en-es
en-fr
en-sl
en
0.25
0.5
0.75
1.25
1.5
1.75
2
Cancer Diagnostics and Prognostics from Comparative Spectral Decompositions of Patient-Matched Genomic Profiles
Published on Sep 19, 20161658 Views
Related categories
Chapter list
Cancer Diagnostics and Prognostics from Comparative Spectral Decompositions of Patient-Matched Genomic Profiles00:00
High-Throughput Biotechnologies Record Global Signals01:05
Quantum Measure of a Single System04:44
Global Mathematical Vocabulary for Molecular Biological Discovery05:10
Physics-Inspired Matrix (and Tensor) Models06:06
Effects of DNA Replication on RNA Expression10:56
HOSVD for Integrative Analysis of a High-Dimensional Dataset - 113:46
HOSVD for Integrative Analysis of a High-Dimensional Dataset - 215:08
HOSVD Detection and Removal of Artifacts15:56
Patterns Underlie Principles of Nature16:05
Computational Discovery and Validation of a Genomic Predictor of GBM Survival17:16
GSVD for Comparative Analysis of Two Different Two-Dimensional Datasets22:53
Copy-Number Variations (CNVs) Common to the GBM Tumor and Normal Brain23:33
Experimental Variations Exclusive to the Tumor or Normal Profiles25:56
Global Pattern of Tumor-Exclusive CopyNumber Alterations Predicts Drug Targets27:02
Global, Genomic Predictor of GBM Survival28:15
Platform-Independent Genomic Predictor of Astrocytoma Outcome30:17
Statistically Better Than, and Independent of Age, Grade, and Laboratory Tests30:43
Tensor GSVD for Comparative Analysis of Two Different High-Dimensional Datasets - 134:33
Computational Discovery and Validation of Genomic Predictors of OV Outcome35:18
Tensor GSVD for Comparative Analysis of Two Different High-Dimensional Datasets - 235:48
Chromosome Arm-Wide Patterns of TumorExclusive Platform-Consistent Alterations Encoding for Cell Transformation35:53
Predictors of OV Survival37:16
Predictors of OV Survival and Response to Platinum-Based Chemotherapy37:41
HO GSVD for Comparative Analysis of Multiple Two-Dimensional Datasets - 139:22
HO GSVD for Comparative Analysis of Multiple Two-Dimensional Datasets - 239:33
Approximately Common HO GSVD Subspace39:34
Common Cell Cycle Subspace39:35
Simultaneous Classification Independent of Sequence Similarity39:35
Patterns Underlie Principles of Nature: Statistics to Processes39:36
SVD Identifies Transcript Length Distribution Functions from DNA Microarray Data39:36
The interplay between mathematical modeling and experimental measurement39:55
Mathematical modeling of large-scale molecular biological data39:57
Collaborators40:43
Multi-Tensor Decompositions for Personalized Cancer Diagnostics and Prognostics40:57