Atjaunināt sīkdatņu piekrišanu

E-grāmata: Biodata Mining And Visualization: Novel Approaches

4.00/5 (2 ratings by Goodreads)
(Intellectual Property Office Of New Zealand, New Zealand)
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 34,94 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
Biodata Mining And Visualization: Novel ApproachesPalielināt
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

There is a lack of an exposition on interdisciplinary and innovative methods of data mining and visualization for biodata. This book fills the gap by introducing an interdisciplinary set of the most recent methods and references on novel techniques from artificial intelligence, data mining, engineering, pattern recognition, and ontological data mining fields that are applicable to bioinformatics. The latest novel approaches are explained in detail, their advantages and disadvantages are summarized, and pointers to the future development of new applications are given. By widening the pool from which biologists and bioinformaticians can adopt methods for biodata mining and visualization, computational data mining experts in nonbiological fields are also encouraged to utilize their expertise in order to contribute to the progress of computational biology, thus enhancing the collaboration between these two disciplines.
Preface v
Acknowledgement vii
About the Author viii
1 Introduction to Modern Molecular Biology
1(20)
1.1 Cells store large amounts of information in DNA
1(6)
1.2 Cells process complex information
7(5)
1.3 Cellular life is chemically complex and somewhat stochastic
12(7)
1.4 Challenges in analyzing complex biodata
19(1)
References
19(2)
2 Biodata Explosion
21(39)
2.1 Primary sequence and structure data
22(9)
2.1.1 DNA sequence databases
22(5)
2.1.2 Protein sequence databases
27(1)
2.1.3 Molecular structure databases
28(3)
2.2 Secondary annotation data
31(7)
2.2.1 Motif annotations
32(3)
2.2.2 Gene function annotations
35(1)
2.2.3 Genomic annotations
36(1)
2.2.4 Inter-species phylogeny and gene family annotations
36(2)
2.3 Experimental and personalized data
38(10)
2.3.1 DNA expression profiles
38(2)
2.3.2 Proteomics data and degradomics
40(1)
2.3.3 Protein expression profiles, 2D gel and protein interaction data
41(1)
2.3.4 Metabolomics and metabolic pathway databases
42(2)
2.3.5 Personalized data
44(4)
2.4 Semantic and processed text data
48(4)
2.4.1 Ontologies
49(2)
2.4.2 Text-mined annotation data
51(1)
2.5 Integrated and federated databases
52(3)
References
55(5)
3 Local Pattern Discovery and Comparing Genes and Proteins
60(37)
3.1 DNA/RNA motif discovery
64(14)
3.1.1 Single motif models: MEME, AlignAce etc.
64(6)
3.1.2 Multiple motif models: LOGOS and MotifRegressor
70(3)
3.1.3 Informative k-mers approach
73(5)
3.2 Protein motif discovery
78(6)
3.2.1 InterProScan and other traditional methods
79(3)
3.2.2 Protein k-mer and other string based methods
82(2)
3.3 Genetic algorithms, particle swarms and ant colonies
84(4)
3.3.1 Genetic algorithms
84(2)
3.3.2 Particle swarm optimization
86(1)
3.3.3 Ant colony optimization
87(1)
3.4 Sequence visualization
88(2)
References
90(7)
4 Global Pattern Discovery and Comparing Genomes
97(48)
4.1 Alignment-based methods
98(10)
4.1.1 Pairwise genome-wide search algorithms: LAGAN, AVID etc.
98(1)
4.1.2 Multiple alignment methods: MLAGAN, MAVID, MULTIZ etc.
98(5)
4.1.3 Dotplots
103(1)
4.1.4 Visualization of genome comparisons
104(1)
4.1.5 Global motif maps
105(3)
4.2 Alignmentless methods
108(17)
4.2.1 K-mer based methods
109(5)
4.2.2 Average common substring and compressibility based methods
114(3)
4.2.3 2D portraits of genomes
117(8)
4.3 Genome scale non-sequence data analysis
125(12)
4.3.1 DNA physical structure based methods
125(6)
4.3.2 Secondary structure based comparisons
131(6)
References
137(8)
5 Molecule Structure Based Searching and Comparison
145(31)
5.1 Molecule structures as graphs or strings
148(9)
5.1.1 3D to 1D transformations
148(3)
5.1.2 Graph matching methods
151(4)
5.1.3 Graph visualization
155(1)
5.1.4 Graph grammars
156(1)
5.2 RNA structure comparison and prediction
157(5)
5.3 Image comparison based methods
162(7)
5.3.1 Gabor filter based methods
165(1)
5.3.2 Image symmetry set based methods
166(2)
5.3.3 Other graph topology based methods
168(1)
References
169(7)
6 Function Annotation and Ontology Based Searching and Classification
176(36)
6.1 Annotation ontologies
176(3)
6.2 Gene Ontology based mining
179(3)
6.3 Sequence similarity based function prediction
182(2)
6.4 Cellular location prediction
184(2)
6.5 New integrative methods: Utilizing networks
186(6)
6.6 Text mining bioliterature for automated annotation
192(13)
6.6.1 Natural language processing (NLP)
193(4)
6.6.2 Semantic profiling
197(2)
6.6.3 Matrix factorization methods
199(6)
References
205(7)
7 New Methods for Genomics Data: SVM and Others
212(33)
7.1 SVM kernels
212(7)
7.2 SVM trees
219(2)
7.3 Methods for microarray data
221(6)
7.3.1 Gene selection algorithms
223(2)
7.3.2 Gene selection by consistency methods
225(2)
7.4 Genome as a time series and discrete wavelet transform
227(4)
7.5 Parameterless clustering for gene expression
231(1)
7.6 Transductive confidence machines, conformal predictors and ROC isometrics
232(4)
7.7 Text compression methods for biodata analysis
236(2)
References
238(7)
8 Integration of Multimodal Data: Toward Systems Biology
245(21)
8.1 Comparative genome annotation systems
246(3)
8.2 Phylogenetics methods
249(4)
8.3 Network inference from interaction and coexpression data
253(5)
8.4 Bayesian inference, association rule mining and Petri nets
258(4)
References
262(4)
9 Future Challenges
266(31)
9.1 Network analysis methods
266(3)
9.2 Unsupervised and supervised clustering
269(1)
9.3 Neural networks and evolutionary methods
270(3)
9.4 Semantic web and ontologization of biology
273(4)
9.5 Biological data fusion
277(2)
9.6 Rise of the GPU machines
279(11)
References
290(7)
Index 297
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97898131070832e.html