Computational text analysis for functional genomics and bioinformatics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford [u.a.]
Oxford Univ. Press
2006
|
| Ausgabe: | 1. publ., reprint. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIV, 288 S. Ill., graph. Darst. |
| ISBN: | 0198567413 0198567405 9780198567417 9780198567400 |
Internformat
MARC
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| 020 | |a 9780198567417 |9 978-0-19-856741-7 | ||
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| 100 | 1 | |a Raychaudhuri, Soumya |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Computational text analysis for functional genomics and bioinformatics |c Soumya Raychaudhuri |
| 250 | |a 1. publ., reprint. | ||
| 264 | 1 | |a Oxford [u.a.] |b Oxford Univ. Press |c 2006 | |
| 300 | |a XXIV, 288 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Bio-informatique | |
| 650 | 7 | |a Bioinformatique |2 ram | |
| 650 | 4 | |a Génomique - Informatique | |
| 650 | 2 | |a Génomique | |
| 650 | 7 | |a Génétique - Informatique |2 ram | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Bioinformatics | |
| 650 | 4 | |a Computational biology | |
| 650 | 4 | |a Genomics |x Data processing | |
| 650 | 0 | 7 | |a Genanalyse |0 (DE-588)4200230-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Molekulare Bioinformatik |0 (DE-588)4531334-9 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Genanalyse |0 (DE-588)4200230-8 |D s |
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| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016084866&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016084866 | ||
Datensatz im Suchindex
| _version_ | 1804137143090544640 |
|---|---|
| adam_text | Contents
Ust
of Figures
xvii
List of Plates
xxi
List of Tables
xxiii
1
An introduction to text analysis in genomics
1
1.1
The genomics literature
2
1.2
Using text in genomics
5
1.2.1
Building databases of genetic knowledge
5
1.2.2
Analyzing experimental genomic data sets
7
1.2.3
Proposing new biological knowledge: identifying
candidate genes
8
1.3
Publicly available text resources
9
1.3.1
Electronic text
9
1.3.2
Genome resources
9
1.3.3
Gene ontology
11
1.4
The advantage of text-based methods
12
1.5
Guide to this book
13
2
Functional genomics
17
2.1
Some molecular biology
17
2.1.1
Central dogma of molecular biology
18
2.1.2
Deoxyribonucleic acid
18
2.1.3
Ribonucleic acid
20
2.1.4
Genes
22
2.1.5
Proteins
24
2.1.6
Biological function
26
2.2
Probability theory and statistics
27
2.2.1
Probability
27
2.2.2
Conditional probability
28
2.2.3
independence
29
2.2.4
Bayes
theorem
30
xii Contents
2.2.5
Probability distribution functions
31
2.2.6
Information theory
33
2.2.7
Population statistics
34
2.2.8
Measuring performance
35
2.3
Deriving
and analyzing sequences
37
2.3.1
Sequencing
39
2.3.2
Homology
40
2.3.3
Sequence alignment
42
2.3.4
Pairwise sequence alignment and dynamic
programming
44
2.3.5
Linear time pairwise alignment: BLAST
47
2.3.6
Multiple sequence alignment
48
2.3.7
Comparing sequences to profiles: weight matrices
50
2.3.8
Position specific iterative BLAST
53
2.3.9
Hidden Markov models
54
2.4
Gene
expression profiling
61
2.4.1
Measuring gene expression with arrays
63
2.4.2
Measuring gene expression by sequencing and
counting transcripts
64
2.4.3
Expression array analysis
65
2.4.4
Unsupervised grouping: clustering
66
2.4.5
/c-means clustering
68
2.4.6
Self-organizing maps
69
2.4.7
Hierarchical clustering
70
2.4.8
Dimension reduction with principal components
analysis
72
2.4.9
Combining expression data with external
information: supervised machine learning
74
2.4.10
Nearest neighbor classification
75
2.4.11
Linear discriminant analysis
75
3
Textual
profiles
of
genes
83
3.1
Representing documents as word vectors
84
3.2
Metrics to compare documents
86
3.3
Some
words are more important for document similarity
88
3.4
Building a vocabulary: feature selection
88
3.5
Weighting words
90
3.6
Latent
:
semantic indexing
92
3.7
Defining textual profiles for genes
94
3.8
Using
text like genomics data
96
3.9
Asimi
pie strategy to assigning keywords to erouos of genes
; 100
Contents
хні
3.10
Querying genes for biological function
101
4
Using text in sequence analysis
107
4.1
SWISS-PROT records as a textual resource
109
4.2
Using sequence similarity to extend literature references
111
4.3
Assigning keywords to summarize sequences hits
112
4.4
Using textual profiles to organize sequence hits
114
4.5
Using text to help identify remote homology
114
4.6
Modifying iterative sequence similarity searches to include text
115
4.7
Evaluating PSI-BLAST modified to include text
117
4.8
Combining sequence and text together
120
5
Text-based analysis of a single series of gene expression measurements
123
5.1
Pitfalls of gene expression analysis: noise
124
5.2
Phosphate metabolism: an example
126
5.3
The top fifteen genes
127
5.4
Distinguishing true positives from false positives with
a literature-based approach
129
5.5
Neighbor expression information
130
5.6
Application to phosphate metabolism data set
132
5.7
Recognizing high induction false positives with
literature-based scores
136
5.8
Recognizing low induction false positives
138
5.9
Assessing experiment quality with literature-based scoring
140
5.10
Improvements
140
5.11
Application to other assays
141
5.12
Assigning keywords that describe the broad biology
of the experiment
141
6
Analyzing groups of genes
147
6.1
Functional coherence of a group of genes
148
6.2
Overview of computational approach
152
6.3
Strategy to evaluate different algorithms
155
6.4
Word distribution divergence
157
6.5
Best article score
160
6.6
Neighbor divergence
163
6.6.1
Calculating a theoretical distribution of scores
163
Contents
6.6.2
Quantifying the difference between the empirical
score distribution and the theoretical one
164
6.7
Neighbor divergence per gene
164
6.8
Corruption studies
166
6.9
Application of functional coherence scoring to screen
gene expression clusters
167
6.10
Understanding the gene group s function
170
7
Analyzing large gene expression data sets
171
7.1
Groups of genes
172
7.2
Assigning keywords
173
7.3
Screening gene expression clusters
173
7.4
Optimizing cluster boundaries: hierarchical clustering
178
7.5
Application to other organisms besides yeast
184
7.6
Identifying and optimizing clusters in
a Drosophila
development data set
189
8
Using text classification for gene function annotation
195
8.1
Functional vocabularies and gene annotation
196
8.1.1
Gene Ontology
197
8.1.2
Enzyme Commission
200
8.1.3
Kyoto Encyclopedia of Genes and Genomes
200
8.2
Text classification
202
8.3
Nearest neighbor classification
. 203
8.4
Naive
Bayes
classification
204
8.5
Maximum entropy classification
205
8.6
Feature selection: choosing the best words for classification
210
8.7
Classifying documents into functional categories
212
8.8
Comparing classifiers
213
8.9
Annotating genes
221
9
Finding gene names
227
9.1
Strategies to identify gene names
228
9.2
Recognizing gene names with a dictionary
228
9.3
Using word structure and appearance to identify gene names
232
9.4
Using syntax to eliminate gene name candidates
233
9.5
Using context as a clue about gene names
235
9.6
Morphology
237
Contents
χ«
9.7
Identifying gene names and their abbreviations
237
9.8.
A single unified gene name finding algorithm
240
10
Protein interaction networks
245
10.1
Genetic networks
246
10.2
Experimental assays to identify protein networks
247
10.2.1
Yeast two hybrid
247
10.2.2
Affinity precipitation
248
10.3
Predicting interactions versus verifying interactions
with scientific text
249
10.4
Networks of co-occurring genes
249
10.5
Protein interactions and gene name co-occurrence in text
250
10.6
Number of textual co-occurrences predicts likelihood
of an experimentally predicted interaction
254
10.7
Information extraction and genetic networks:
increasing specificity and identifying interaction type
259
10.8
Statistical machine learning
262
11
Conclusion
271
index
273
|
| adam_txt |
Contents
Ust
of Figures
xvii
List of Plates
xxi
List of Tables
xxiii
1
An introduction to text analysis in genomics
1
1.1
The genomics literature
2
1.2
Using text in genomics
5
1.2.1
Building databases of genetic knowledge
5
1.2.2
Analyzing experimental genomic data sets
7
1.2.3
Proposing new biological knowledge: identifying
candidate genes
8
1.3
Publicly available text resources
9
1.3.1
Electronic text
9
1.3.2
Genome resources
9
1.3.3
Gene ontology
11
1.4
The advantage of text-based methods
12
1.5
Guide to this book
13
2
Functional genomics
17
2.1
Some molecular biology
17
2.1.1
Central dogma of molecular biology
18
2.1.2
Deoxyribonucleic acid
18
2.1.3
Ribonucleic acid
20
2.1.4
Genes
22
2.1.5
Proteins
24
2.1.6
Biological function
26
2.2
Probability theory and statistics
27
2.2.1
Probability
27
2.2.2
Conditional probability
28
2.2.3
independence
29
2.2.4
Bayes'
theorem
30
xii Contents
2.2.5
Probability distribution functions
31
2.2.6
Information theory
33
2.2.7
Population statistics
34
2.2.8
Measuring performance
35
2.3
Deriving
and analyzing sequences
37
2.3.1
Sequencing
39
2.3.2
Homology
40
2.3.3
Sequence alignment
42
2.3.4
Pairwise sequence alignment and dynamic
programming
44
2.3.5
Linear time pairwise alignment: BLAST
47
2.3.6
Multiple sequence alignment
48
2.3.7
Comparing sequences to profiles: weight matrices
50
2.3.8
Position specific iterative BLAST
53
2.3.9
Hidden Markov models
54
2.4
Gene
expression profiling
61
2.4.1
Measuring gene expression with arrays
63
2.4.2
Measuring gene expression by sequencing and
counting transcripts
64
2.4.3
Expression array analysis
65
2.4.4
Unsupervised grouping: clustering
66
2.4.5
/c-means clustering
68
2.4.6
Self-organizing maps
69
2.4.7
Hierarchical clustering
70
2.4.8
Dimension reduction with principal components
analysis
72
2.4.9
Combining expression data with external
information: supervised machine learning
74
2.4.10
Nearest neighbor classification
75
2.4.11
Linear discriminant analysis
75
3
Textual
profiles
of
genes
83
3.1
Representing documents as word vectors
84
3.2
Metrics to compare documents
86
3.3
Some
words are more important for document similarity
88
3.4
Building a vocabulary: feature selection
88
3.5
Weighting words
90
3.6
Latent
:
semantic indexing
92
3.7
Defining textual profiles for genes
94
3.8
Using
text like genomics data
96
3.9
Asimi
pie strategy to assigning keywords to erouos of genes
; 100
Contents
хні
3.10
Querying genes for biological function
101
4
Using text in sequence analysis
107
4.1
SWISS-PROT records as a textual resource
109
4.2
Using sequence similarity to extend literature references
111
4.3
Assigning keywords to summarize sequences hits
112
4.4
Using textual profiles to organize sequence hits
114
4.5
Using text to help identify remote homology
114
4.6
Modifying iterative sequence similarity searches to include text
115
4.7
Evaluating PSI-BLAST modified to include text
117
4.8
Combining sequence and text together
120
5
Text-based analysis of a single series of gene expression measurements
123
5.1
Pitfalls of gene expression analysis: noise
124
5.2
Phosphate metabolism: an example
126
5.3
The top fifteen genes
127
5.4
Distinguishing true positives from false positives with
a literature-based approach
129
5.5
Neighbor expression information
130
5.6
Application to phosphate metabolism data set
132
5.7
Recognizing high induction false positives with
literature-based scores
136
5.8
Recognizing low induction false positives
138
5.9
Assessing experiment quality with literature-based scoring
140
5.10
Improvements
140
5.11
Application to other assays
141
5.12
Assigning keywords that describe the broad biology
of the experiment
141
6
Analyzing groups of genes
147
6.1
Functional coherence of a group of genes
148
6.2
Overview of computational approach
152
6.3
Strategy to evaluate different algorithms
155
6.4
Word distribution divergence
157
6.5
Best article score
160
6.6
Neighbor divergence
163
6.6.1
Calculating a theoretical distribution of scores
163
Contents
6.6.2
Quantifying the difference between the empirical
score distribution and the theoretical one
164
6.7
Neighbor divergence per gene
164
6.8
Corruption studies
166
6.9
Application of functional coherence scoring to screen
gene expression clusters
167
6.10
Understanding the gene group's function
170
7
Analyzing large gene expression data sets
171
7.1
Groups of genes
172
7.2
Assigning keywords
173
7.3
Screening gene expression clusters
173
7.4
Optimizing cluster boundaries: hierarchical clustering
178
7.5
Application to other organisms besides yeast
184
7.6
Identifying and optimizing clusters in
a Drosophila
development data set
189
8
Using text classification for gene function annotation
195
8.1
Functional vocabularies and gene annotation
196
8.1.1
Gene Ontology
197
8.1.2
Enzyme Commission
200
8.1.3
Kyoto Encyclopedia of Genes and Genomes
200
8.2
Text classification
202
8.3
Nearest neighbor classification
. 203
8.4
Naive
Bayes
classification
204
8.5
Maximum entropy classification
205
8.6
Feature selection: choosing the best words for classification
210
8.7
Classifying documents into functional categories
212
8.8
Comparing classifiers
213
8.9
Annotating genes
221
9
Finding gene names
227
9.1
Strategies to identify gene names
228
9.2
Recognizing gene names with a dictionary
228
9.3
Using word structure and appearance to identify gene names
232
9.4
Using syntax to eliminate gene name candidates
233
9.5
Using context as a clue about gene names
235
9.6
Morphology
237
Contents
χ«
9.7
Identifying gene names and their abbreviations
237
9.8.
A single unified gene name finding algorithm
240
10
Protein interaction networks
245
10.1
Genetic networks
246
10.2
Experimental assays to identify protein networks
247
10.2.1
Yeast two hybrid
247
10.2.2
Affinity precipitation
248
10.3
Predicting interactions versus verifying interactions
with scientific text
249
10.4
Networks of co-occurring genes
249
10.5
Protein interactions and gene name co-occurrence in text
250
10.6
Number of textual co-occurrences predicts likelihood
of an experimentally predicted interaction
254
10.7
Information extraction and genetic networks:
increasing specificity and identifying interaction type
259
10.8
Statistical machine learning
262
11
Conclusion
271
index
273 |
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| author | Raychaudhuri, Soumya |
| author_facet | Raychaudhuri, Soumya |
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| edition | 1. publ., reprint. |
| format | Book |
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| id | DE-604.BV022879871 |
| illustrated | Illustrated |
| index_date | 2024-07-02T18:50:09Z |
| indexdate | 2024-07-09T21:07:36Z |
| institution | BVB |
| isbn | 0198567413 0198567405 9780198567417 9780198567400 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016084866 |
| oclc_num | 244812688 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR |
| owner_facet | DE-355 DE-BY-UBR |
| physical | XXIV, 288 S. Ill., graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Oxford Univ. Press |
| record_format | marc |
| spelling | Raychaudhuri, Soumya Verfasser aut Computational text analysis for functional genomics and bioinformatics Soumya Raychaudhuri 1. publ., reprint. Oxford [u.a.] Oxford Univ. Press 2006 XXIV, 288 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Bio-informatique Bioinformatique ram Génomique - Informatique Génomique Génétique - Informatique ram Datenverarbeitung Bioinformatics Computational biology Genomics Data processing Genanalyse (DE-588)4200230-8 gnd rswk-swf Molekulare Bioinformatik (DE-588)4531334-9 gnd rswk-swf Genanalyse (DE-588)4200230-8 s Molekulare Bioinformatik (DE-588)4531334-9 s DE-604 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016084866&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Raychaudhuri, Soumya Computational text analysis for functional genomics and bioinformatics Bio-informatique Bioinformatique ram Génomique - Informatique Génomique Génétique - Informatique ram Datenverarbeitung Bioinformatics Computational biology Genomics Data processing Genanalyse (DE-588)4200230-8 gnd Molekulare Bioinformatik (DE-588)4531334-9 gnd |
| subject_GND | (DE-588)4200230-8 (DE-588)4531334-9 |
| title | Computational text analysis for functional genomics and bioinformatics |
| title_auth | Computational text analysis for functional genomics and bioinformatics |
| title_exact_search | Computational text analysis for functional genomics and bioinformatics |
| title_exact_search_txtP | Computational text analysis for functional genomics and bioinformatics |
| title_full | Computational text analysis for functional genomics and bioinformatics Soumya Raychaudhuri |
| title_fullStr | Computational text analysis for functional genomics and bioinformatics Soumya Raychaudhuri |
| title_full_unstemmed | Computational text analysis for functional genomics and bioinformatics Soumya Raychaudhuri |
| title_short | Computational text analysis for functional genomics and bioinformatics |
| title_sort | computational text analysis for functional genomics and bioinformatics |
| topic | Bio-informatique Bioinformatique ram Génomique - Informatique Génomique Génétique - Informatique ram Datenverarbeitung Bioinformatics Computational biology Genomics Data processing Genanalyse (DE-588)4200230-8 gnd Molekulare Bioinformatik (DE-588)4531334-9 gnd |
| topic_facet | Bio-informatique Bioinformatique Génomique - Informatique Génomique Génétique - Informatique Datenverarbeitung Bioinformatics Computational biology Genomics Data processing Genanalyse Molekulare Bioinformatik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016084866&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT raychaudhurisoumya computationaltextanalysisforfunctionalgenomicsandbioinformatics |