesian belief networks 122
2.2. Syndromic data 126
2.3. Environmental data 128
2.4. Test scenarios 130
2.5. Evaluation metrics 130
3. Results 131
3.1. Scenario 1 131
3.2. Scenario 2 134
3.3. Promptness 135
4. Summary and Conclusions 136
References 137
xiv Contents
Chapter 7 An Evaluation of Over the Counter Medication Sales
for Syndromic Surveillance 143
1. Introduction 143
2. Background and Related Work 144
3. Data 144
4. Approaches 145
4.1. Lead lag correlation analysis 145
4.2. Regression test of predictive ability 146
4.3. Detection based approaches 148
4.4. Supervised algorithm for outbreak detection
in OTC data 148
4.5. Modified Holt Winters forecaster 150
4.6. Forecasting based on multi channel regression 151
5. Experiments 153
5.1. Lead lag correlation analysis of OTC data 153
5.2. Regression test of the predicative value of OTC 154
5.3. Results from detection based approaches 156
6. Conclusions and Future Work 158
References 159
Chapter 8 Collaborative Health Sentinel 163
1. Introduction 163
2. Infectious Disease and Existing Health Surveillance
Programs 166
3. Elements of the Collaborative Health Sentinel (CHS) System.. 170
3.1. Sampling 170
3.2. Creating a national health map 177
3.3. Detection 177
3.4. Reaction 183
3.5. Cost considerations 184
4. Interaction with the Health Information Technology
(HOT) World 185
5. Conclusion 188
References 189
Appendix A HL7 192
Contents xv
Chapter 9 A Multi Modal System Approach for Drug Abuse
Research and Treatment Evaluation: Information
Systems Needs and Challenges 195
1. Introduction 195
2. Context 198
2.1. Data sources 198
2.2. Examples of relevant questions 199
3. Possible System Structure 201
4. Challenges in System Development and Implementation 204
4.1. Ontology development 204
4.2. Data source control, proprietary issues 205
4.3. Privacy, security issues 205
4.4. Costs to implement/maintain system 206
4.5. Historical hypothesis testing paradigm 206
4.6. Utility, usability, credibility of such a system 206
4.7. Funding of system development 207
5. Summary 207
References 208
Chapter 10 Knowledge Representation for Versatile Hybrid
Intelligent Processing Applied in Predictive Toxicology..213
1. Introduction 214
2. Hybrid Intelligent Techniques for Predictive Toxicology
Knowledge Representation 217
3. XML Schemas for Knowledge Representation and
Processing in AI and Predictive Toxicology 218
4. Towards a Standard for Chemical Data Representation in
Predictive Toxicology 220
5. Hybrid Intelligent Systems for Knowledge Representation in
Predictive Toxicology 225
5.1. A formal description of implicit and explicit
knowledge based intelligent systems 226
5.2. An XML schema for hybrid intelligent systems 228
6. A Case Study 231
6.1. Materials and methods 232
6.2. Results 233
xvi Contents
7. Conclusions 235
References 236
Chapter 11 Ensemble Classification System Implementation for
Biomedical Microarray Data 239
1. Introduction 240
2. Background 241
2.1. Reasons for ensemble 241
2.2. Diversity and ensemble 241
2.3. Relationship between measures of diversity and
combination method 243
2.4. Measures of diversity 243
2.5. Microarray data 244
3. Ensemble Classification System (ECS) Design 245
3.1. ECS overview 245
3.2. Feature subset selection 247
3.3. Base classifiers 248
3.4. Combination strategy 249
4. Experiments 250
4.1. Experimental datasets 250
4.2. Experimental results 252
5. Conclusion and Further Work 254
References 255
Chapter 12 An Automated Method for Cell Phase Identification
in High Throughput Time Lapse Screens 257
1. Introduction 258
2. Nuclei Segmentation and Tracking 259
3. Cell Phase Identification 260
3.1. Feature calculation 260
3.2. Identifying cell phase 262
3.3. Correcting cell phase identification errors 265
4. Experimental Results 266
5. Conclusion 272
References 272
Contents xvii
Chapter 13 Inference of Transcriptional Regulatory Networks
Based on Cancer Microarray Data 275
1. Introduction 275
2. Subnetworks and Transcriptional Regulatory Networks
Inference 277
2.1. Inferring subnetworks using z score 277
2.2. Inferring subnetworks based on graph theory 278
2.3. Inferring subnetworks based on Bayesian networks 279
2.4. Inferring transcriptional regulatory networks based
on integrated expression and sequence data 283
3. Multinomial Probit Regression with Baysian Gene Selection... 284
3.1. Problem formulation 284
3.2. Bayesian variable selection 286
3.3. Bayesian estimation using the strongest genes 288
3.4. Experimental results 289
4. Network Construction Based on Clustering and Predictor
Design 293
4.1. Predictor construction using reversible jump MCMC
annealing 293
4.2. CoD for predictors 295
4.3. Experimental results on aMyeloid line 296
5. Concluding Remarks 298
References 299
Chapter 14 Data Mining in Biomedicine 305
1. Introduction 305
2. Predictive Model Construction 306
2.1. Derivation of unsupervised models 307
2.2. Derivation of supervised models 311
3. Validation 316
4. Impact Analysis 318
5. Summary 319
References 319
xviii Contents
Chapter 15 Mining Multilevel Association Rules from Gene
Ontology and Microarray Data 321
1. Introduction 321
2. Proposed Methods 323
2.1. Preprocessing 323
2.2. Hierarchy information encoding 324
3. The MAGO Algorithm 326
3.1. MAGO algorithm 327
3.2. CMAGO (Constrained Multilevel Association rules
with Gene Ontology) 329
4. Experimental Results 330
4.1. The characteristic of thedataset 331
4.2. Experimental results 331
4.3. Interpretation 334
5. Concluding Remarks 335
References 336
Chapter 16 A Proposed Sensor Configuration and Sensitivity
Analysis of Parameters with Applications to Biosensors.. 339
1. Introduction 340
2. Sensor System Configuration 342
3. Optical Biosensors 346
3.1. Relationship between parameters 347
3.2. Modelling of parameters 351
4. Discussion 356
5. Conclusion 358
References 359
Epilogue 361
References 364
Index 365
|
| adam_txt |
CONTENTS
Preface v
Chapter 1 Survey of Early Warning Systems for Environmental
and Public Health Applications 1
1. Introduction 1
2. Disease Surveillance 3
3. Reference Architecture for Model Extraction 5
4. Problem Domain 9
5. Data Sources 10
6. Detection Methods 12
7. Summary and Conclusion 13
References 14
Chapter 2 Time Lapse Cell Cycle Quantitative Data Analysis
Using Gaussian Mixture Models 17
1. Introduction 18
2. Material and Feature Extraction 20
2.1. Material and cell feature extraction 20
2.2. Model the time lapse data using AR model 23
3. Problem Statement and Formulation 24
4. Classification Methods 26
4.1. Gaussian mixture models and the EM algorithm 26
4.2. K Nearest Neighbor (KNN) classifier 28
4.3. Neural networks 28
4.4. Decision tree 29
4.5. Fisher clustering 30
5. Experimental Results 30
5.1. Trace identification 31
5.2. Cell morphologic similarity analysis 33
5.3. Phase identification 35
5.4. Cluster analysis of time lapse data 37
xi
xii Contents
6. Conclusion 40
Appendix A 41
Appendix B 42
References 43
Chapter 3 Diversity and Accuracy of Data Mining Ensemble 47
1. Introduction 47
2. Ensemble and Diversity 49
2.1. Why needs diversity? 49
2.2. Diversity measures 51
3. Probability Analysis 52
4. Coincident Failure Diversity 52
5. Ensemble Accuracy 55
5.1. Relationship between random guess and accuracy of
lower bound single models 55
5.2. Relationship between accuracy A and the number
of models N 56
5.3. When model's accuracy 50% 57
6. Construction of Effective Ensembles 58
6.1. Strategies for increasing diversity 59
6.2. Ensembles of neural networks 60
6.3. Ensembles of decision trees 61
6.4. Hybrid ensembles 62
7. An Application: Osteoporosis Classification Problem 62
7.1. Osteoporosis problem 63
7.2. Results from the ensembles of neural nets 63
7.3. Results from ensembles of the decision trees 66
7.4. Results of hybrid ensembles 67
8. Discussion and Conclusions 68
References 70
Chapter 4 Integrated Clustering for Microarray Data 73
1. Introduction 73
2. Related Work 77
3. Data Preprocessing 81
Contents xiii
4. Integrated Clustering 83
4.1. Clustering algorithms 83
4.2. Integration methodology 88
5. Experimental Evaluation 89
5.1. Evaluation methodology 89
5.2. Results 91
5.3. Discussion 93
6. Conclusions 94
References 94
Chapter 5 Complexity and Synchronization of EEG with
Parametric Modeling 99
1. Introduction 100
1.1. Brief review of EEG recording analysis 100
1.2. AR modeling based EEG analysis 101
2. TVAR Modeling 104
3. Complexity Measure 105
4. Synchronization Measure 109
5. Conclusions 113
References 114
Chapter 6 Bayesian Fusion of Syndromic Surveillance with
Sensor Data for Disease Outbreak Classification 119
1. Introduction 120
2. Approach 122
2.1. Bayesian belief networks 122
2.2. Syndromic data 126
2.3. Environmental data 128
2.4. Test scenarios 130
2.5. Evaluation metrics 130
3. Results 131
3.1. Scenario 1 131
3.2. Scenario 2 134
3.3. Promptness 135
4. Summary and Conclusions 136
References 137
xiv Contents
Chapter 7 An Evaluation of Over the Counter Medication Sales
for Syndromic Surveillance 143
1. Introduction 143
2. Background and Related Work 144
3. Data 144
4. Approaches 145
4.1. Lead lag correlation analysis 145
4.2. Regression test of predictive ability 146
4.3. Detection based approaches 148
4.4. Supervised algorithm for outbreak detection
in OTC data 148
4.5. Modified Holt Winters forecaster 150
4.6. Forecasting based on multi channel regression 151
5. Experiments 153
5.1. Lead lag correlation analysis of OTC data 153
5.2. Regression test of the predicative value of OTC 154
5.3. Results from detection based approaches 156
6. Conclusions and Future Work 158
References 159
Chapter 8 Collaborative Health Sentinel 163
1. Introduction 163
2. Infectious Disease and Existing Health Surveillance
Programs 166
3. Elements of the Collaborative Health Sentinel (CHS) System. 170
3.1. Sampling 170
3.2. Creating a national health map 177
3.3. Detection 177
3.4. Reaction 183
3.5. Cost considerations 184
4. Interaction with the Health Information Technology
(HOT) World 185
5. Conclusion 188
References 189
Appendix A HL7 192
Contents xv
Chapter 9 A Multi Modal System Approach for Drug Abuse
Research and Treatment Evaluation: Information
Systems Needs and Challenges 195
1. Introduction 195
2. Context 198
2.1. Data sources 198
2.2. Examples of relevant questions 199
3. Possible System Structure 201
4. Challenges in System Development and Implementation 204
4.1. Ontology development 204
4.2. Data source control, proprietary issues 205
4.3. Privacy, security issues 205
4.4. Costs to implement/maintain system 206
4.5. Historical hypothesis testing paradigm 206
4.6. Utility, usability, credibility of such a system 206
4.7. Funding of system development 207
5. Summary 207
References 208
Chapter 10 Knowledge Representation for Versatile Hybrid
Intelligent Processing Applied in Predictive Toxicology.213
1. Introduction 214
2. Hybrid Intelligent Techniques for Predictive Toxicology
Knowledge Representation 217
3. XML Schemas for Knowledge Representation and
Processing in AI and Predictive Toxicology 218
4. Towards a Standard for Chemical Data Representation in
Predictive Toxicology 220
5. Hybrid Intelligent Systems for Knowledge Representation in
Predictive Toxicology 225
5.1. A formal description of implicit and explicit
knowledge based intelligent systems 226
5.2. An XML schema for hybrid intelligent systems 228
6. A Case Study 231
6.1. Materials and methods 232
6.2. Results 233
xvi Contents
7. Conclusions 235
References 236
Chapter 11 Ensemble Classification System Implementation for
Biomedical Microarray Data 239
1. Introduction 240
2. Background 241
2.1. Reasons for ensemble 241
2.2. Diversity and ensemble 241
2.3. Relationship between measures of diversity and
combination method 243
2.4. Measures of diversity 243
2.5. Microarray data 244
3. Ensemble Classification System (ECS) Design 245
3.1. ECS overview 245
3.2. Feature subset selection 247
3.3. Base classifiers 248
3.4. Combination strategy 249
4. Experiments 250
4.1. Experimental datasets 250
4.2. Experimental results 252
5. Conclusion and Further Work 254
References 255
Chapter 12 An Automated Method for Cell Phase Identification
in High Throughput Time Lapse Screens 257
1. Introduction 258
2. Nuclei Segmentation and Tracking 259
3. Cell Phase Identification 260
3.1. Feature calculation 260
3.2. Identifying cell phase 262
3.3. Correcting cell phase identification errors 265
4. Experimental Results 266
5. Conclusion 272
References 272
Contents xvii
Chapter 13 Inference of Transcriptional Regulatory Networks
Based on Cancer Microarray Data 275
1. Introduction 275
2. Subnetworks and Transcriptional Regulatory Networks
Inference 277
2.1. Inferring subnetworks using z score 277
2.2. Inferring subnetworks based on graph theory 278
2.3. Inferring subnetworks based on Bayesian networks 279
2.4. Inferring transcriptional regulatory networks based
on integrated expression and sequence data 283
3. Multinomial Probit Regression with Baysian Gene Selection. 284
3.1. Problem formulation 284
3.2. Bayesian variable selection 286
3.3. Bayesian estimation using the strongest genes 288
3.4. Experimental results 289
4. Network Construction Based on Clustering and Predictor
Design 293
4.1. Predictor construction using reversible jump MCMC
annealing 293
4.2. CoD for predictors 295
4.3. Experimental results on aMyeloid line 296
5. Concluding Remarks 298
References 299
Chapter 14 Data Mining in Biomedicine 305
1. Introduction 305
2. Predictive Model Construction 306
2.1. Derivation of unsupervised models 307
2.2. Derivation of supervised models 311
3. Validation 316
4. Impact Analysis 318
5. Summary 319
References 319
xviii Contents
Chapter 15 Mining Multilevel Association Rules from Gene
Ontology and Microarray Data 321
1. Introduction 321
2. Proposed Methods 323
2.1. Preprocessing 323
2.2. Hierarchy information encoding 324
3. The MAGO Algorithm 326
3.1. MAGO algorithm 327
3.2. CMAGO (Constrained Multilevel Association rules
with Gene Ontology) 329
4. Experimental Results 330
4.1. The characteristic of thedataset 331
4.2. Experimental results 331
4.3. Interpretation 334
5. Concluding Remarks 335
References 336
Chapter 16 A Proposed Sensor Configuration and Sensitivity
Analysis of Parameters with Applications to Biosensors. 339
1. Introduction 340
2. Sensor System Configuration 342
3. Optical Biosensors 346
3.1. Relationship between parameters 347
3.2. Modelling of parameters 351
4. Discussion 356
5. Conclusion 358
References 359
Epilogue 361
References 364
Index 365 |
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| id | DE-604.BV022401572 |
| illustrated | Illustrated |
| index_date | 2024-07-02T17:18:35Z |
| indexdate | 2024-07-09T20:56:48Z |
| institution | BVB |
| isbn | 9789812700650 981270065X 9789812700643 9812700641 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015610204 |
| oclc_num | 137294938 |
| open_access_boolean | |
| owner | DE-M49 DE-BY-TUM |
| owner_facet | DE-M49 DE-BY-TUM |
| physical | XVIII, 370 S. Ill., graph. Darst., Tab. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | World Scientific Publ. |
| record_format | marc |
| series | Science, engineering, and biology informatics |
| series2 | Science, engineering, and biology informatics |
| spelling | Life science data mining eds. Stephen Wong ... Hackensack, NJ World Scientific Publ. 2006 XVIII, 370 S. Ill., graph. Darst., Tab. txt rdacontent n rdamedia nc rdacarrier Science, engineering, and biology informatics 2 Bio-informatique Exploration de données (Informatique) Sciences de la vie - Informatique Automatic Data Processing Bioinformatics Biological Science Disciplines methods Computational Biology methods Computational biology Methods Information Storage and Retrieval Biostatistik (DE-588)4729990-3 gnd rswk-swf Biologie (DE-588)4006851-1 gnd rswk-swf Biowissenschaften (DE-588)4129772-6 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Biologie (DE-588)4006851-1 s Data Mining (DE-588)4428654-5 s DE-604 Biostatistik (DE-588)4729990-3 s Biowissenschaften (DE-588)4129772-6 s Statistik (DE-588)4056995-0 s Datenanalyse (DE-588)4123037-1 s b DE-604 Wong, Stephen Sonstige oth Science, engineering, and biology informatics 2 (DE-604)BV022401500 2 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015610204&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Life science data mining Science, engineering, and biology informatics Bio-informatique Exploration de données (Informatique) Sciences de la vie - Informatique Automatic Data Processing Bioinformatics Biological Science Disciplines methods Computational Biology methods Computational biology Methods Information Storage and Retrieval Biostatistik (DE-588)4729990-3 gnd Biologie (DE-588)4006851-1 gnd Biowissenschaften (DE-588)4129772-6 gnd Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4729990-3 (DE-588)4006851-1 (DE-588)4129772-6 (DE-588)4056995-0 (DE-588)4428654-5 (DE-588)4123037-1 |
| title | Life science data mining |
| title_auth | Life science data mining |
| title_exact_search | Life science data mining |
| title_exact_search_txtP | Life science data mining |
| title_full | Life science data mining eds. Stephen Wong ... |
| title_fullStr | Life science data mining eds. Stephen Wong ... |
| title_full_unstemmed | Life science data mining eds. Stephen Wong ... |
| title_short | Life science data mining |
| title_sort | life science data mining |
| topic | Bio-informatique Exploration de données (Informatique) Sciences de la vie - Informatique Automatic Data Processing Bioinformatics Biological Science Disciplines methods Computational Biology methods Computational biology Methods Information Storage and Retrieval Biostatistik (DE-588)4729990-3 gnd Biologie (DE-588)4006851-1 gnd Biowissenschaften (DE-588)4129772-6 gnd Statistik (DE-588)4056995-0 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Bio-informatique Exploration de données (Informatique) Sciences de la vie - Informatique Automatic Data Processing Bioinformatics Biological Science Disciplines methods Computational Biology methods Computational biology Methods Information Storage and Retrieval Biostatistik Biologie Biowissenschaften Statistik Data Mining Datenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015610204&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV022401500 |
| work_keys_str_mv | AT wongstephen lifesciencedatamining |