![Transcriptional Gene Regulation Networks Gene Regulation Network (GRN) model T v E xt r ac el lu la r co mm un i ca ti on Drosophila eve stripe expression in model (right) and data (left). Green: eve expression, red: kni expression. From [Reinitz and Sharp, Mech. of Devel., 49:133-158, 1995 ]. [Mjolsness et al. J. Theor. Biol. 152: 429-453, 1991]](https://image.slidesharecdn.com/scientific-applications-of-machine-learning3096/85/Scientific-applications-of-machine-learning-10-320.jpg)
Scientific applications of machine learning
- 1. Scientific Applications of Machine Learning Eric Mjolsness Scientific Inference Systems Laboratory Donald Bren School of Information and Computer Sciences, and Institute for Genomics and Bioinformatics University of California, Irvine
- 2. Scientific Imagery Applications NGC 7331 - http://photojournal.jpl.nasa.gov/catalog/PIA06322 Arabidopsis SAM - Meyerowitz Lab
- 3. Some Basic Machine Learning Distinctions Supervised vs. unsupervised learning Supervised e.g. classification and regression Feature selection regression for phenomenological model fitting e.g. GRN’s Unsupervised e.g. clustering; may be preprocessor Generative vs. Kernal methods Generative (statistical inference) models Kernal methods e.g Support Vector Machines Vector vs. Relationship data Vector data: preprocessed image features log I, x, … Images, time series, shifted spectra - semigroup actions Sparse graph/relationship data - permutation actions
- 4. Correspondence Problems Extended sources - map morphologies Similar to biological imaging problems Fewer sources but many pixels Moving or changing point sources E.g. Ida and Dactyl / JPL MLS Dense point sources with instrument noise e.g. globular clusters (radial density function) Techniques: soft permutations, geometric transformations via optimization & continuation Embedding inside a graph clustering (optimization) algorithm Multiscale acceleration of optimization
- 5. Mixture Models Mixture of Gaussians, t-distributions, … Can do outlier detection Mixture of factor analyzers Mixture of time series models * Problem-specific generative models Can formulate with a Stochastic Parameterized Grammar Clustering graphs Frey et al. 1998 Utsugi and Kumagai 2000
- 6. Stochastic Grammars for Data Modeling
- 7. Text & Biology Models
- 8. More Detailed Clustering Grammars Clusters generate data Priors on cluster centers & variances Iterative through levels in a hierarchy Recursive through hierarchy
- 9. Rock Field Grammar grammar rockfield() {
- 10. Transcriptional Gene Regulation Networks Gene Regulation Network (GRN) model T v E xt r ac el lu la r co mm un i ca ti on Drosophila eve stripe expression in model (right) and data (left). Green: eve expression, red: kni expression. From [Reinitz and Sharp, Mech. of Devel., 49:133-158, 1995 ]. [Mjolsness et al. J. Theor. Biol. 152: 429-453, 1991]
- 11. Gene Regulation + Signal Transduction Network T transcriptional regulation targets receptors ligands cell nucleus
- 12. Software architectures for systems biology: Sigmoid & Cellerator
- 13. 3-tier architecture Database Access Model Translation Sigmoid Pathway Representation/Storage Database Cellerator Simulation/Inference Engine ME NU Interactive Graphic Model (SVG/Applet) Graphic Output OJB API JLink API P R O P E R T Y SOAP: Web Service XML(Object), Image, v ia HTTP
- 14. Possible software support Machine learning (open source/academic) CompClust (CIT/JPL): Scripting/GUI dichotomy data point; dataset views WEKA data mining Intel: PNL Probabilistic Networks Library Future: stochastic grammar modeler + autogeneration (as in Cellerator) Image processing, data environments Matlab, IDL, Mathematica, Khoros/VisiQuest, … NIHImage/ImageJ, …
- 15. Metadata in Systems Biology SBML Sigmoid UML
- 16. Fletcher et al., Science v. 283, 1999 Brand et. al., Science 289 , 617-619, (2000) WUS
- 17. SAM gene network: Results protein concentrations Y wus (init) and L1 X
- 18. SAM: Gene Network Model Z wus clv1 clv3 X diffusive Y L1 diffusive
- 19. SAM growth imagery PIN1 cell walls
- 20. Venu Gonehal
- 21. Basic Machine Learning Distinctions Supervised vs. unsupervised learning Supervised e.g. classification and regression Feature selection regression for phenomenological model fitting e.g. GRN’s Unsupervised e.g. clustering; may be preprocessor Generative vs. Kernal methods Generative (statistical inference) models Kernal methods e.g Support Vector Machines Vector vs. Relationship data Vector data: preprocessed image features log I, x, … Images, time series, shifted spectra - semigroup actions Sparse graph/relationship data - permutation actions
- 22. Contacts Wayne Hayes, UCI ICS faculty scientific computing UCI ICS Maching Learning Padhraic Smyth Pierre Baldi Chris Hart, Caltech Biology grad student