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Kernel methods for genomic data fusion
Published on Oct 23, 20124087 Views
Despite significant advances in omics techniques, the identification of genes causing rare genetic diseases and the understanding of the molecular networks underlying those disorders remains di
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Chapter list
Kernel methods for genomic data fusion00:00
Disease gene discovery in rare congenital disorders00:38
Genetic diagnosis06:57
Deletion del(22)(q12.2)11:08
Candidate gene prioritization13:04
Data fusion16:43
Challenge of heterogeneous data17:31
Prioritization by example18:26
Region 12q24: 327 candidates19:52
Data fusion with order statistics21:05
Training of an attribute submodel22:30
Scoring of an attribute submodel23:19
http://www.esat.kuleuven.ac.be/endeavour23:57
Large-scale statistical validation24:20
A novel locus for congenital heart defect on chromosome 6q24-2526:18
Kernel methods for genomic data fusion 1630:06
Kernel-based genomic data fusion30:38
Kernel data fusion (a.k.a. MKL)32:25
Prioritization by novelty detection33:37
One-class support vector machine34:21
Kernel fusion for novelty detection34:54
Kernel fusion in one-class SVM35:51
L2 vs. L∞ kernel fusion36:04
A framework for kernel data fusion37:54
Kernel data fusion38:08
ETkL: Extract, Transform, Kernelize, Learn40:11
The No-Voodoo principle41:24
Handling large kernel matrices43:51
Incomplete Cholesky decomposition44:40
What if no or few genes known for a disease?45:28
Expression of candidate genes46:06
Systems biology: network analysis47:27
Integrative protein network48:16
Concept - 148:25
Concept - 248:43
Methods48:45
Methods & benchmark49:51
Results50:29
DDX4 expression neighborhood51:10
PINTA web tool51:32
Krylov subspace methods51:46
Networks vs. kernels52:50
Biomagnet54:03