Statistical modeling and machine learning for molecular biology:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
[2017]
|
| Schriftenreihe: | Chapman & Hall/CRC mathematical and computational biology series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xvi, 264 Seiten Illustrationen, Diagramme |
| ISBN: | 9781482258592 1482258595 |
Internformat
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| 245 | 1 | 0 | |a Statistical modeling and machine learning for molecular biology |c Alan M. Moses, University of Toronto, Canada |
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press, Taylor & Francis Group |c [2017] | |
| 264 | 4 | |c © 2017 | |
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| 500 | |a Includes bibliographical references and index | ||
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| 650 | 4 | |a Molecular biology / Statistical methods | |
| 650 | 4 | |a Molecular biology / Data processing | |
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Datensatz im Suchindex
| _version_ | 1804177475146612736 |
|---|---|
| adam_text | Contents
Acknowledgments, xv
Section I Overview
Chapter 1 - Across Statistical Modeling and Machine
_____________Learning on a Shoestring___________________3
1.1 ABOUT THIS BOOK 3
1.2 WHAT WILL THIS BOOK COVER? 4
1.2.1 Clustering 4
1.2.2 Regression 5
1.2.3 Classification 6
1.3 ORGANIZATION OF THIS BOOK 6
1.4 WHY ARE THERE MATHEMATICAL CALCULATIONS
IN THE BOOK? 8
1.5 WHAT WON T THIS BOOK COVER? 11
1.6 WHY IS THIS A BOOK? 12
REFERENCES AND FURTHER READING 14
Chapter 2 ■ Statistical Modeling_______________________T5
2.1 WHAT IS STATISTICAL MODELING? 15
2.2 PROBABILITY DISTRIBUTIONS ARE THE MODELS 18
2.3 AXIOMS OF PROBABILITY AND THEIR
CONSEQUENCES: RULES OF PROBABILITY 23
2.4 HYPOTHESIS TESTING: WHAT YOU PROBABLY
ALREADY KNOW ABOUT STATISTICS 26
ix
X H Contents
2.5 TESTS WITH FEWER ASSUMPTIONS 30
2.5.1 Wilcoxon Rank-Sum Test, Also Known As the
Mann-Whitney U Test (or Simply the WMW Test) 30
2.5.2 Kolmogorov-Smirnov Test (KS-Test) 31
2.6 CENTRAL LIMIT THEOREM 33
2.7 EXACT TESTS AND GENE SET ENRICHMENT ANALYSIS 33
2.8 PERMUTATION TESTS 36
2.9 SOME POPULAR DISTRIBUTIONS 38
2.9.1 The Uniform Distribution 38
2.9.2 The T-Distribution 39
2.9.3 The Exponential Distribution 39
2.9.4 The Chi-Squared Distribution 39
2.9.5 The Poisson Distribution 39
2.9.6 The Bernoulli Distribution 40
2.9.7 The Binomial Distribution 40
EXERCISES 40
REFERENCES AND FURTHER READING 41
Chapter 3 » Multiple Testing___________________________43
3.1 THE BONFERRON1 CORRECTION AND GENE SET
ENRICHMENT ANALYSIS 43
3.2 MULTIPLE TESTING IN DIFFERENTIAL EXPRESSION
ANALYSIS 46
3.3 FALSE DISCOVERY RATE 48
3.4 eQTLs: A VERY DIFFICULT MULTIPLE-TESTING
PROBLEM 49
EXERCISES 51
REFERENCES AND FURTHER READING 52
Chapter 4 - Parameter Estimation and Multivariate Statistics 53
4.1 FITTING A MODEL TO DATA: OBJECTIVE
FUNCTIONS AND PARAMETER ESTIMATION 53
4.2 MAXIMUM LIKELIHOOD ESTIMATION 54
4.3 LIKELIHOOD FOR GAUSSIAN DATA 55
Contents ■ xi
4.4 HOW TO MAXIMIZE THE LIKELIHOOD ANALYTICALLY 56
4.5 OTHER OBJECTIVE FUNCTIONS 60
4.6 MULTIVARIATE STATISTICS 64
4.7 MLEs FOR MULTIVARIATE DISTRIBUTIONS 69
4.8 HYPOTHESIS TESTING REVISITED: THE PROBLEMS
WITH HIGH DIMENSIONS 77
4.9 EXAMPLE OF LRT FOR THE MULTINOMIAL: GC
CONTENT IN GENOMES 80
EXERCISES 83
REFERENCES AND FURTHER READING 83
Section II Clustering
Chapter 5 ■ Distance-Based Clustering______________87
5.1 MULTIVARIATE DISTANCES FOR CLUSTERING 87
5.2 AGGLOMERATIVE CLUSTERING 91
5.2 CLUSTERING DNA AND PROTEIN SEQUENCES 95
5.4 IS THE CLUSTERING RIGHT? 98
5.5 /C-MEANS CLUSTERING 100
5.6 SO WHAT IS LEARNING ANYWAY? 106
5.7 CHOOSING THE NUMBER OF CLUSTERS FOR
K-MEANS 107
5.8 K-MEDOIDS AND EXEMPLAR-BASED CLUSTERING 109
5.9 GRAPH-BASED CLUSTERING: DISTANCES VERSUS
INTERACTIONS OR CONNECTIONS 110
5.10 CLUSTERING AS DIMENSIONALITY REDUCTION 113
EXERCISES 113
REFERENCES AND FURTHER READING 115
Chapter 6 - Mixture Models and Hidden Variables
___________for Clustering and Beyond______________117
6.1 THE GAUSSIAN MIXTURE MODEL 118
6.2 E-M UPDATES FOR THE MIXTURE OF GAUSSIANS 123
xii a Contents
6.3 DERIVING THE E-M ALGORITHM FOR THE MIXTURE
OF GAUSSIANS 127
6.4 GAUSSIAN MIXTURES IN PRACTICE AND THE
CURSE OF DIMENSIONALITY 131
6.5 CHOOSING THE NUMBER OF CLUSTERS
USING THE AIC 131
6.6 APPLICATIONS OF MIXTURE MODELS IN
BIOINFORMATICS 133
EXERCISES 141
REFERENCES AND FURTHER READING 142
Section Ell Regression
Chapter 7 ■ Univariate Regression_________________145
7.1 SIMPLE LINEAR REGRESSION AS A PROBABILISTIC
MODEL 145
7.2 DERIVING THE MLEs FOR LINEAR REGRESSION 146
7.3 HYPOTHESIS TESTING IN LINEAR REGRESSION 149
7.4 LEAST SQUARES INTERPRETATION OF LINEAR
REGRESSION 154
7.5 APPLICATION OF LINEAR REGRESSION TO eQTLs 155
7.6 FROM HYPOTHESIS TESTING TO STATISTICAL
MODELING: PREDICTING PROTEIN LEVEL BASED
ON mRNA LEVEL 157
7.7 REGRESSION IS NOT JUST LINEAR —POLYNOMIAL
AND LOCAL REGRESSIONS 161
7.8 GENERALIZED LINEAR MODELS 165
EXERCISES 167
REFERENCES AND FURTHER READING 167
Chapter 8 ■ Multiple Regression___________________169
8.1 PREDICTING Y USING MULTIPLE Xs 169
8.2 HYPOTHESIS TESTING IN MULTIPLE DIMENSIONS:
PARTIAL CORRELATIONS 171
Contents
XIII
8.3 EXAMPLE OF A HIGH-DIMENSIONAL MULTIPLE
REGRESSION: REGRESSING GENE EXPRESSION LEVELS
ON TRANSCRIPTION FACTOR BINDING SITES 174
8.4 AIC AND FEATURE SELECTION AND OVERFITTING
IN MULTIPLE REGRESSION 179
EXERCISES 182
REFERENCES AND FURTHER READING 183
Chapter 9 ■ Regularization in Multiple Regression
___________and Beyond_______________________________185
9.1 REGULARIZATION AND PENALIZED LIKELIHOOD 186
9.2 DIFFERENCES BETWEEN THE EFFECTS OF ¿1 AND L2
PENALTIES ON CORRELATED FEATURES 189
9.3 REGULARIZATION BEYOND SPARSITY:
ENCOURAGING YOUR OWN MODEL STRUCTURE 190
9.4 PENALIZED LIKELIHOOD AS MAXIMUM A
POSTERIORI (MAP) ESTIMATION 192
9.5 CHOOSING PRIOR DISTRIBUTIONS FOR
PARAMETERS: HEAVY-TAILS IF YOU CAN 193
EXERCISES 197
REFERENCES AND FURTHER READING 199
Section IV Classification
Chapter 10 ■ Linear Classification__________________203
10.1 CLASSIFICATION BOUNDARIES AND LINEAR
CLASSIFICATION 205
10.2 PROBABILISTIC CLASSIFICATION MODELS 206
10.3 LOGISTIC REGRESSION 208
1 0.4 LINEAR DISCRIMINANT ANALYSIS (LDA) AND THE
LOG LIKELIHOOD RATIO 210
1 0.5 GENERATIVE AND DISCRIMINATIVE MODELS FOR
CLASSIFICATION 214
10.6 NAÏVE BAYES: GENERATIVE MAP CLASSIFICATION 215
xîv b Contents
10.7 TRAINING NAÏVE BAYES CLASSIFIERS 221
10.8 NAÏVE BAYES AND DATA INTEGRATION 222
EXERCISES 223
REFERNCES AND FURTHER READING 223
Chapter 11 ■ Nonlinear Classification________________225
11.1 TWO APPROACHES TO CHOOSE NONLINEAR
BOUNDARIES: DATA-GUIDED AND MULTIPLE
SIMPLE UNITS 226
11.2 DISTANCE-BASED CLASSIFICATION WITH
fc-NEAR EST NEIGHBORS 228
11.3 SVMs FOR NONLINEAR CLASSIFICATION 230
11.4 DECISION TREES 234
11.5 RANDOM FORESTS AND ENSEMBLE
CLASSIFIERS: THE WISDOM OF THE CROWD 236
11.6 MULTICLASS CLASSIFICATION 237
EXERCISES 238
REFERENCES AND FURTHER READING 239
Chapter 12 ■ Evaluating Classifiers__________________241
12.1 CLASSIFICATION PERFORMANCE STATISTICS IN THE
IDEAL CLASSIFICATION SETUP 241
12.2 MEASURES OF CLASSIFICATION PERFORMANCE 242
12.3 ROC CURVES AND PRECISION-RECALL PLOTS 245
12.4 EVALUATING CLASSIFIERS WHEN YOU
DON T HAVE ENOUGH DATA 248
12.5 LEAVE-ONE-OUT CROSS-VALIDATION 251
12.6 BETTER CLASSIFICATION METHODS
VERSUS BETTER FEATURES 253
EXERCISES 254
REFERENCES AND FURTHER READING 255
INDEX, 257
Molecular biologists are performing increasingly large and compli-
cated experiments, but often have little background in data analysis.
The book is devoted to teaching the statistical and computational
techniques molecular biologists need to analyze their data. It explains
the big-picture concepts in data analysis using a wide variety of real-
world molecular biological examples such as eQTLs, ortholog identi-
fication, motif finding, inference of population structure, protein fold
prediction and many more. The book takes a pragmatic approach,
focusing on techniques that are based on elegant mathematics yet
are the simplest to explain to scientists with little background in com-
puters and statistics.
Key Features
• Assumes no background in statistics or computers
• Covers most major types of molecular biological data
• Covers the statistical and machine learning concepts of most
practical utility (P-values, clustering, regression, regularization
and classification)
• Intended for graduate students beginning careers in molecular
biology, systems biology, bioengineering and genetics
About the Author
Alan M Moses is currently Associate Professor and Canada Research
Chair in Computational Biology in the Departments of Cell Systems
Biology and Computer Science at the University of Toronto. His re-
search touches on many of the major areas in computational biology,
including DNA and protein sequence analysis, phylogenetic models,
population genetics, expression profiles, regulatory network simula-
tions and image analysis.
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| spelling | Moses, Alan Verfasser (DE-588)1125639407 aut Statistical modeling and machine learning for molecular biology Alan M. Moses, University of Toronto, Canada Boca Raton ; London ; New York CRC Press, Taylor & Francis Group [2017] © 2017 xvi, 264 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC mathematical and computational biology series Includes bibliographical references and index Datenverarbeitung Molecular biology / Statistical methods Molecular biology / Data processing Molekularbiologie (DE-588)4039983-7 gnd rswk-swf Biostatistik (DE-588)4729990-3 gnd rswk-swf Molekularbiologie (DE-588)4039983-7 s Biostatistik (DE-588)4729990-3 s DE-604 Erscheint auch als Online-Ausgabe 978-1-4822-5861-5 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029687400&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029687400&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Moses, Alan Statistical modeling and machine learning for molecular biology Datenverarbeitung Molecular biology / Statistical methods Molecular biology / Data processing Molekularbiologie (DE-588)4039983-7 gnd Biostatistik (DE-588)4729990-3 gnd |
| subject_GND | (DE-588)4039983-7 (DE-588)4729990-3 |
| title | Statistical modeling and machine learning for molecular biology |
| title_auth | Statistical modeling and machine learning for molecular biology |
| title_exact_search | Statistical modeling and machine learning for molecular biology |
| title_full | Statistical modeling and machine learning for molecular biology Alan M. Moses, University of Toronto, Canada |
| title_fullStr | Statistical modeling and machine learning for molecular biology Alan M. Moses, University of Toronto, Canada |
| title_full_unstemmed | Statistical modeling and machine learning for molecular biology Alan M. Moses, University of Toronto, Canada |
| title_short | Statistical modeling and machine learning for molecular biology |
| title_sort | statistical modeling and machine learning for molecular biology |
| topic | Datenverarbeitung Molecular biology / Statistical methods Molecular biology / Data processing Molekularbiologie (DE-588)4039983-7 gnd Biostatistik (DE-588)4729990-3 gnd |
| topic_facet | Datenverarbeitung Molecular biology / Statistical methods Molecular biology / Data processing Molekularbiologie Biostatistik |
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