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Joint Mining of Biological Text and Images: Case Studies
Published on Feb 25, 20076579 Views
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Chapter list
Joint Mining of Biological Text and Images: Case Studies00:00
Systems Biology and Location Proteomics00:31
Automated Interpretation02:42
Supervised Learning of High-Resolution Subcellular Location Patterns03:06
The Challenge03:20
Feature-based, Supervised learning approach04:13
Boland & Murphy 200104:38
Machine Learning Methods05:11
Evaluating Classifiers05:25
2D Classification Results 05:45
Human Classification Results 06:19
Computer vs. Human06:54
3D HeLa cell images07:15
Unsupervised Learning to Identify High-Resolution Protein Patterns08:58
Location Proteomics09:31
What Now?10:19
Chen et al 2003; Chen and Murphy 200510:53
slide1812:14
Nucleolar Proteins12:16
Punctate Nuclear Proteins12:22
Predominantly Nuclear Proteins with Some Punctate Cytoplasmic Staining12:28
Nuclear and Cytoplasmic Proteins with Some Punctate Staining12:35
Image Content-based Retrieval and Interpretation of Micrographs from On-line Journal Articles 13:06
Objectives of SLIF17:03
Example page from biomedical literature18:04
SLIF components19:14
Overview: Image processing in SLIF20:32
Overview: Image processing in SLIF0122:32
Overview: Text Processing in SLIF Find entity names in text, and panel labels in text and the image. Match panels labels in text to panel labels on the image. Associate entity names to textual pane24:13
Some major challenges25:25
Graphical model: Panel typing39:28
SLIF programmatic interface48:10
Acknowledgments48:25
Brian Athey (UMich), CMU: Bob Murphy48:43